# Expanding the Design Space of Computer Vision-based Interactive Systems for
Group Dance Practice

Soohwan Lee  Department of Design, UNISTUlsanRepublic of Korea
<EMAIL_ADDRESS>,  Seoyeong Hwang  Department of Design,
UNISTUlsanRepublic of Korea<EMAIL_ADDRESS>,  Ian Oakley  School of
Electrical Engineering, KAISTDaejeonRepublic of Korea<EMAIL_ADDRESS>and  Kyungho Lee  Department of Design, UNISTUlsanRepublic of Korea
<EMAIL_ADDRESS>
(2024)

###### Abstract.

Group dance, a sub-genre characterized by intricate motions made by a cohort
of performers in tight synchronization, has a longstanding and culturally
significant history and, in modern forms such as cheerleading, a broad base of
current adherents. However, despite its popularity, learning group dance
routines remains challenging. Based on the prior success of interactive
systems to support individual dance learning, this paper argues that group
dance settings are fertile ground for augmentation by interactive aids. To
better understand these design opportunities, this paper presents a sequence
of user-centered studies of and with amateur cheerleading troupes, spanning
from the formative (interviews, observations) through the generative (an
ideation workshop) to concept validation (technology probes and speed dating).
The outcomes are a nuanced understanding of the lived practice of group dance
learning, a set of interactive concepts to support those practices, and design
directions derived from validating the proposed concepts. Through this
empirical work, we expand the design space of interactive dance practice
systems from the established context of single-user practice (primarily
focused on gesture recognition) to a multi-user, group-based scenario focused
on feedback and communication.

Offline group dance practice; Cheerleading; Group-level performance; Pose
estimation; Group communication; Learning

††copyright: acmcopyright††journalyear: 2024††copyright:
rightsretained††conference: Designing Interactive Systems Conference; July
1–5, 2024; IT University of Copenhagen, Denmark††booktitle: Designing
Interactive Systems Conference (DIS ’24), July 1–5, 2024, IT University of
Copenhagen, Denmark††doi: 10.1145/3643834.3661568††isbn:
979-8-4007-0583-0/24/07††ccs: Human-centered computing Scenario-based
design††ccs: Human-centered computing Participatory design††ccs: Human-
centered computing Empirical studies in interaction design

Figure 1. We explored and proposed new dimensions for the design of a group-
based interactive dance practice system. The group contexts require
consideration of different feedback strategies such as timing and privacy.

This diagram contrasts two design spaces for dance technology. The left side,
”Existing Design Space,” focuses on individual user interaction with
components like Movement Recognition, Performance Evaluation, and Feedback.
The right side, ”Expanded Design Space,” extends to a group system,
incorporating Multi-Person Pose Estimation and Group Dynamics. It emphasizes
advanced features such as adaptive mechanisms by practice maturity, and
considerations for privacy and timing, reflecting the shift from solo to group
dance activities in a Human Group-AI Interaction Space.

##  1\. Introduction

Group dancing involves teams of performers engaged in highly synchronized and
tightly choreographed physical action. Such dances are a longstanding part of
most human societies—they appear in many cultures and across most of recorded
history (Sachs, 1963). They persist and prosper today in a variety of
currently popular forms, such as traditional folk dancing (Chauvigné et al.,
2019), line dancing (Joseph and Southcott, 2019), and cheerleading (Lesko,
1988). Cheerleading, a spirited amalgamation of synchronized dance,
acrobatics, and chants, is a particularly relevant and topical example of the
form due to its broad global appeal, the relatively young age of its
practitioners, its emphasis on encouraging female participation in athletics
(Moritz, 2011) and its recent provisional recognition as an Olympic sport
(Ruiz, 2016). The benefits of participating in amateur cheerleading and other
forms of group dance are also substantial and extend well beyond improved
physical fitness (Thomas et al., 2004) to include greater self-esteem (Deng et
al., 2022a) and group bonding (in the form of elevated levels of pro-social
behavior associated with performing synchronized movements (Cirelli, 2018)).

Despite these benefits, mastering the intensive synchronized movements of
cheerleading proves challenging for many participants (Zhou et al., 2021b).
Such failures are associated with dropping out of training and, more
seriously, effects such as reduced confidence, self-esteem, and perceived
social standing (Barnett, 2006). We believe interactive solutions to
facilitate practicing group dance routines could improve novices’ performance,
prolong their engagement with group dance activities, and, ultimately,
increase the rewards they reap from their practice. However, while the HCI
literature tackling dance learning and education is substantial, covering
topics as diverse as providing accurate and consistent feedback on performed
movements (Gibbons, 2004), through improving support for self-observation
(Raheb et al., 2016), to exploring feedback forms and modalities (Trajkova and
Cafaro, 2018), it predominantly considers dance learning to be a solo
activity. It focuses on and seeks to support individual dancers practicing
alone. We identify a research gap in the lack of literature considering the
unique aspects, properties, and environment of group dance activities. These
range from the fundamental and functional, such as the core emphasis on
synchronized group movements (Zhou et al., 2021b), through the social, such as
the delivery of appropriate and supportive collective feedback in a group
setting (Zhu et al., 2023), to the instructional, in that many cheerleading
troupes lack professional trainers and instead educational activities are
simply conducted by the most experienced members (Kim et al., 2023).

This paper seeks to fill this gap and understand the learning needs of troupes
engaged in group dance practice. Additionally, we seek to design interactive
systems that can support real-world group dance training. In this work, we
specifically focus on dance cheerleading, a sub-genre distinguished from other
cheerleading forms by its strong focus on rhythmic, synchronized movements and
reduced emphasis on individualistic expression (Deng et al., 2022b). It is
primarily practiced by college teams, in groups of between about 10 and 30
individuals, who lack professional trainers. These properties make it an ideal
candidate for study – the precision of the movements it requires is high, the
social environments in which it is practiced are complex and nuanced, and it
lacks an established tradition of professional coaching. It represents a dance
learning context that has been historically overlooked and one in which we
believe that interactive technology has the potential to realize substantial
gains in terms of learner performance, attitude, and experience.

To realize these goals, we first conducted a formative study using video
observation of practice sessions and one-on-one interviews with a cohort of 16
amateur cheerleaders (five instructors and 11 learners). We synthesized the
data captured into 24 distinct challenges under the overarching theme of
giving and receiving feedback on learners’ performance. We took these
challenges forward into a design workshop conducted with six design
researchers, who ultimately generated 55 ideas for vision-based interactive
systems capable of meeting one or more of the identified challenges. To
validate these ideas, we weaned them down and transformed them into 5
technology probes and 15 representative storyboards, each encompassing aspects
of several of the original ideas, and presented them to nine cheerleaders
(organized in groups of three) following the technology probe (Hutchinson et
al., 2003) and speed dating (Davidoff et al., 2007; Zimmerman and Forlizzi,
2017) approach. Their feedback provides insight into both the specific designs
presented and the underlying issues they seek to address and represents
valuable guidance for future work seeking to develop interactive systems to
support group dance activity.

Figure 2. Schematic of the research process: The purple box shows the process
we followed to create concrete prototypes, from understanding end users and
their context to exploring and evaluating the pre-prototype design space.
Finally, we suggest future directions for system design and development.

This displays a research process diagram for developing group dance
interactive practice systems. It details steps from literature review to
potential user experience enhancement, using method icons (M1 to M6) such as
literature review, observations, interviews, design workshop, and technology
probe & speed dating workshops, culminating in evaluating expanded design
concepts.

This study makes the following contributions to the DIS community:

  * •

We expand the design space of interactive systems to support teaching and
learning dance by documenting the unique challenges inherent in group contexts
(Section 5). Future researchers can use this content to understand the
practice of group dance learning and develop their own novel prototypes and
designs.

  * •

We present novel designs for supporting group dance learning (Sections 6 and
7), providing a concrete case study of how such systems can be conceived and
developed. Our solutions focus on feedback and communication and are validated
by developing 15 specific scenarios and using these in speed dating and
technology probe studies. These design examples and user reactions can
catalyze and inspire future designers working on interactive systems for
learning group dance.

This paper is structured as follows (Figure 2). Firstly, we applied a human-
centered approach to deeply understand human-human interactions (HHI)
(Pritchard et al., 1988; Weldon and Weingart, 1993). We show the relevance and
novelty of our proposal in addition to the existing design space through the
literature review (Figure 2-M1) (Section 3) on this topic. We then conducted a
formative study that resulted in addressing 24 specific needs and pain points
(Figure 2-M2&M3) (Section 4). We then conducted a design ideation workshop
with six designers, which resulted in 55 innovative ideas that we refined into
15 storyboards to articulate new design dimensions (Figure 2-M4) (Section 5).
For the evaluation phase, we tested these dimensions against potential user
experiences using technology probes and speed-dating workshops focused on RGB
camera-based designs (Figure 2-M5&M6) (Section 6). This approach ensured that
our expansion of the design space was theoretically grounded and deeply rooted
in the empirical evidence from real-world group dance practice sessions.
Finally, we discuss the design implications (Section 7) and conclude the paper
(Section 8).

##  2\. Related Work

###  2.1. Dance Practice and Learning in HCI

Dance education, rich in diversity across various genres, presents a unique
set of challenges in both individual and group contexts. While individual
dance practice emphasizes mastering specific techniques, personal expression,
and interpreting music, it inherently involves navigating physical and
psychological challenges such as maintaining endurance and coping with the
pressures of solo performance (de Las Heras-Fernández et al., 2023; Etzinger,
2023; Schwender et al., 2018). However, these challenges take on a different
dimension in the context of group dance. Specifically, despite the critical
role of group dynamics in dance (Buckroyd, 2000), the particular difficulties
encountered in group settings — such as achieving synchronization, managing
interdependence among dancers, and navigating group feedback processes — have
been, by and large, overlooked in the existing literature on interactive
systems to support dance education.

The spectrum of group dance genres, from the coordinated precision of ballet
ensembles to the collective storytelling in folk dances, highlights the
importance of synchrony and teamwork. Achieving seamless integration of
movements in these styles is a significant challenge, requiring individual
skill and a deep understanding of group behavior and interaction (Buckroyd,
2000). This study, therefore, zeroes in on the element of synchronization as a
pivotal aspect of group dance, recognizing its paramount importance in the
aesthetic and technical success of group performances.

Dance cheerleading, aligning closely with synchronized dance, offers a unique
perspective within the group dance milieu. This style is distinguished from
other cheerleading forms by its focus on rhythmic, synchronized movements and
less emphasis on individualistic expression (Deng et al., 2022b). Especially
the standardization and precision required in Korean college cheerleading
routines make it a compelling subject for studying group dance dynamics (Kim
et al., 2023). It represents an ideal case to explore the complexities and
challenges of group synchronization and collective performance in dance. This
exploration aims to shed light on the less-explored aspects of group dance
practice, specifically addressing the difficulties in achieving unified group
execution and the role of feedback in such settings.

###  2.2. Systems for Interactive Dance Practice

Interactive systems for dance practice, developed within the realm of HCI,
have historically employed various technologies to capture and analyze
movement (Raheb et al., 2019). These include vision-based motion capture (Chan
et al., 2011; Usui et al., 2015a; Tsampounaris et al., 2016; Aristidou et al.,
2014, 2015), depth cameras (such as Microsoft Kinect) (Trajkova and Cafaro,
2018; Anderson et al., 2013; Kyan et al., 2015; Camurri et al., 2016a;
Alexiadis et al., 2011; Marquardt et al., 2012), and inertial motion sensors
(Kitsikidis et al., 2014). Each of these tools offers unique capabilities for
detecting and interpreting dancers’ movements, contributing to the rich
tapestry of technological solutions available for dance training. Alongside
capturing movements, a significant body of research has also focused on
translating the results of movement assessment into appropriate and effective
feedback (Trajkova and Cafaro, 2018, 2016). This feedback is delivered through
various modalities and different styles. It is widely acknowledged that
carefully designed and appropriate feedback must be delivered to enhance a
dancer’s learning experience and the accuracy of their performance (Trajkova
and Cafaro, 2018).

However, transitioning these systems to support group dance practice presents
distinct challenges, not least in multi-person movement capture and analysis.
Recent developments in computer vision and deep learning have begun to address
these challenges and multi-person pose estimation using RGB cameras
(Bazarevsky et al., 2020; Lugaresi et al., 2019; Xu et al., 2018; Zhang et
al., 2019), which are now common in mobile phones, is now achievable. While
previous approaches to capturing multiple people’s movements through a variety
of devices have been proposed, utilizing only a single RGB camera is an
important step toward creating systems that are accessible and portable enough
for real-world group dance environments (Lee et al., 2020; Zhou et al., 2021b;
Zhu et al., 2023; Kang et al., 2023). Accordingly, we adopt this approach
throughout our work. Despite these technical developments, interactive systems
to support group dance (Zhou et al., 2021b, 2019; Lee and Lee, 2023; Zhu et
al., 2023) remain scarce in the research literature in comparison to the
wealth of work focusing on individual dance practice. This research gap,
especially in creating accessible systems for group dance practice, forms the
basis for the work described in this paper.

###  2.3. Early-Stage Design Evaluation

Evaluating design alternatives at an early stage serves to explore and
evaluate the extended design space before prototyping, ensuring alignment with
user needs and expectations. This process employs methodologies such as speed
dating workshops (Zimmerman and Forlizzi, 2017; Davidoff et al., 2007),
experience prototyping (Davidoff et al., 2007), paper prototyping (Sefelin et
al., 2003), and scenarios (IxDF, 2016). It de-risks design ideas and thus
serves as a crucial step in the design process. It enables designers to refine
and adjust concepts based on practical user requirements, significantly
enhancing the potential for the solutions to meet real-world needs effectively
and without necessarily being tied to introducing new technologies into
unexplored areas.

This study leverages a technology probe workshop (Hutchinson et al., 2003;
Zhou et al., 2021a) and the speed dating method (Zimmerman and Forlizzi, 2017;
Davidoff et al., 2007) to evaluate the use of RGB cameras in group dance
contexts. Analogous approaches have been used in previous research on mixed
reality mirror systems for improvisational choreography to quickly identify
limitations and opportunities in the user experience of the technology (Zhou
et al., 2023). By enabling direct interaction with emerging technologies, the
technology probe workshop helps identify user needs and the practical
challenges of technology integration. Conversely, the speed dating methodology
facilitates quick, iterative feedback on various design concepts, allowing for
an agile refinement process based on user interaction and response. Together,
they provide a robust framework for assessing both the technical viability and
user experience implications of integrating RGB cameras into dance practice,
guiding our design process to be both technologically innovative and deeply
aligned with user needs. This dual approach ensures our exploration into the
expanded design space is comprehensive and that the resulting interactive
systems are both technologically functional and highly relevant to the
specific demands of group dance practice.

Figure 3. Existing Design Space: Overview of eight key design dimensions for
interactive dance systems, based on a review of 22 studies. This diagram
encapsulates essential factors like Device (Capture Movement), Movement
Parameter, Modality, and others, highlighting the foundational elements for
advancing dance technology in education.

This presents a comprehensive matrix categorizing the design space of
interactive dance systems. The matrix is organized by various design
dimensions such as devices used to capture movement (e.g., Kinect, optical
motion capture), types of movement parameters analyzed (e.g., motion accuracy,
timing), feedback modalities (e.g., visual, auditory, tactile), types of
feedback (e.g., corrective, reflective), feedback cadence (continuous or
discrete), and the devices for feedback delivery (e.g., 2D screen, wearable
haptic device). Each cell in the matrix is color-coded to indicate the
relevance or application of each element to different user groups (amateur,
professional, all levels) and various technology aspects in dance education.

##  3\. Existing Design Space for Interactive Dance Systems

We examined the existing design space for dance interactive practice systems
by analyzing a total of 22 interactive concepts, including 17 concepts (Chan
et al., 2011; Kyan et al., 2015; Camurri et al., 2016b; Raheb et al., 2018;
Molina-Tanco et al., 2017; Trajkova and Cafaro, 2018; Alexiadis et al., 2011;
Usui et al., 2015b; Aristidou et al., 2014, 2015; Fujimoto et al., 2012;
Kitsikidis et al., 2014; Essid et al., 2013; Magnenat-Thalmann et al., 2008;
Marquardt et al., 2012; Bakogianni et al., 2007; Vicky Karkou and Kavakli,
2008; Pristaš et al., 2016; Anderson et al., 2013) from a ’Dance Interactive
Learning System’ review up to 2019 (Raheb et al., 2019) and an additional five
concepts introduced between 2019 and 2023 (Zhou et al., 2021b; Kang et al.,
2023; Zhu et al., 2023; Blanchet and Han, 2023; Lee et al., 2020). This
analysis led us to summarize the design dimensions of current systems into
eight distinct dimensions, categorized within four broader categories
reflecting the ’Dance Interactive System Workflow (Raheb et al., 2019)’:
Student Moving, Capturing Movement, Processing Data and, Feedback. These are
shown in Figure 3, and the design dimensions are defined as follows:

  * •

Target Group: Systems are designed to cater to various skill levels, including
amateur dancers seeking foundational skills, professional dancers focusing on
advanced techniques, and systems accommodating all levels, offering a broad
range of functionalities.

  * •

Device(Capturing Movement): A variety of devices are employed for movement
capture, including Kinect for gesture and posture recognition, optical and
inertia motion capture systems for precise movement tracking, gyroscopes
embedded in limbs for orientation and rotation data, depth sensors for spatial
analysis, and RGB Cameras for accessible, camera-based capture.

  * •

Movement Parameter: Analyzed parameters include motion accuracy (precision of
movements), overall similarity (comparative analysis with a standard),
teacher’s movement (mimicry and learning), and timing (temporal alignment with
music or choreography).

  * •

Modality: Feedback is delivered through various modalities, such as screen
displays, auditory cues via speakers or earbuds, and tactile sensations
through wearable haptic devices, catering to different learning preferences.

  * •

Type: The feedback type encompasses correction (corrective feedback on
errors), judgment (evaluative comments or scores), and reflection (encouraging
self-assessment and insight).

  * •

Quantitative Type: Feedback can be continuous, offering ongoing data during
the dance, or discrete/asynchronous, providing information at specific
intervals or after the performance.

  * •

Phase: The timing of feedback varies, with some systems offering insights on a
real-time basis during the dance and others on a post-hoc basis, analyzing
performance after completion.

  * •

Device(Feedback): Devices for feedback delivery include 2D screens (TVs,
projectors) for visual feedback, tablets/mobile phones for portable access,
head-mounted AR/VR for immersive experiences, speakers for audio cues, earbuds
for private auditory feedback, and wearable haptic devices for physical
sensations.

Each of these dimensions plays a crucial role in defining the functionality
and user experience of dance interactive systems functionality and user
experience. Understanding these dimensions allows us to grasp the current
landscape of such systems, providing a foundation for our study’s objective to
expand this design space for group dance contexts.

##  4\. Understanding the Problem Space of Group Cheerleading Practice

Through an exploratory study, we aimed to uncover the challenges college
student cheerleaders face during their practice sessions. Due to ease of
access, we focused on the activities of a single cheerleading crew based at
our local university. We engaged this group using complementary methods:
indirect and participatory observation of practice sessions and in-depth
interviews. The observational methods supported gathering insights from actual
practice sessions, capturing cheerleaders’ authentic and natural behavior
while avoiding the recall biases that might impact self-reported data. The in-
depth interviews delved into the participants’ experiences, challenges, and
perspectives, providing deeper insights into their pain points and shedding
light on the behaviors seen during the observation sessions.

Figure 4. Micro-perspective View of Practice: The instructor gives  during the
small group practice. At the end of the practice,  is given on the practice,
which is used to prepare for the next practice. So, in a real-world small
group practice, post feedback includes feedback after the choreography
practice and before the next practice.

This depicts a micro-perspective view of a dance practice cycle focusing on
the timing and type of feedback an instructor provides. It illustrates three
feedback phases during dance practice: Before the Movement– The instructor
provides post feedback, analyzing the previous attempt and suggesting
improvements. During the Movement– Real-time feedback is given, with immediate
corrections such as ”Arms up higher!” After the Movement– A general review of
the practice session is discussed to prepare for the next practice. The figure
shows that from a micro perspective, there are three phases: before, during,
and after the movement, but there is no real distinction between feedback
before and after the movement. Therefore, it emphasizes that feedback can be
divided into two types: real-time feedback and post-feedback.

###  4.1. Indirect Observation

We observed nine practice sessions with the cheerleading crew spread over
three weeks. Each practice session lasted approximately two hours and was
attended by 16 individuals. Eleven of the troop members were first-year
students (and cheerleading novices), while the remaining five were sophomores
who had at least one year of cheerleading experience. This crew was organized
according to experience level and grade, with one of the sophomores serving as
president and two as vice presidents. In each session, we observed, one author
acted as an observer. Additionally, we installed three GoPro cameras to
support later analysis (based on indirect observation). Two were placed in the
corners of the practice room to provide a wide-angle view of the entire space
and encompass the movements of all participants. The final camera was attached
to the chest of one of the sophomore peer instructors to provide a first-
person view of their perspective.

###  4.2. In-depth Interview Procedure

Participants for the in-depth interviews were selected from students who are
or have been involved in the cheerleading crew, including the 11 current
student members (5M/6F, p1~p11) and 5 participants (3M/2F, p12~p16) who served
in instructor roles (two of them retired). The students had an average age of
18.64 (SD=0.81), with an average of 4.64 months (SD=3.98) of cheerleading
experience. Two students were sophomores with over a year of experience, while
the other nine were freshmen with less than six months of experience. The
instructors had an average age of 20 (SD=1.73), with 13.2 months (SD=7.46) of
experience as a student and 6.4 months (SD=4.16) as an instructor in
cheerleading. Both students and instructors had experience in other group
dances such as ballet or K-pop, with an average of 9.82 and 14.8 months,
respectively. All participants, except the two retired instructors,
participated in the observation phase. This multidimensional selection aimed
to capture insights from both the instructors’ and students’ perspectives.

Interviews were conducted individually with each participant in the troop,
ensuring perspectives from senior and junior members were captured. Sessions
lasted approximately one hour. Interviews were audio recorded and transcribed
with participant consent. In general, the interviews focused first on seeking
explanations for behaviors seen in the participant observation sessions and
second on how participants anticipated that interactive systems could be
designed to support cheerleading activities in the future.

Figure 5. Macro-perspective View of Practice: If each step goes well, the
dancers move on to the next step. If each step is unsuccessful, they return to
the previous step.  Initially, the instructors teach the dancers the
choreography.  Based on this, the dancers learn the choreography on their own.
At this time, the instructors may go around and give individual coaching.
Once the movements have been learned, the dancers practice in groups of 3 to
5. Small-group practice can be (a) under the instructor’s guidance, (b) with
the instructor dancing together, or (c) just the dancers without the
instructor.  In the final stage, everyone comes together to synchronize the
entire movement. At this time, the instructor provides guidance or engages in
exercises together.

It illustrates the stages of dance practice from early to late show
preparation through a series of four panels: Instructor Guidance: An
instructor demonstrates dance moves to a line of dancers directly facing him.
Individual Practice: Dancers practice the moves independently without the
instructor’s intervention. Small-Group Practice: Dancers practice in small
groups of three to five, shown in three sub-stages: with the instructor
actively participating, the instructor observing but not participating, and
without the instructor. Whole-Group Practice: All dancers practice together in
unison, synchronizing movements under the instructor’s guidance. Each panel is
supported by a corresponding real-life image showing dancers and the
instructor in a practice setting, emphasizing the transition from learning to
execution.

###  4.3. Analysis of College Students’ Cheerleading Practice

We found that the troop frequently split into small practice groups (Figure
5-). Busy with sophomore-level practice, instructors often could not help
junior members, especially during these small group sessions. In this manner,
we found that students had difficulty knowing when and how to correct their
behavior within the small groups. Interested in this issue, our interviews
explicitly queried participants on the role of small group practice in the
overall learning process. It quickly became apparent that while the end goal
is for everyone to do the same thing, everyone cannot do the same thing at the
start. Consequently, we found that the structure of overall macro-practice
within the group iterated between periods of individual practice, small group
practice, and complete practice, transitioning between these stages as and
when required (Figure 5).

We further investigated the characteristics and challenges of feedback in
small group practice in the presence of an instructor. These insights emerged
from thematic analysis (Braun and Clarke, 2006; Hsieh and Shannon, 2005) and
fell into three broad categories: Insights into feedback received in real-time
during the action, insights received after action, and common insights (Table
1). These three categories reflect the observation that feedback in a non-
professional dance studio takes place either during a movement or after a
movement. While prior work has noted that practice can be categorized into
three phases (before, during, and after a movement) (Trajkova et al., 2019),
our experience in an amateur dance studio indicates that the line between
’before movement’ and ’after movement’ is highly blurred (Figure 4). The final
categorized insights were rendered onto cards and used in an ideation workshop
in the next phase of the project (Figure 6).

Table 1. 24 insight list from the in-depth interview: Summary of 24 insights
from the formative study on feedback experiences in group dance practice. The
table categorizes insights into three key phases: during movement, post-
movement, and those applicable across both contexts. This classification
highlights dancers’ nuanced preferences and challenges in receiving feedback,
providing a foundational understanding for designing interactive dance
feedback systems.

The table provides insights from an in-depth interview on feedback in dance
practice, structured by feedback timing: real-time and post-movement. It
categorizes experiences into positive, neutral, and negative experiences,
detailing participants’ insights and reactions to different feedback
scenarios. The table aims to inform the design of interactive dance feedback
systems by highlighting dancers’ preferences and challenges.

##  5\. Expanding the Design Space of Interactive Systems for Group Dance
Practice.

This section details the systematic process we used to generate prospective
designs for interactive systems that can improve small-group cheerleading
practice. We outline the workshops and methods we used that were instrumental
in generating, diverging, and converging ideas. Our goal was to, firstly,
expand the design space of interactive systems for dance practice into a set
of provocative ideas tackling genuine problems and, secondly, to converge this
set down into a smaller group of firm and clearly expressed concepts that
would be amenable to further rounds of design inquiry, development, and study.

In particular, we chose a computer vision-based approach to explore and expand
the design space for improving small-group cheerleading practice.
Specifically, we chose to analyze body movement through RGB single-camera-
based pose estimation. The portability of the single-camera setup made it
suitable for a real-world dance studio environment. We also provided
participants with information that can be computed from pose coordinates (pose
similarity(Zhou et al., 2021b; Lee and Lee, 2023), pose correctness(Kang et
al., 2023; Lee et al., 2020), temporal alignment(Zhou et al., 2021b), etc.).

Figure 6. Workflow of the Idea Divergence Workshop Process: This diagram
visualizes the development of a dance feedback system, beginning with user
insights from interviews that inform critical feedback information. These
insights guide the selection of feedback modalities, culminating in creating
seed ideas grouped into final storyboards showcasing potential system
interactions. In Session 1, ideation for  was conducted based on  insights and
insights. In Session 2, ideation for  was conducted based on both  insights
and  insights.

This outlines the Idea Divergence Workshop Process for a dance feedback
system, detailing two sessions: one for Real-time Feedback and another for
Post Feedback. It starts with user needs and pain points identified through
observation and interview, leading into specific information categories like
pose correctness and similarity scores. These feed into modalities for
delivering feedback—visual, text, audio, verbal, and haptic. The process
culminates in seed ideas, illustrated by storyboards, which visualize
potential user interactions and feedback presentation in the system.

###  5.1. Idea Divergence Workshop

This workshop focused on developing ideas for providing effective feedback to
a small group of cheerleading practitioners that could address or highlight
the experiences reflected in the existing 24 insight lists from the previous
step. The main objective was to explore different ways the ’who, what, when,
and how’ could be communicated through an interactive system based on
assessing the cheerleaders’ behavior during small group practice (Figure 6).
This ideation workshop involved six design graduate students(3M/3F) and had
the following structure.

Ice breaking & priming (15 minutes): The workshop began with a brief
introduction to cheerleading and its unique challenges, especially in small
group practice. The lack of clear feedback was highlighted as a problem, and
examples of computer vision-based interactive systems for dancing were
presented to set the stage for idea generation (Zhou et al., 2021b; Lee and
Lee, 2023; Anderson et al., 2013; Zhou et al., 2023; Trajkova and Cafaro,
2018).

Introduce Insights & Modality (5 minutes): Participants were provided insight
cards derived from the previous in-depth interviews: , , and  insights (Table
1). Participants were also introduced to different modalities (visual,
auditory, tactile, etc.) that could be considered for delivering feedback.

Brainstorming (45 minutes + 45 minutes): The brainstorming session was divided
into two parts: The first 45 minutes focused on systems that provide ; insight
cards related to real-time and common insights were utilized. The second 45
minutes focused on systems that provide , using cards related to post feedback
and common insights. In both sessions, participants were asked to explore how
and what information could be communicated to address or emphasize specific
insights.

To expand the design space, we conducted an ideation workshop that generated
55 ideas for an interactive system to improve group cheerleading practice. 26
ideas focused on  during movement, and 29 focused on providing . These ideas
were systematically categorized based on timing, audience, information
conveyed, devices used, and feedback modality. The raw data of the seed ideas
is presented as supplementary material. We combined these ideas from this rich
collection into a provocative storyboard that effectively represented the
expanded design space (Figure 7). These storyboards served as a valuable tool
for evaluating the potential and limitations of the proposed interactive
system in the subsequent speed-dating workshop.

###  5.2. Expanded Design Space

We employed storyboard-based design representations to facilitate a nuanced
understanding of potential user experiences and system interactions. Because
of our focus on the small group context, these storyboards were curated based
on three main criteria: 1) consideration of the group context, 2) anticipation
of interactions between members after the system is deployed, and 3) inclusion
of scenarios not adequately addressed in existing dance interactive learning
systems.

Not only merely addressing the superficial challenges faced by cheerleaders,
our selection of storyboards was intentionally provocative, aiming to catalyze
discussion around the complex group dynamics we had observed. These
storyboards also extended the themes and techniques explored in the Technology
Probe Workshop, providing a cohesive narrative for vision-based interactive
systems.

To compare different interactive paradigms in a group cheerleading context, we
crafted 15 storyboards. Of these, ten focused on providing  during movements,
while five were geared towards delivering insights of  (Figure 7). A
description of each storyboard and the types and modalities of feedback the
system provides can be found in Figure 8.

Figure 7. Storyboards: A compilation of 15 storyboard images depicting real-
time and post-feedback concepts for interactive dance systems. The collection
features ten ideas designed for  and five ideas for , illustrating a broad
spectrum of approaches for supporting group dance practice through vision-
based technology.

This displays 15 storyboards split into real-time interactive and post-
interactive systems for dance training. It visually explores advanced feedback
mechanisms using augmented mirrors and other technologies. Examples include an
Error-Induced Pause where music stops for mistakes, a Color-Coded Performance
for accuracy visualization, and a Comprehensive Report that summarizes
performance post-practice. These storyboards demonstrate potential feedback
methods designed to enhance learning and precision in dance training through
immediate and reflective feedback mechanisms.

Figure 8. Expanded Design Space: The design dimensions for interactive dance
systems are illustrated through 15 storyboards developed from a design
ideation workshop. This expanded framework introduces two additional
dimensions—Feedback Timing and Feedback Privacy—to the existing eight,
showcasing the evolution and diversification of design considerations for
enhancing group dance practice.

This matrix visualizes the design dimensions from 15 storyboards created in an
ideation divergence workshop, highlighting newly added dimensions of feedback
timing and privacy. It builds on the existing design dimensions shown in
Figure 3, presenting a refined framework for interactive dance systems across
various user levels and technology applications.

##  6\. Evaluating Expanded Design Space

We assessed the design space for interactive feedback systems to bolster group
dance practice. This evaluation sought to pinpoint the limitations and
opportunities of various interactive concepts and technologies generated from
the preceding design ideation workshop. Our objective was to gather insights
critical for guiding the development of tangible prototypes. Specifically, we
concentrated on understanding how these systems could be effectively utilized
within a real-world dance studio, exploring their potential applications and
impact on the dance practice environment.

Figure 9. Technology Probes: Participants experienced the technology probe
through an augmented mirror during the workshop.  Presenting overall pose
similarity through emoticons,  overlaying pose skeletons on top of each
participant,  overlaying each other’s pose skeletons (not their own) on top of
each other,  visualizing color-coded skeleton for each body part with an
overall score based on the calculated pose similarity by body parts, and
presenting overall pose similarity through background effect changes.

This displays five panels capturing feedback mechanisms from a technology
probe workshop, shown on a screen to participants. A) Emoticon feedback for
overall pose similarity. B) Overlaid pose skeletons of different participants.
C) Multiple overlaid skeletons highlighting individual discrepancies. D)
Color-coded skeleton for body part accuracy. E) Overall pose similarity with
an animated crowd background, summarizing performance quality.

###  6.1. Technology Probe & Speed Dating Workshop

####  6.1.1. Technology Probe Workshop

The technology probe workshop had two objectives: 1) to increase participants’
familiarity with vision-based interactive systems and 2) to identify
limitations and opportunities for integrating these technologies into a real-
world dance studio (Hutchinson et al., 2003; Zhou et al., 2021a). Given the
lack of such systems in current studios and the lack of real-time vision-based
systems for group dancing, this session served as a primer on the capabilities
of these systems, thereby informing subsequent discussions during the Speed
Dating Workshop.

Technology Probes:  Five technology probes were developed according to the
following criteria: 1) demonstrating how vision-based systems capture and
evaluate behavior, 2) low implementation cost and time, and 3) alignment with
storyboard concepts presented in the Speed Dating Workshop. The first probe
displayed pose similarity through discrete and value feedback (Gibbons, 2004;
Raheb et al., 2016), employing emojis based on a 5-point scale (”Excellent” to
”Poor”) (Figure 9-). The second and third probes visualized participants’ own
skeletons overlaid (Figure 9-) on top of their bodies or skeletons of
participants other than themselves overlaid on top of their bodies (Figure
9-). The fourth probe presented pose similarity overall and by body part,
using a color-coded system on the skeleton visualization (Figure 9-). This
probe gave continuous and corrective feedback with colored skeletons (Zhou et
al., 2019, 2021b). The fifth probe metaphorically reflected the similarity of
current poses by altering the number of people visible in the concert hall
with a 5-point scale (Figure 9-). All probes utilized an augmented mirror
interface, consistent with prior research, to focus on vision-based
modalities.

We presented the technology probe using an augmented mirror primarily because
we explored a vision-based system, not to exclude other information delivery
modalities such as auditory or haptic. Through the technology probe, we wanted
participants to experience and understand how a vision-based system captures
and evaluates their movements. Thus, following previous studies(Zhou et al.,
2022, 2023; Martinez Plasencia et al., 2014), we used an augmented mirror as
it is a mature and expressive way to showcase such experiences.

Implementation:  All features were implemented using OpenCV111OpenCV:
https://opencv.org/ in a Python environment. We employed MoveNet for multi-
person pose estimation due to its ability to perform over 30 FPS on smaller
devices 222MoveNet, Tensorflow Hub:
https://www.tensorflow.org/hub/tutorials/movenet, which makes it suitable for
real-world dance studio contexts. While methods like cosine similarity(Kang et
al., 2023; Guo et al., 2022, 2021) and dynamic time warping(Zhu et al., 2023;
Kim and Kim, 2018; Ferguson et al., 2014) were considered to calculate pose
similarity, we chose not to utilize them. We decided that using Body-part
level Pose Distance (BPD) values (Zhou et al., 2021b; Lee and Lee, 2023) was
sufficient because we wanted to 1) rapidly explore the design space before
building a relatively robust concept with a high-quality movement dataset for
each session participant and 2) provide an overall experience with a vision-
based interactive system for evaluating dance movements. Thus, we use the same
method to calculate the normalized $BPD(i,t)$ for each body vector $i$ at a
given frame $t$ (Zhou et al., 2021b; Lee and Lee, 2023):

(1)
$BPD(i,t)=\left(\frac{d(i,t)}{J}\right)^{\lambda}=\left(\frac{\sum_{J}\left|\vec{v}_{j}(i,t)-\vec{v}_{R}(i,t)\right|}{J}\right)^{\lambda}$

where $\vec{v}_{j}(i,t)$ is the $i$-th normalized vector
($i\in\\{1,2,….,14\\}$) of the $j$-th person($j\in\\{1,2,…J\\}$), and
$\vec{v}_{R}(i,t)$ is the $i$-th normalized reference vector, as same as the
average of the normalized vectors of all participants. The lambda is a
parameter used to set the sensitivity of the pose discrepancy. To compute the
overall pose similarity, a simple addition of the BPD vector values was used
for a simple implementation.

Figure 10. Setup: A large monitor was positioned at the front of the space,
and a Logitech Stream camera placed on top of the monitor received the
dancers’ movements to provide real-time feedback. Each session was conducted
with three participants, and the sessions were recorded via two GoPros located
in the front and back corners of the space.

This oblique 3D diagram depicts the configuration used in a technology probe
workshop. It shows three participants and the key equipment: a front-
positioned monitor displaying real-time feedback, a Logitech Stream camera
above the monitor, and two GoPro cameras capturing the session from the front
and back of the space.

Setup and Procedure:  The experimental setup included an 86-inch LED TV
(Samsung 86UHD-MN) to serve as the augmented mirror and a Logitech StreamCam
for real-time capture within a 5m x 6m indoor space (Figure 10). All sessions
were recorded. Each probe was explored for approximately 20 minutes through
free movement and set cheerleading routines. Participants did not see the
skeleton visualization during the first and fifth probes; it was displayed
from the second through the fourth probes. The reason for withholding the
skeleton visualization in the first probe was to assess natural interaction
with the system and its inherent reliability. It was displayed in subsequent
probes to offer insights into how body motions were captured and evaluated.
The final probe again omitted the skeleton visualization to align with
storyboard concepts that did not always employ an augmented mirror.

####  6.1.2. Speed Dating Workshop

Similar to previous research (Zhou et al., 2023), the Speed Dating Workshop
was designed to offer a rapid, cost-effective assessment of the expanded
design space explored in earlier phases (Zimmerman and Forlizzi, 2017). The
session evaluated the challenges and opportunities of various design
alternatives for practical implementation in real-world dance studios.
Leveraging insights from the preceding Technology Probe Workshop, participants
engaged with multiple storyboards to consider the practicalities of
incorporating vision-based interactive systems into the cheerleading dance
studio (Figure 7). Discussions centered on participants’ segmented experiences
with each concept, their reliability in practical settings, their potential
impact on dance studios, and avenues for combining and improving the systems.

###  6.2. Participants & Procedure

We employed a two-tiered approach to evaluate our design alternatives (Zhou et
al., 2023): a Technology Probe Workshop (Hutchinson et al., 2003; Zhou et al.,
2021a) followed by a Speed Dating Workshop (Zimmerman and Forlizzi, 2017).
These methods afforded us both a technological examination and a user-
experience-centered evaluation of vision-based interactive systems for
cheerleading practice (Zhou et al., 2023).

For the study, we recruited 9 participants (2M/7F) with experience in the same
cheerleading crew we previously studied. The average age of the participants
was 20.11 (SD=2.03), and they had been learning cheerleading as students for
an average of 10.89 months. Three participants (P6, P7, and P9) had been
cheerleading instructors for an additional year. In addition to cheerleading,
the participants had an average of 28.67 months of experience learning group
dances such as K-pop or ballet. However, their experience with computational-
interactive systems such as technology probes was limited, with an average
rating of 0.33 on a 4-point Likert scale (0-Never, 3-I consider myself an
expert). Specifically, each participant had performed at least once with the
crew and was either currently active or had been active for a minimum of one
year in the past.

The study was conducted in teams of three participants and ran three times. As
a token of appreciation, participants were compensated with 25,000 KRW. Each
session spanned 2.5 hours and was split into two distinct activities:

Technology Probe Workshop(30m): The first half-hour was allocated for the
Technology Probe Workshop. Participants with limited prior exposure to
computer vision-based systems participated in this section. Inspired by prior
research (Zhou et al., 2023), the aim was to evaluate the technological
feasibility and ascertain user engagement with our proposed interactive
cheerleading systems.

Speed Dating Workshop(2h): The following two hours were dedicated to a Speed
Dating Workshop. Here, participants engaged with storyboards depicting the
interactive concepts that leveraged computer vision techniques. The primary
objective was to swiftly gauge user receptiveness and fine-tune the design
alternatives based on real-world studio scenarios. The extended duration
allowed for in-depth discussions concerning potential challenges and
opportunities should these systems be deployed in their dance studios.

These workshops provided comprehensive insights into the strengths,
limitations, and potential improvements for vision-based motion assessment
technologies in cheerleading practice.

###  6.3. Potential User Experience

Since our overall research has focused on exploring and expanding a design
space for vision-based interactive systems in group dance contexts, we
summarize the results of evaluating this design space through technology and
speed dating workshops. The lead author utilized thematic analysis methods to
analyze and interpret what was discussed at the workshops (Braun and Clarke,
2006; Hsieh and Shannon, 2005).

####  6.3.1. Technology Probe Workshop Results

Initial Expectations: Participants had initial expectations of the system
before trying it out. Some participants expected the AI system to provide
consistent and objective ratings. ”Sometimes one instructor thinks it’s right
and another thinks it’s wrong, so I think this system would be an objective
and accurate assessment. (P1)” There was also an expectation that the
cheerleading choreography would track detailed body parts (hand movements,
head turns, etc.) that are traditionally difficult to see with the human eye.
”When we’re practicing detailed movements, we’re checking a lot of hand
gestures, like turning our heads to each other, so I think there was an
expectation that those things would be tracked. (P2)”

Initial Explorations: Participants were initially free to test the
technology’s challenges and opportunities. They tested how accurately and
quickly the system captured their bodies. In particular, they focused on
movements that are typically utilized in cheerleading and tested how well it
tracked them when they performed occlusive or vigorous movements. ”I was
curious about how far the skeleton was tracked, so I wanted to see if wrists
and hands were tracked as well, to see how well it would recognize the body
when turning around. (P7)” They also tested the system by matching movements
to each other and making small body movements to see how sensitive the system
is to assess the similarity of movements. ”Since the system is measuring
similarity between people, I wanted to get a little bit of a baseline of what
it considers to be similar. (P7)”

Critiques: Participants criticized the vision-based technology for its lack of
real-world use in a dance studio. In particular, P6 mentioned that the
camera’s recognition speed is slightly delayed, confusing because it doesn’t
match your movements and the sound of your footsteps. P2 also pointed out that
the camera’s field of view is too narrow to recognize multiple people. In
addition, another participant pointed out that the narrow angle of view forces
people to stand close to each other, and when performing vigorous movements
such as cheerleading, body parts overlap, and tracking becomes difficult at
that moment. ”I think it would be better to use a wide-angle camera or
something like that because if we’re all crammed together on a small screen,
our bodies might overlap. (P7)” One participant also criticized the
limitations of 2D tracking, saying that even when the system thinks the
movements are the same, they may actually be slightly different. ”When we
actually showed it, it felt a little out of order because we weren’t matching
the shape (the skeleton); we were matching the behavior (the body). (P6)”

In addition to the vision-based pose evaluation system, some participants
criticized the augmented mirror and its visual effects. P6 said that in the
third probe (Figure 9-), it was difficult to focus on other participants’
skeletons and pay attention to their own movements, and P4 said that it was
difficult to know if the movements were correct because the skeletons of other
people were different from their actual body shapes. In addition, some
participants said that in the fifth probe (Figure 9-), the visual effects
actually covered the mirror, which had the negative effect of distracting
them. ”I don’t really see much of a difference, and I think it’s more
distracting to have to watch the motion and see the background change
underneath. (P4)”

Suggestions: Participants provided several suggestions for the use of the
technology in the context of real-world group dance practice. First, there
were suggestions for improving the limitations of the pose tracking
technology. P7 suggested that due to the limitations of the current 2D-based
tracking technology, depth should also be measured, or a multi-view would be
needed to evaluate cheerleading movements more rigorously. In addition, some
participants wanted more control over the system. They wanted to adjust the
sensitivity with which the system calculates pose similarity and set the
duration (window size) for which feedback is provided if it is discrete. They
felt the system’s settings should differ depending on the practice context. ”I
think if you make the system a little more sensitive when practicing the
basics initially, the psychological pressure will make you work harder. (P9)”

There were also responses about how the provided technology could be used
differently in a dance studio: P6 said that it would be suitable for
demonstrating and explaining the mechanics of the joints in a cheerleading
move rather than for assessment purposes. ”If the instructor has a skeleton
visualization and says that the joints of the body follow this route, I think
it’s a little bit more intuitive to show how the body should move and the
mechanism of the joints and stuff like that. (P6)”

Participants also discussed how reliable these systems would be in practice in
a dance studio. P9 said that the initial experience with the system would be
important. In particular, P1 said they would naturally trust it when it showed
them how their body was being captured, like a skeleton visualization, or when
they saw the scores change in real time. Despite this, several participants
said they did not fully trust the system. P6 and P7 felt that the systems were
similar to the machines that rate karaoke songs and would never fully replace
human motion assessment.

####  6.3.2. Speed Dating Workshop Result

Reflections on the Design Space for General Practice Apps

Augmented Mirror with Visual Cues: Participants discussed how the
visualizations provided through the augmented mirror would be experienced in
actual practice. Some participants felt that the visualizations provided
through the mirror were distracting and took away from the role of the mirror.
”It was a bit distracting. I don’t know if it’s used for games and stuff like
that, but other than that, I don’t think it’s appropriate for a training
situation. (P4)” P9 said that providing real-time visual feedback should be
subtle enough to be perceived by squinting. In addition, P7 believed that
those who can fully absorb the information provided by the mirror during
intensive movement are already skilled enough not to need such an auxiliary
system. P7 also highlighted that different systems are needed at different
times of practice. ”I think that someone who can check themselves by looking
at the visual information in the mirror at that moment while dancing is
already somewhat skilled (P7)”. However, university cheerleading crews usually
practice for a specific performance. As such, participants noted that the
closer they get to the performance (and the more experienced they thus are),
the more they have to practice without mirrors and that visual aids would
inherently become less applicable. ”At the end of the day, the performance
itself is done in a situation with no visual support, so I don’t think visuals
will be very helpful at some point. (P6)” P2&P3 felt that auditory cues would
be more suitable for real-time feedback.

Discrete Feedback vs Continuous Feedback: Participants discussed whether
feedback should be provided continuously or discretely. Generally, when
feedback was provided continuously, it was presented through a score (0 to
100) or color (0 to 255) to provide a more precise indication of the
similarity of actions. On the other hand, discrete feedback was presented in
five bins (Excellent-Good-Fair-Poor-Bad) to give a rough idea of the
similarity of behaviors. P4 and P6 felt that individualized feedback was not
really constructive because it seemed to say that only their movements at that
moment were correct. P9 felt that the continuous feedback was more reliable
and that the more precisely they could assess the similarity of the movements,
the more reliable the system would be in practice, as it is vital in
cheerleading to match each other’s movements completely.

Feedback Timing: Participants also broke down the timing of when feedback can
be delivered to dancers. Typically, corrective feedback is given immediately
after an incorrect movement. However, there was also discussion of feedback
given before or simultaneously with a movement, such as confirmatory or
encouraging comments during correctly performed actions. Regarding feedback
before performing a particular movement, P9 said that being told to push
through a physically challenging section of the movement would fire her up.
Another participant said that if she repeatedly got a certain section wrong,
guiding her on what to do or shouting at her to focus on that section would
make her more tense and focused (P1), states which she regarded as beneficial
to her learning. In addition, one participant said it would be helpful to be
told the beats one after the other, especially if they were starting the
movements sequentially. ”I have friends who can’t count the beats, so I think
it would be good to have a reference for them so that if they miss something
like that, they can now count the beats with a blinking time or something like
that so that they can count the beats themselves. (P7)”

Evaluation Metrics: The participants elaborated on the more critical
information that should be conveyed to the dancers during actual cheerleading
practice situations. P4 mentioned that it is not very meaningful to point out
someone who made a move wrong or only to communicate the score, but it is
essential to communicate how to improve the move. In addition, P7 suggested
the possibility of a new dance evaluation metric, not just calculating pose
similarity or temporal alignment, but something evaluated sequentially in a
practice session from a macro perspective. ”I think it’s a little less
intuitive to have a score for each body part… In the training process, it’s
more like ’let’s match the movements!’ rather than ’let’s train the arms!’ I
think it would be nice to have a score for each training process… Some points
for memorizing the choreography, some points for matching the movements, some
points for detailed movement… (P7)”

Reflections on Design Space for Group-Specific Apps

Group Reflection: The participants discussed their experiences with reflective
feedback in cheerleading practice involving group rather than individual
reflection. In particular, P6 and P7 mentioned that the concept of generating
sounds with body movements to match the similarity of their movements through
sound (Figure 7-) was similar to how they use footsteps to check the beat in
actual practice. In particular, some participants hoped that this concept
would encourage them to collaborate with each other during practice. ”I think
it would be good because it would give us a small pleasure to match the sound,
and it would give us a sense of cooperation to try to match it together.
(P4&P6)” Regarding the concept of overlaying each other’s body skeletons
(Figure 7-), one participant said that it was similar to the practice of
wearing colored bracelets while standing in a line to match movements in real
practice. ”Like we actually practice with colored bracelets, I think if we
have our own skeleton color, we can recognize it a little bit better. (P2)”

Public Feedback vs Private Feedback: Participants discussed receiving feedback
publicly versus privately in group practice situations. P1, P5, and P6 said
that they would feel stressed if they received feedback publicly in front of
others that they were constantly doing poorly. On the other hand, some
participants said that public feedback actually made them feel more socially
tense. ”I think it’s really effective if you’re motivated, but if you’re not,
I think you’ll ignore private feedback. (P1)”; ”I think if we all listen to
someone’s feedback together, we can point out what they’re doing wrong, so I
think we need to know what we’re doing wrong. (P3&P4)”

Challenges of Interactive Systems in Dance Studios

Quantified Evaluation Results: Participants mentioned the quantitative metrics
that the system provides after evaluating their behavior. P1 said that these
quantitative indicators gave him confidence in the system. On the other hand,
some other participants were concerned that these quantitative metrics would
hurt the group atmosphere. ”I’m not sure why there’s a score… The whole point
of using a system like this is to improve something that’s a little bit wrong,
to make it better, not to see who’s better… (P4)”; ”I’m comfortable with vague
criteria, but I think it would bother me if my friend next to me got a 75 and
I only got a 74. (P6)”; ”I think cheerleading is all about making the same
moves together, so I don’t think we need to break down who’s good and who’s
not. (P7)” In this regard, P7 believes that objective metrics such as grades
should be passed through the instructor to the student as much as possible.

Impact on Group Dynamics: Participants were concerned about the potential
impact the system could have on group dynamics. Participants were concerned
that the system could confuse the role of the instructor: ”I think there could
be cases where the instructor gives feedback that the behavior is wrong, but
the students rebel because the AI says their behavior is right. (P7)”; ”I
think there could be cases where the system and the instructor disagree. Since
the behavior-matching process is centered around the instructor, who is also
the crew’s president, I think the instructor’s opinion should come first. (P1,
P2)” Additionally, P8 mentioned possibly taking on an unwanted role. ”In a
small group context, if the system says I’m the best at something, I feel like
I have to lead an exercise even if I don’t want to lead the group. (P8)”

Opportunities for Interactive Systems in Dance Studio

Purpose, Role, Use: Participants discussed how and for what purposes such a
system could be utilized within a dance studio. Some participants mentioned
that such a system could be used to set common goals and could be utilized for
evaluation purposes. ”I feel like we’re more motivated to get it right. (P7)”;
”I think we’ll practice movements a few times amongst ourselves and then use
the system as a final evaluation. (P2&P3)” Participants also mentioned the
need for the system to be selected and adapted to consider the group’s
characteristics and the practice period. ”For example, it’s okay if the music
is interrupted when we’re trying to figure out the moves together, but at the
beginning, we don’t know the choreography very well, so we’re taught through
skeleton visualization… I think a different system should be used for each
stage of learning. (P4)” In addition, P9 noted that the choice of system
should be considered in relation to the characteristics of the group. ”I think
it would be a lot of fun to use this as a game, like a good-natured
competition, but I think you’d have to use it carefully depending on the
group’s disposition. (P9)”

P7 also mentioned that such a system would serve as a tool to assist
instructors. He mentioned the process and dilemma of preparing amateur student
dance crews to perform and noted that balancing the use of interactive systems
would be essential. ”In the end, we practice for performance, but the higher
the performance quality, the harder it is to practice and the less satisfied
we are with the club. Since we are amateur students, we can be divided into
those who want to enjoy club activities and those who want to make a great
performance. It would be better if the characteristics and diversity of these
amateur groups could be considered and used appropriately. (P7)”

Re-purposing and Suggestions: Participants offered other suggestions for re-
purposing the system, particularly P7, noting that the concept of making
sounds through the body could be utilized in choreography where the same
movement is initiated on different beats. ”I think if you use this system,
even if you close your eyes, you can tell if you start the movement on the
right beat. (P7)” P7 also mentioned that it might be better to set a threshold
for the score, showing only pass or fail depending on the context. ”If the
dancer crosses the threshold, the score is passed, and the dancer is simply
informed of the pass, and if the dancer fails, the detailed score is given to
the instructor so that they can refer to it in their practice. (P7)”

##  7\. Discussion

###  7.1. Navigating Feedback Dynamics

Through observation and in-depth interviews conducted within the dynamic
context of cheerleading, we identified 24 key insights (Table 1). These
insights pointed to a clear preference for short auditory feedback during the
intensive movements of routines and specific visual feedback or explanations
after the movement has concluded. These distinctions underscore the need for
interactive systems that offer feedback adaptable to the physical nature of
dance, its timing, and the different learning stages dancers progress through.
Additionally, we identified it is important for interactive systems to adjust
their feedback to suit both the immediate and reflective dance learning
phases. Furthermore, our research unearthed insights related to group
dynamics, highlighting the intricate balance necessary in offering
personalized feedback that supports individual development while maintaining
group cohesion and avoiding discomfort among dancers.

Our findings indicate that designing interactive dance systems requires
sensitivity to each group’s distinct cultural and organizational attributes.
Cheerleaders in our study preferred avoiding public correction, stemming from
the stringent, synchronized nature of their routines within a hierarchical
structure. In contrast, systems like ZoombaTogether allow for public yet
anonymous feedback, with studies showing that participants are motivated by
the understanding that they are not alone in their challenges, rather than
feeling singled out (Zhu et al., 2023). This reflects Kurt Lewin’s field
theory (Lewin, 1942), which suggests behavior results from personal and
environmental factors. Thus, future interactive dance systems should be
versatile, reflecting group dance’s diverse cultural and organizational
settings.

Our research contributes to HCI by shedding light on a previously unexplored
domain. We highlight the critical role of context in discerning user needs and
illustrate how domain-specific insights can steer the development of systems
that bolster group dance practice. However, we must also acknowledge the
limitations of our work: we concentrated on small group dance practice during
cheerleading, a genre characterized by amateur performers, hierarchical social
structures, and synchronized, intense movements. While this focus was apt for
our investigation, future research should adopt requirements for different
contexts, such as larger groups or varying group atmospheres and
organizational cultures, which may alter user needs substantially. More
comprehensive research using methods that grasp the dynamic nature of group
interaction is necessary to accurately tailor interactive system designs to
the varied landscapes of group dance practice.

###  7.2. Expanding and Evaluating the Design Space

We sought to expand and understand the design space for interactive systems to
support group dance learning through two methods. Our technology probe
workshop highlighted the technical challenges and considerations of using RGB
cameras for capturing poses during group dance practice, particularly
regarding their portability and suitability for real-world applications.
Contrary to prior research on augmented mirrors for improvisational dance(Zhou
et al., 2023), issues such as delay and poor depth perception were identified
as disruptive in structured group dance practice. Delays in feedback were
reported to disrupt the learning experience, while a single RGB camera’s
limited depth perception impairs accurate movement evaluation, which is
crucial in synchronized dance performance. Additionally, to mitigate occlusion
issues among dancers—a factor less considered in traditional vision-based
systems—the spatial arrangement of camera fields of view needs careful
planning to ensure all dancers are clearly in sight. It is insufficient to
simply consider camera location, as occlusions can occur in quite complex
patterns. Solutions that address these practical challenges will enhance
system tracking capabilities and preserve the integrity of group formations,
improvements that will make a significant step towards effectively integrating
RGB cameras in group dance settings.

In contrast, our speed dating workshop provided nuanced insights into user
experiences with our 15 storyboards detailing concepts for interactive systems
for group dance practice. The feedback mechanisms elicited mixed reactions,
with visual cues being distracting for some, yet others saw potential benefits
if these cues were subtly applied. This underscores the need for a design
approach that carefully tailors feedback to support rather than interrupt the
dance experience. Furthermore, the discussions on the timing and modality of
feedback highlighted a preference for preventive feedback, offering guidance
just before complex movements. This approach to feedback, which is relatively
under-explored in dance HCI research, is a key area for future exploration. We
recommend that future system designs aim to include content that prepares and
guides dancers for upcoming movements.

The perceptions of quantitative score-based feedback elicited critical
reflections on trust and group dynamics. While the accurate, individual
objective performance measurement enhanced the system’s trustworthiness, it
was also reported as potentially problematic and demotivating. It was viewed
as introducing a competitive element to the practice that was misaligned with
the collective goal of synchronized movement inherent in cheerleading.
However, the social comparisons that arise from dance cheerleading’s explicit,
objective goals and quantitative performance metrics cannot simply be
explained by traditional social comparison theory (Festinger, 1954). Rather,
the feedback highlighted concerns over creating an unnecessary competitive
hierarchy that could detract from the group’s cohesion and the shared goal of
synchronicity. This was particularly evident in responses indicative of upward
social comparison, where lower scores made participants feel they were
detracting from the group’s collective effort. This sentiment reflected the
high cohesion and collectivist orientation of the groups we studied and their
emphasis on the importance of unity in achieving synchronized movements.

The transition in participant attitudes towards public versus private feedback
throughout the study was striking. During the initial formative study phase,
participants preferred privacy in feedback to avoid psychological pressure.
However, in the speed dating workshop, opinions shifted towards favoring
public feedback to increase the efficiency of practice sessions and group
cohesion. This significant shift in feedback preference from private to public
can be attributed to an enhanced understanding of how public feedback could
streamline the practice process, reinforcing group cohesion and facilitate a
more collaborative learning environment (Zajonc, 1965; Cottrell et al., 1968).
This evolution in preferences underscores the importance of adaptable feedback
mechanisms that cater to the dynamic nature of group cohesion and learning
needs over time.

Participants also highlighted the positive impact of systems that promote
group reflection and cooperation. In the highly synchronized context of dance
cheerleading, the collective goal is paramount, rendering individual
competition irrelevant. Systems like sound-based group reflection were
appreciated for enhancing group cohesion by encouraging collective effort
toward achieving synchronized movements. This feedback underscores the
importance of designing systems that provide corrective feedback and promote
group reflection, ensuring that technological interventions align with the
collective objectives and supportive dynamics of group dance practice.

###  7.3. Design Implications

Our study’s foray into interactive systems for group dance highlighted the
utility of RGB cameras for capturing poses: they are highly portable and
suitable for real-world settings. Yet, this approach unveiled challenges, such
as managing user expectations and acquainting them with new technologies. This
underscores the importance of an initialization session to set realistic
expectations about the system’s capabilities. Furthermore, it points to the
need to explore alternative pose-capturing technologies that offer portability
while addressing the limitations of vision-based systems. For instance, the
VoLearn system uses phone IMUs for measuring body movements and employs
Dynamic Time Warping (DTW) for pose evaluation (Xia et al., 2022, 2021),
offering a solution to vision-based systems’ occlusion issues.

Regarding group dynamics, our research highlighted the critical role of
feedback in influencing group dynamics, fostering either competition or
cooperation. We did not initially consider ”anonymity” as a design dimension,
suggesting future exploration in this area is needed. Anonymity in feedback
mechanisms, exemplified by systems like ZoombaTogether(Zhu et al., 2023),
could reduce competitive tensions, promoting a supportive practice
environment. This introduces a potential design direction in the form of
systems that maintain motivational feedback without fostering competition.
Additionally, the concept of feedback that encourages group reflection rather
than individual correction represents an area with significant potential for
future HCI research that focuses on enhancing cooperative dynamics. According
to collaborative learning theory, which posits that learners engage in a
social and interactive process to construct knowledge through discourse and
collaboration within a community (Dillenbourg, 1999), such group-centric
feedback mechanisms could effectively support this process. The relationship
between technology, students, and dance studio instructors also warrants
further investigation to ensure future novel interactive systems genuinely
enhance the existing learning experiences.

The application of interactive systems across various group dance contexts
underscores the necessity for adaptability to meet specific genre
requirements. For example, our sound-generating system designed for group
reflection, as opposed to its utilization in improvisational settings like
CO/DA (Françoise et al., 2022), exemplifies the varied potential applications
of these systems in dance. This flexibility is crucial, highlighting the
importance of crafting systems catering to differing goals—from evaluation and
motivation to enhancing practice. The varied feedback preferences of
participants, influenced by their practice contexts, suggest a significant
need for adaptive systems such as adapt2learn (Turakhia et al., 2021). These
systems could leverage educational principles like scaffolding and fading to
dynamically adjust students’ levels dynamically, further enriching learning
experiences (Puntambekar and Hubscher, 2005). Exploring suitable feedback
modalities (Chauvigné et al., 2019; Trajkova and Cafaro, 2018) and dynamics in
large groups, particularly for dances that require precise coordination, opens
an expansive field for research. Developing systems to guide and synchronize
movements in extensive group practice could profoundly enhance both the
effectiveness and enjoyment of group dance activities, thereby expanding the
reach and impact of interactive dance systems in creating supportive,
engaging, and rewarding experiences.

##  8\. Limitations & Future Work

One of the limitations of our approach is the expansion of the design space
without the full development and deployment of a comprehensive system
prototype. While technology probes provided valuable insights into potential
system features and user experience in practice, they did not constitute a
fully realized system capable of extensive testing in diverse group dance
settings. Additionally, our participant sample was limited to cheerleading
teams from a single university. This meant we focused on synchronized dance,
intensive movement, and amateur-level practice in a hierarchical
organizational culture. While our findings can apply to group dance genres
with similar characteristics, further research is needed to understand the
requirements and dynamics of group dance practices that do not share these
attributes.

Future work could involve developing a comprehensive interactive system for
group dance practice based on the insights gained and design concepts
validated through our formative research and technology probes. This next
phase would include detailed system development and extensive field
deployments to rigorously test the system’s effectiveness, scalability, and
adaptability to various group dance contexts beyond amateur cheerleading
practices. Long-term impact studies could be conducted to investigate the
effects of interactive systems on group dynamics, learning motivation, and
skill acquisition over time. Furthermore, developing adaptive feedback
mechanisms that can tailor feedback based on the evolving needs and skill
levels of individual dancers and the group could be explored to enhance the
learning experience further. Lastly, future work could delve deeper into the
pedagogical aspects of integrating interactive technologies into group dance
practice, collaborating with dance instructors and educators to develop
teaching strategies and methodologies that leverage these systems for a more
holistic approach to technology-enhanced dance learning.

##  9\. Conclusion

This paper expands on the design space of interactive systems for group dance
practice by conducting user-centered studies with amateur cheerleading
troupes. We identified user requirements related to feedback dynamics in group
dance practice through observations and interviews. Building on these
insights, we generated 15 storyboards of novel concepts in an ideation
workshop and quickly evaluated the potential user experiences with these
prospective systems using technology probes and speed-dating workshops. To
ensure the practical applicability of our research, we envisioned a vision-
based system that could be used in real-world group dance practice,
prioritizing portability for amateur settings. Our findings contribute to HCI
by highlighting the unique aspects and needs of group dance activities, which
have been largely overlooked in previous research focusing on individual dance
learning.

Our work offers valuable insights into designing interactive systems that
cater to the complexities of group dynamics, synchronization, and collective
learning in dance. By expanding the design dimensions to include feedback
timing, privacy, and simultaneity, we provide a foundation for future research
and development of technologies that enhance the group dance experience.
Furthermore, our findings emphasize the importance of considering the complex
group dynamics and contexts within group dance practices when designing
interactive systems. Our study can inspire the further exploration of
interactive systems in diverse group dance contexts, ultimately leading to
more engaging, effective, and rewarding practices for dancers at all levels.

###### Acknowledgements.

The authors deeply appreciate the UNICH, UNIST Cheerleading Crew, for
participating in this study and Eunyong and Yonghwan for valuable discussions.
This research was partially supported by a grant from the Korea Institute for
Advancement of Technology (KIAT) funded by the Government of Korea (MOTIE)
(P0025495, Establishment of Infrastructure for Integrated Utilization of
Design Industry Data). This work was also partially supported by the
Technology Innovation Program (20015056, Commercialization design and
development of Intelligent Product-Service System for personalized full silver
life cycle care) funded By the Ministry of Trade, Industry & Energy(MOTIE,
Korea).

## References

  * (1)
  * Alexiadis et al. (2011) Dimitrios S. Alexiadis, Philip Kelly, Petros Daras, Noel E. O’Connor, Tamy Boubekeur, and Maher Ben Moussa. 2011.  Evaluating a dancer’s performance using kinect-based skeleton tracking. In _Proceedings of the 19th ACM international conference on Multimedia_ _(MM ’11)_. Association for Computing Machinery, New York, NY, USA, 659–662.  https://doi.org/10.1145/2072298.2072412
  * Anderson et al. (2013) Fraser Anderson, Tovi Grossman, Justin Matejka, and George Fitzmaurice. 2013.  YouMove: enhancing movement training with an augmented reality mirror. In _Proceedings of the 26th annual ACM symposium on User interface software and technology_ _(UIST ’13)_. Association for Computing Machinery, New York, NY, USA, 311–320.  https://doi.org/10.1145/2501988.2502045
  * Aristidou et al. (2015) Andreas Aristidou, Efstathios Stavrakis, Panayiotis Charalambous, Yiorgos Chrysanthou, and Stephania Loizidou Himona. 2015.  Folk Dance Evaluation Using Laban Movement Analysis.  _J. Comput. Cult. Herit._ 8, 4, Article 20 (aug 2015), 19 pages.  https://doi.org/10.1145/2755566
  * Aristidou et al. (2014) Andreas Aristidou, Efstathios Stavrakis, and Yiorgos Chrysanthou. 2014.  Motion Analysis for Folk Dance Evaluation.. In _GCH_. Darmstadt, 55–64.
  * Bakogianni et al. (2007) Sophia Bakogianni, Evangelia Kavakli, Vicky Karkou, and Maroussa Tsakogianni. 2007.  Teaching Traditional Dance using E-learning tools: Experience from the.  (2007).
  * Barnett (2006) Lynn A. Barnett. 2006.  Flying High or Crashing Down: Girls’ Accounts of Trying Out for Cheerleading and Dance.  _Journal of Adolescent Research_ 21, 5 (2006), 514–541.  https://doi.org/10.1177/0743558406291687 arXiv:https://doi.org/10.1177/0743558406291687
  * Bazarevsky et al. (2020) Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, and Matthias Grundmann. 2020.  BlazePose: On-device Real-time Body Pose tracking.  https://doi.org/10.48550/arXiv.2006.10204 arXiv:2006.10204 [cs].
  * Blanchet and Han (2023) Julien Blanchet and Sixuan Han. 2023.  Integrating a LLM into an Automatic Dance Practice Support System: Breathing Life Into The Virtual Coach. In _Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology_ (¡conf-loc¿, ¡city¿San Francisco¡/city¿, ¡state¿CA¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) _(UIST ’23 Adjunct)_. Association for Computing Machinery, New York, NY, USA, Article 118, 2 pages.  https://doi.org/10.1145/3586182.3625119
  * Braun and Clarke (2006) Virginia Braun and Victoria Clarke. 2006.  Using thematic analysis in psychology.  _Qualitative Research in Psychology_ 3, 2 (Jan. 2006), 77–101.  https://doi.org/10.1191/1478088706qp063oa Publisher: Routledge _eprint: https://www.tandfonline.com/doi/pdf/10.1191/1478088706qp063oa.
  * Buckroyd (2000) Julia Buckroyd. 2000.  Group dynamic.  https://www.communitydance.org.uk/DB/animated-library/group-dynamic?ed=14052
  * Camurri et al. (2016a) Antonio Camurri, Corrado Canepa, Nicola Ferrari, Maurizio Mancini, Radoslaw Niewiadomski, Stefano Piana, Gualtiero Volpe, Jean-Marc Matos, Pablo Palacio, and Muriel Romero. 2016a.  A system to support the learning of movement qualities in dance: a case study on dynamic symmetry. In _Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct_ (Heidelberg, Germany) _(UbiComp ’16)_. Association for Computing Machinery, New York, NY, USA, 973–976.  https://doi.org/10.1145/2968219.2968261
  * Camurri et al. (2016b) Antonio Camurri, Corrado Canepa, Nicola Ferrari, Maurizio Mancini, Radoslaw Niewiadomski, Stefano Piana, Gualtiero Volpe, Jean-Marc Matos, Pablo Palacio, and Muriel Romero. 2016b.  A system to support the learning of movement qualities in dance: a case study on dynamic symmetry. In _Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct_ _(UbiComp ’16)_. Association for Computing Machinery, New York, NY, USA, 973–976.  https://doi.org/10.1145/2968219.2968261
  * Chan et al. (2011) Jacky C.P. Chan, Howard Leung, Jeff K.T. Tang, and Taku Komura. 2011.  A Virtual Reality Dance Training System Using Motion Capture Technology.  _IEEE Transactions on Learning Technologies_ 4, 2 (April 2011), 187–195.  https://doi.org/10.1109/TLT.2010.27 Conference Name: IEEE Transactions on Learning Technologies.
  * Chauvigné et al. (2019) Léa A.S. Chauvigné, Ashley Walton, Michael J. Richardson, and Steven Brown. 2019.  Multi-person and multisensory synchronization during group dancing.  _Human Movement Science_ 63 (2019), 199–208.  https://doi.org/10.1016/j.humov.2018.12.005
  * Cirelli (2018) Laura K Cirelli. 2018.  How interpersonal synchrony facilitates early prosocial behavior.  _Current Opinion in Psychology_ 20 (2018), 35–39.  https://doi.org/10.1016/j.copsyc.2017.08.009 Early Development of prosocial behavior.
  * Cottrell et al. (1968) Nickolas B Cottrell, Dennis L Wack, Gary J Sekerak, and Robert H Rittle. 1968.  Social facilitation of dominant responses by the presence of an audience and the mere presence of others.  _Journal of personality and social psychology_ 9, 3 (1968), 245.
  * Davidoff et al. (2007) Scott Davidoff, Min Kyung Lee, Anind K Dey, and John Zimmerman. 2007.  Rapidly exploring application design through speed dating. In _UbiComp 2007: Ubiquitous Computing: 9th International Conference, UbiComp 2007, Innsbruck, Austria, September 16-19, 2007. Proceedings 9_. Springer, 429–446.
  * de Las Heras-Fernández et al. (2023) Rosa de Las Heras-Fernández, Guillermo Mendoza, and Manuel Jimenez. 2023.  Psychological stress responses to a live performance by professional flamenco dancers.  _PeerJ_ 11 (2023), e15282.  https://doi.org/10.7717/peerj.15282
  * Deng et al. (2022a) Chenliang Deng, Qiaoyan Yu, Ganglin Luo, and Shuiping Lu. 2022a.  Effects of 16 Weeks of Cheerleading on Physical Self-Esteem and Mental Health of Female College Students.  _Front Psychol_ 13 (June 2022), 925162.
  * Deng et al. (2022b) Chenliang Deng, Qiaoyan Yu, Ganglin Luo, and Shuiping Lu. 2022b.  Effects of 16 weeks of cheerleading on physical self-esteem and mental health of female college students.  _Frontiers in psychology_ 13 (2022), 925162.  https://doi.org/10.3389/fpsyg.2022.925162
  * Dillenbourg (1999) Pierre Dillenbourg. 1999.  What do you mean by collaborative learning?  _Collaborative-learning: Cognitive and Computational Approaches._ (1999), 1–19.
  * Essid et al. (2013) Slim Essid, Xinyu Lin, Marc Gowing, Georgios Kordelas, Anil Aksay, Philip Kelly, Thomas Fillon, Qianni Zhang, Alfred Dielmann, Vlado Kitanovski, et al. 2013\.  A multi-modal dance corpus for research into interaction between humans in virtual environments.  _Journal on Multimodal User Interfaces_ 7 (2013), 157–170.  https://doi.org/10.1007/s12193-012-0109-5
  * Etzinger (2023) Morgan Etzinger. 2023.  Dance and the Self: An analysis on the psychological impacts of dance.  (2023).
  * Ferguson et al. (2014) Sam Ferguson, Emery Schubert, and Catherine J. Stevens. 2014.  Dynamic dance warping: Using dynamic time warping to compare dance movement performed under different conditions. In _Proceedings of the 2014 International Workshop on Movement and Computing_ _(MOCO ’14)_. Association for Computing Machinery, New York, NY, USA, 94–99.  https://doi.org/10.1145/2617995.2618012
  * Festinger (1954) Leon Festinger. 1954.  A theory of social comparison processes.  _Human relations_ 7, 2 (1954), 117–140.  https://doi.org/10.1177/001872675400700202
  * Françoise et al. (2022) Jules Françoise, Sarah Fdili Alaoui, and Yves Candau. 2022.  CO/DA: Live-Coding Movement-Sound Interactions for Dance Improvisation. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3491102.3501916
  * Fujimoto et al. (2012) Minoru Fujimoto, Masahiko Tsukamoto, and Tsutomu Terada. 2012.  A dance training system that maps self-images onto an instruction video. In _The Fifth International Conference on Advances in Computer-Human Interactions_. 309–314.
  * Gibbons (2004) Elizabeth Gibbons. 2004.  Feedback in the Dance Studio.  _Journal of Physical Education, Recreation & Dance_ 75, 7 (Sept. 2004), 38–43.  https://doi.org/10.1080/07303084.2004.10607273
  * Guo et al. (2021) Hong Guo, Shanchen Zou, Chuying Lai, and Hongxin Zhang. 2021.  PhyCoVIS: A visual analytic tool of physical coordination for cheer and dance training.  _Computer Animation and Virtual Worlds_ 32, 1 (2021), e1975.  https://doi.org/10.1002/cav.1975 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cav.1975.
  * Guo et al. (2022) Hong Guo, ShanChen Zou, YiLin Xu, Han Yang, Jian Wang, HongXin Zhang, and Wei Chen. 2022.  DanceVis: toward better understanding of online cheer and dance training.  _Journal of Visualization_ 25, 1 (Feb. 2022), 159–174.  https://doi.org/10.1007/s12650-021-00783-x
  * Hsieh and Shannon (2005) Hsiu-Fang Hsieh and Sarah E. Shannon. 2005.  Three Approaches to Qualitative Content Analysis.  _Qualitative Health Research_ 15, 9 (Nov. 2005), 1277–1288.  https://doi.org/10.1177/1049732305276687 Publisher: SAGE Publications Inc.
  * Hutchinson et al. (2003) Hilary Hutchinson, Wendy Mackay, Bo Westerlund, Benjamin B. Bederson, Allison Druin, Catherine Plaisant, Michel Beaudouin-Lafon, Stéphane Conversy, Helen Evans, Heiko Hansen, Nicolas Roussel, and Björn Eiderbäck. 2003.  Technology probes: inspiring design for and with families. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ _(CHI ’03)_. Association for Computing Machinery, New York, NY, USA, 17–24.  https://doi.org/10.1145/642611.642616
  * IxDF (2016) Interaction Design Foundation IxDF. 2016.  What are User Scenarios?  https://www.interaction-design.org/literature/topics/user-scenarios
  * Joseph and Southcott (2019) Dawn Joseph and Jane Southcott. 2019.  Meanings of leisure for older people: an Australian study of line dancing.  _Leisure Studies_ 38, 1 (2019), 74–87.  https://doi.org/10.1080/02614367.2018.1544655 arXiv:https://doi.org/10.1080/02614367.2018.1544655
  * Kang et al. (2023) Jiwon Kang, Chaewon Kang, Jeewoo Yoon, Houggeun Ji, Taihu Li, Hyunmi Moon, Minsam Ko, and Jinyoung Han. 2023.  Dancing on the inside: A qualitative study on online dance learning with teacher-AI cooperation.  _Education and Information Technologies_ (March 2023).  https://doi.org/10.1007/s10639-023-11649-0
  * Kim et al. (2023) Na-Gyeong Kim, Jin-Kook Kim, and Sue-Yong Shin. 2023.  A Study on the Development Direction of Action Cheerleading in Korea.  _orean Journal of Sports Science_ 32, 5 (2023), 29–49.  https://doi.org/10.35159/kjss.2023.10.32.5.29
  * Kim and Kim (2018) Yeonho Kim and Daijin Kim. 2018.  Real-time dance evaluation by markerless human pose estimation.  _Multimedia Tools and Applications_ 77, 23 (Dec. 2018), 31199–31220.  https://doi.org/10.1007/s11042-018-6068-4
  * Kitsikidis et al. (2014) Alexandros Kitsikidis, Kosmas Dimitropoulos, Erdal Yilmaz, Stella Douka, and Nikos Grammalidis. 2014.  Multi-sensor technology and fuzzy logic for dancer’s motion analysis and performance evaluation within a 3D virtual environment. In _Universal Access in Human-Computer Interaction. Design and Development Methods for Universal Access: 8th International Conference, UAHCI 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part I 8_. Springer, 379–390.
  * Kyan et al. (2015) Matthew Kyan, Guoyu Sun, Haiyan Li, Ling Zhong, Paisarn Muneesawang, Nan Dong, Bruce Elder, and Ling Guan. 2015.  An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment.  _ACM Transactions on Intelligent Systems and Technology_ 6, 2 (March 2015), 23:1–23:37.  https://doi.org/10.1145/2735951
  * Lee et al. (2020) Jae-Jun Lee, Jong-Hyeok Choi, Tserenpurev Chuluunsaikhan, and Aziz Nasridinov. 2020.  Pose evaluation for dance learning application using joint position and angular similarity. In _Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers_ _(UbiComp-ISWC ’20)_. Association for Computing Machinery, New York, NY, USA, 67–70.  https://doi.org/10.1145/3410530.3414402
  * Lee and Lee (2023) Soohwan Lee and Kyungho Lee. 2023.  CheerUp: A Real-time Ambient Visualization of Cheerleading Pose Similarity. In _Companion Proceedings of the 28th International Conference on Intelligent User Interfaces_ _(IUI ’23 Companion)_. Association for Computing Machinery, New York, NY, USA, 72–74.  https://doi.org/10.1145/3581754.3584135
  * Lesko (1988) Nancy Lesko. 1988.  “We’re Leading America”: The Changing Organization and Form of High School Cheerleading.  _Theory & Research in Social Education_ 16, 4 (1988), 263–278.  https://doi.org/10.1080/00933104.1988.10505570 arXiv:https://doi.org/10.1080/00933104.1988.10505570
  * Lewin (1942) Kurt Lewin. 1942.  Field theory and learning.  (1942).
  * Lugaresi et al. (2019) Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. 2019.  MediaPipe: A Framework for Building Perception Pipelines.  https://doi.org/10.48550/arXiv.1906.08172 arXiv:1906.08172 [cs].
  * Magnenat-Thalmann et al. (2008) Nadia Magnenat-Thalmann, Dimitrios Protopsaltou, and Evangelia Kavakli. 2008.  Learning how to dance using a web 3D platform. In _Advances in Web Based Learning–ICWL 2007: 6th International Conference Edinburgh, UK, August 15-17, 2007 Revised Papers 6_. Springer, 1–12.
  * Marquardt et al. (2012) Zoe Marquardt, João Beira, Natalia Em, Isabel Paiva, and Sebastian Kox. 2012.  Super Mirror: a kinect interface for ballet dancers. In _CHI ’12 Extended Abstracts on Human Factors in Computing Systems_ (Austin, Texas, USA) _(CHI EA ’12)_. Association for Computing Machinery, New York, NY, USA, 1619–1624.  https://doi.org/10.1145/2212776.2223682
  * Martinez Plasencia et al. (2014) Diego Martinez Plasencia, Florent Berthaut, Abhijit Karnik, and Sriram Subramanian. 2014.  Through the combining glass. In _Proceedings of the 27th annual ACM symposium on User interface software and technology_ _(UIST ’14)_. Association for Computing Machinery, New York, NY, USA, 341–350.  https://doi.org/10.1145/2642918.2647351
  * Molina-Tanco et al. (2017) Luis Molina-Tanco, Carmen García-Berdonés, and Arcadio Reyes-Lecuona. 2017.  The Delay Mirror: a Technological Innovation Specific to the Dance Studio. In _Proceedings of the 4th International Conference on Movement Computing_ (London, United Kingdom) _(MOCO ’17)_. Association for Computing Machinery, New York, NY, USA, Article 9, 6 pages.  https://doi.org/10.1145/3077981.3078033
  * Moritz (2011) Amy Moritz. 2011.  Cheerleading: not just for the sidelines anymore.  _Sport in Society_ 14, 5 (2011), 660–669.  https://doi.org/10.1080/17430437.2011.575109 arXiv:https://doi.org/10.1080/17430437.2011.575109
  * Pristaš et al. (2016) Nikolina Pristaš, Goran Sergej Pristaš, and Tomislav Medak. 2016.  BADco. and Daniel Turing: Whatever Dance Toolbox.  In _Transmission in Motion_. Routledge, 142–151.
  * Pritchard et al. (1988) Robert D Pritchard, Steven D Jones, Philip L Roth, Karla K Stuebing, and Steven E Ekeberg. 1988.  Effects of group feedback, goal setting, and incentives on organizational productivity.  _Journal of applied psychology_ 73, 2 (1988), 337.  https://doi.org/10.1037/0021-9010.73.2.337
  * Puntambekar and Hubscher (2005) Sadhana Puntambekar and Roland Hubscher. 2005.  Tools for Scaffolding Students in a Complex Learning Environment: What Have We Gained and What Have We Missed?  _Educational Psychologist_ 40, 1 (2005), 1–12.
  * Raheb et al. (2016) K. El Raheb, A. Katifori, and Y. Ioannidis. 2016.  HCI challenges in Dance Education.  _EAI Endorsed Transactions on Ambient Systems_ ”3”, 9 (Aug. 2016).  https://eudl.eu/doi/10.4108/eai.23-8-2016.151642
  * Raheb et al. (2019) Katerina El Raheb, Marina Stergiou, Akrivi Katifori, and Yannis Ioannidis. 2019.  Dance Interactive Learning Systems: A Study on Interaction Workflow and Teaching Approaches.  _Comput. Surveys_ 52, 3 (June 2019), 50:1–50:37.  https://doi.org/10.1145/3323335
  * Raheb et al. (2018) Katerina El Raheb, George Tsampounaris, Akrivi Katifori, and Yannis Ioannidis. 2018.  Choreomorphy: a whole-body interaction experience for dance improvisation and visual experimentation. In _Proceedings of the 2018 International Conference on Advanced Visual Interfaces_ (Castiglione della Pescaia, Grosseto, Italy) _(AVI ’18)_. Association for Computing Machinery, New York, NY, USA, Article 27, 9 pages.  https://doi.org/10.1145/3206505.3206507
  * Ruiz (2016) Rebecca R. Ruiz. 2016.  Cheerleading and Muay Thai Given Provisional Olympic Status.  _The New York Times_ (2016), 14.
  * Sachs (1963) Curt Sachs. 1963.  World history of the dance.  (1963).
  * Schwender et al. (2018) Tina M Schwender, Sarah Spengler, Christina Oedl, and Filip Mess. 2018.  Effects of dance interventions on aspects of the participants’ self: a systematic review.  _Frontiers in psychology_ 9 (2018), 1130.  https://doi.org/10.3389/fpsyg.2018.01130
  * Sefelin et al. (2003) Reinhard Sefelin, Manfred Tscheligi, and Verena Giller. 2003.  Paper prototyping - what is it good for? a comparison of paper- and computer-based low-fidelity prototyping. In _CHI ’03 Extended Abstracts on Human Factors in Computing Systems_ (Ft. Lauderdale, Florida, USA) _(CHI EA ’03)_. Association for Computing Machinery, New York, NY, USA, 778–779.  https://doi.org/10.1145/765891.765986
  * Thomas et al. (2004) David Q Thomas, Jeffrey G Seegmiller, Tara L Cook, and Beth A Young. 2004.  PHYSIOLOGIC PROFILE OF THE FITNESS STATUS OF COLLEGIATE CHEERLEADERS.  _The Journal of Strength & Conditioning Research_ 18, 2 (2004).
  * Trajkova and Cafaro (2016) Milka Trajkova and Francesco Cafaro. 2016.  E-ballet: designing for remote ballet learning. In _Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct_ _(UbiComp ’16)_. Association for Computing Machinery, New York, NY, USA, 213–216.  https://doi.org/10.1145/2968219.2971442
  * Trajkova and Cafaro (2018) Milka Trajkova and Francesco Cafaro. 2018.  Takes Tutu to Ballet: Designing Visual and Verbal Feedback for Augmented Mirrors.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 2, 1 (2018), 38:1–38:30.  https://doi.org/10.1145/3191770
  * Trajkova et al. (2019) Milka Trajkova, Francesco Cafaro, and Lynn Dombrowski. 2019.  Designing for Ballet Classes: Identifying and Mitigating Communication Challenges Between Dancers and Teachers. In _Proceedings of the 2019 on Designing Interactive Systems Conference_ _(DIS ’19)_. Association for Computing Machinery, New York, NY, USA, 265–277.  https://doi.org/10.1145/3322276.3322312
  * Tsampounaris et al. (2016) Georgios Tsampounaris, Katerina El Raheb, Vivi Katifori, and Yannis Ioannidis. 2016.  Exploring Visualizations in Real-time Motion Capture for Dance Education. In _Proceedings of the 20th Pan-Hellenic Conference on Informatics_ (Patras, Greece) _(PCI ’16)_. Association for Computing Machinery, New York, NY, USA, Article 76, 6 pages.  https://doi.org/10.1145/3003733.3003811
  * Turakhia et al. (2021) Dishita G Turakhia, Andrew Wong, Yini Qi, Lotta-Gili Blumberg, Yoonji Kim, and Stefanie Mueller. 2021.  Adapt2Learn: A Toolkit for Configuring the Learning Algorithm for Adaptive Physical Tools for Motor-Skill Learning. In _Proceedings of the 2021 ACM Designing Interactive Systems Conference_ (Virtual Event, USA) _(DIS ’21)_. Association for Computing Machinery, New York, NY, USA, 1301–1312.  https://doi.org/10.1145/3461778.3462128
  * Usui et al. (2015a) Yoko Usui, Katsumi Sato, and Shinichi Watabe. 2015a.  Learning Hawaiian hula dance by using tablet computer. In _SIGGRAPH Asia 2015 Symposium on Education_ _(SA ’15)_. Association for Computing Machinery, New York, NY, USA, 1–2.  https://doi.org/10.1145/2818498.2818507
  * Usui et al. (2015b) Yoko Usui, Katsumi Sato, and Shinichi Watabe. 2015b.  Learning Hawaiian hula dance by using tablet computer. In _SIGGRAPH Asia 2015 Symposium on Education_ (Kobe, Japan) _(SA ’15)_. Association for Computing Machinery, New York, NY, USA, Article 6, 2 pages.  https://doi.org/10.1145/2818498.2818507
  * Vicky Karkou and Kavakli (2008) Sophia Bakogianni Vicky Karkou and Evangelia Kavakli. 2008.  Traditional dance, pedagogy and technology: an overview of the WebDANCE project.  _Research in Dance Education_ 9, 2 (2008), 163–186.  https://doi.org/10.1080/14647890802087985
  * Weldon and Weingart (1993) Elizabeth Weldon and Laurie R Weingart. 1993.  Group goals and group performance.  _British Journal of Social Psychology_ 32, 4 (1993), 307–334.  https://doi.org/10.1111/j.2044-8309.1993.tb01003.x
  * Xia et al. (2022) Chengshuo Xia, Xinrui Fang, Riku Arakawa, and Yuta Sugiura. 2022.  VoLearn: A Cross-Modal Operable Motion-Learning System Combined with Virtual Avatar and Auditory Feedback.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 6, 2 (July 2022), 81:1–81:26.  https://doi.org/10.1145/3534576
  * Xia et al. (2021) Chengshuo Xia, Xinrui Fang, and Yuta Sugiura. 2021.  VoLearn: An Operable Motor Learning System with Auditory Feedback. In _Adjunct Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’21 Adjunct)_. Association for Computing Machinery, New York, NY, USA, 103–105.  https://doi.org/10.1145/3474349.3480186
  * Xu et al. (2018) Weipeng Xu, Avishek Chatterjee, Michael Zollhöfer, Helge Rhodin, Dushyant Mehta, Hans-Peter Seidel, and Christian Theobalt. 2018.  MonoPerfCap: Human Performance Capture From Monocular Video.  _ACM Transactions on Graphics_ 37, 2 (May 2018), 27:1–27:15.  https://doi.org/10.1145/3181973
  * Zajonc (1965) Robert B Zajonc. 1965.  Social Facilitation: A solution is suggested for an old unresolved social psychological problem.  _Science_ 149, 3681 (1965), 269–274.
  * Zhang et al. (2019) Yongqi Zhang, Biao Xie, Haikun Huang, Elisa Ogawa, Tongjian You, and Lap-Fai Yu. 2019.  Pose-Guided Level Design. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3290605.3300784
  * Zhou et al. (2021a) Qiushi Zhou, Cheng Cheng Chua, Jarrod Knibbe, Jorge Goncalves, and Eduardo Velloso. 2021a.  Dance and Choreography in HCI: A Two-Decade Retrospective. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–14.  https://doi.org/10.1145/3411764.3445804
  * Zhou et al. (2023) Qiushi Zhou, Louise Grebel, Andrew Irlitti, Julie Ann Minaai, Jorge Goncalves, and Eduardo Velloso. 2023.  Here and Now: Creating Improvisational Dance Movements with a Mixed Reality Mirror. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, 1–16.  https://doi.org/10.1145/3544548.3580666
  * Zhou et al. (2022) Qiushi Zhou, Andrew Irlitti, Difeng Yu, Jorge Goncalves, and Eduardo Velloso. 2022.  Movement Guidance using a Mixed Reality Mirror. In _Proceedings of the 2022 ACM Designing Interactive Systems Conference_ _(DIS ’22)_. Association for Computing Machinery, New York, NY, USA, 821–834.  https://doi.org/10.1145/3532106.3533466
  * Zhou et al. (2019) Zhongyi Zhou, Yuki Tsubouchi, and Koji Yatani. 2019.  Visualizing Out-of-synchronization in Group Dancing. In _Adjunct Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’19 Adjunct)_. Association for Computing Machinery, New York, NY, USA, 107–109.  https://doi.org/10.1145/3332167.3356888
  * Zhou et al. (2021b) Zhongyi Zhou, Anran Xu, and Koji Yatani. 2021b.  SyncUp: Vision-based Practice Support for Synchronized Dancing.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 5, 3 (2021), 143:1–143:25.  https://doi.org/10.1145/3478120
  * Zhu et al. (2023) Qingxiaoyang Zhu, Xiaoyu Zhang, Shuyan Dai, Noriko Satake, and Hao-Chuan Wang. 2023.  ZoombaTogether: A Video Conference Add-on for Generating Interactive Visual Feedback for Online Group Exercise Through On-The-Fly Pose Tracking. In _Companion Publication of the 2023 ACM Designing Interactive Systems Conference_ _(DIS ’23 Companion)_. Association for Computing Machinery, New York, NY, USA, 261–265.  https://doi.org/10.1145/3563703.3596653
  * Zimmerman and Forlizzi (2017) John Zimmerman and Jodi Forlizzi. 2017.  Speed Dating: Providing a Menu of Possible Futures.  _She Ji: The Journal of Design, Economics, and Innovation_ 3, 1 (March 2017), 30–50.  https://doi.org/10.1016/j.sheji.2017.08.003