#  _The Cadaver in the Machine_ : The Social Practices of Measurement and
Validation in Motion Capture Technology

Emma Harvey  Cornell UniversityIthacaNew YorkUSA<EMAIL_ADDRESS>,  Hauke
Sandhaus  Cornell UniversityNew YorkNew YorkUSA ,  Abigail Z. Jacobs
University of MichiganAnn ArborMichiganUSA ,  Emanuel Moss  IntelNew YorkNew
YorkUSA and  Mona Sloane  University of VirginiaCharlottesvilleVirginiaUSA

(2024)

###### Abstract.

Motion capture systems, used across various domains, make body representations
concrete through technical processes. We argue that the measurement of bodies
and the validation of measurements for motion capture systems can be
understood as social practices. By analyzing the findings of a systematic
literature review (N=278) through the lens of social practice theory, we show
how these practices, and their varying attention to errors, become ingrained
in motion capture design and innovation over time. Moreover, we show how
contemporary motion capture systems perpetuate assumptions about human bodies
and their movements. We suggest that social practices of measurement and
validation are ubiquitous in the development of data- and sensor-driven
systems more broadly, and provide this work as a basis for investigating
hidden design assumptions and their potential negative consequences in human-
computer interaction.††©2024. Copyright held by the authors. This is the
authors’ version of the work. The version of record will be published in 2024
ACM CHI Conference on Human Factors in Computing Systems (CHI ’24).

motion capture, measurement, validation, social practices, anthropometry

††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: 2024 CHI Conference on Human Factors in Computing
Systems; May 11–16, 2024; Honolulu, HI††ccs: General and reference
Validation††ccs: General and reference Measurement††ccs: General and reference
Evaluation††ccs: Human-centered computing Human computer interaction
(HCI)††ccs: Social and professional topics Socio-technical systems††ccs:
Hardware Hardware validation

##  1\. Introduction

Motion capture systems are widely used to measure bodies and their movement.
Representations of bodies inferred by motion capture systems are used in a
range of fields including entertainment, manufacturing, medicine, sports, and
robotics (Nogueira, 2012; Zhou and Hu, 2008; He and Luo, 2019; Song et al.,
2021; Menolotto et al., 2020; Bregler, 2007; Magnenat-Thalmann et al., 1998).
Applications include generic settings like gesture detection, tangible
interfaces, and pose estimation, as well as more sensitive (and fraught)
applications like health diagnosis, surveillance, and emotion or gender
detection (Flux et al., 2020; Cunha et al., 2016; Das et al., 2011; Smith et
al., 2002; Deligianni et al., 2019; Barnachon et al., 2014; Boualia and
Essoukri Ben Amara, 2019).

Motion capture systems and their applications are based on assumptions about
how and what we can learn about human bodies and their movement. These
assumptions require interrogation, as they underwrite the uses to which
systems are put and shape the material effects of these systems in the world.
We draw special attention to how the ubiquitous and concomitant activities of
measurement and validation co-develop over time and carry forward such
assumptions. We show that errors play a central role in identifying hidden,
historically layered, and contingent assumptions in motion capture technology.
By analyzing measurement and validation as _social practices_ , we show that
social practice theory offers a crucial lens into the ways in which these
assumptions—and their potential harms—become hidden and entrenched.

###  1.1. Preliminaries: Inferring skeletons, body segments, and their motion.

Motion capture technologies infer the position, rotation and translation of
bodies in space from data collected by sensors. Sensors may be placed directly
on the surface of the body (e.g., inertial measurement units (IMU) or
accelerometers) or may be external cameras that sense markers placed on the
body (e.g., optoelectronic systems) or sense the surface of the body directly
(e.g., depth cameras). Data from these sensors are used to infer features and
movement of bodies and their components (limbs, torso, head, feet, and hands).
This requires statistical inference about the relationship between bodies’
surfaces, skeletal elements, and non-skeletal elements (e.g., soft tissue,
clothing) (Nogueira, 2012). These inferences rely on the results of prior
studies, which were conducted to establish models of the human body and
movement through practices of measurement and validation.

Several key terms emerge. While these terms shift meanings and use over time,
we introduce them here for the reader to better clarify our findings in the
following sections. _Skeletons_ are typically inferred from motion capture
methods, and may refer to representations of the body’s skeletal structure or
to the generic body representation output of motion capture systems. Inferring
the motions (_kinematics_) and forces (_dynamics_) of these (also inferred)
skeletons relies on simplified models of bodies.111See Vaughan et al. (Vaughan
et al., 1992) for an accessible overview of dynamics for human gait analysis.
Consider estimating the amount of force exerted at a joint, such as a knee.
This is done through stylized models of the kinematics and dynamics of
components of the body and how they relate to each other. First, the body
segments corresponding to the thigh and calf must be identified, and their
motions measured. Then, the amount of force theoretically required to cause
the observed motion must be calculated. That calculation itself requires
knowledge of a subject’s body segment parameters (BSPs): characteristics
including mass and center of gravity that are not directly observable without
invasive procedures up to and including dismemberment. As a result, BSPs must
also be inferred from formulas based on an individual’s anthropometric
measurements (Vaughan et al., 1992).

Through a lens of social practice theory, we show these elements of motion
capture rest on normative assumptions about who should be measured to
understand the human body: those who are male, white, ‘able-bodied,’ and of
unremarkable weight. We further show that these assumptions continue to be
implicit in modern motion capture due to practices of validation that
entrench, rather than challenge, them. We note that physiological and
anthropometric measurement and representation have been foundational to the
field of HCI and human factors, reflecting the ways that physical interactions
and arrangements of bodies constrain and enable computational interactions
(Hewett et al., 1992). As the body becomes a site of interface and interaction
with computer systems (Dourish, 2001) through VR, AR, and XR, the assumptions
that underpin its symbolic representation in those systems will have a
material effect on such interactions (Klemmer et al., 2006; Gerling and Spiel,
2021).

Throughout this work we attend to the shifting ways in which motion capture
design engages with _measurement_ , i.e., the practices concerned with the
standardization of generating quantitative knowledge (such as of body
features), and _validation_ , concerned with the conformity to, or violation
of, standards for generating quantitative knowledge based on previously
established measurement practices. A key theme is concerned with types of
_errors_. Measurement practices center around known sources of error, and
validation practices center around the identification and management of those
errors. While our focus is a critical unearthing of the social practices of
measurement and validation in motion capture, adjacent literatures offer a
critical lens to examine the representations of bodies in extended reality and
CHI more broadly (e.g., Gerling and Spiel, 2021; Sum et al., 2022; Williams et
al., 2021); to empirically study knowledge infrastructures (Karasti and
Blomberg, 2018; Star, 1999; Simonsen et al., 2020; Muller et al., 2021); to
understand how assumptions are encumbered and carried forward by communities
in data-driven systems (Passi and Jackson, 2017, 2018; Pasquetto et al., 2019;
Jaton, 2017), and the implications and politics thereof (Pine and Liboiron,
2015; Pierce et al., 2020); and to measurement, validation,222We also note
specific engagement with the _validity_ of motion capture systems for the
design of HCI-related tasks and human factors more broadly. Bachynskyi et al.
(Bachynskyi et al., 2014), for instance, explores the validity of motion
capture systems for HCI-related tasks, such as playing a dance game, typing,
and steering a plane. Bachynskyi et al. argued that motion capture for HCI
must be broadly applicable to people of varying demographic and physical
characteristics, and is more interested in the effects of motion (e.g., muscle
stress or fatigue) than precisely quantifying the motions themselves.
Crucially, they noted that “biomechanical models cannot be considered as
‘valid in general,’ but they have to be validated for each application or
task” (Bachynskyi et al., 2014; Barocas et al., [n. d.]). In contrast we
undertake a systematic review to understand what is meant by “validation”—and
what that implies for HCI research. and error (e.g., Jacobs and Wallach, 2021;
Coston et al., 2023; Lin and Jackson, 2023).

###  1.2. Contributions.

This paper offers a detailed case study of the emergence and stabilization of
the assumptions built into motion capture technologies. Additionally, this
paper also contributes an articulation of how social practice theory (Shove et
al., 2012) can be deployed in the analysis of human-computer interactions. It
does so by applying a social practice theory lens to the historical
development of measurement and validation practices in motion capture
technology design and innovation. This approach focuses on _practices_ as the
unit of inquiry and attends to how the elements of social practice, i.e.
_meanings, materials_ , and _competences_ , link up to stabilize a social
practice across time, space, and contexts. It allows analysts to attend to the
role assumptions play in stabilizing measurement and validation practices in
motion capture from its inception to today.

We argue that it is essential to examine the history of motion capture, and
specifically of measurement and validation in motion capture technology, to
reveal the dynamic interaction between assumptions and technology design. To
those ends, we articulate how assumptions are embedded in all sociotechnical
systems and introduce the analytical lens of _social practices_ of measurement
and validation in §2. We argue that historical analysis is key for this
approach and describe our systematic literature review of motion capture
design, measurement, and validation from 1930s to today, in §3. In §4, we
offer a history of measurement practices surfaced by our review and
characterize how different types of errors reveal stabilizing practices in
each historical era. We uncovered three distinct phases of historical
development in which measurement practices concretely stabilize as distinct
types of errors take center stage. Similarly, we describe when and how
validation practices stabilized (§5) and where these measurement and
validation practices encode assumptions that underpin motion capture
technology and continue to underpin the basic functioning of the technology
today. Finally, in §6 we show how this type of analysis of social practices
reveals the hidden stakes of related artificial intelligence-based
technologies that inherit the assumptions of their contexts. Such a
perspective thus makes possible meaningful holistic AI audits for
accountability (Sloane et al., 2022).

##  2\. Social practices of measurement and validation

All sociotechnical systems are based on assumptions: about things, about
people, and about how the world works or ought to work. Assumptions are not
inherently bad; they guarantee a basis upon which people and systems can
interact. But assumptions baked into sociotechnical systems can cause harms.
This is particularly the case when they enshrine stereotypes and prejudices
that cause representational harms (for example, sexualizing Black women and
girls (Noble, 2018), allocative harms (for example, keeping poor people from
accessing resources (Eubanks, 2017; Barocas et al., 2023)), or bodily harms
(for example, through ”race-norming” in diagnostics (Braun, 2014; Gasquoine,
2009)). Even though they are the root cause of these harms, assumptions can be
hard to identify and exorcise. They are the background knowledge, taken-for-
granted approaches, and unreflected-upon aspects of an activity or
understanding. Assumptions remain unseen.

In the following we reveal how assumptions become embedded in sociotechnical
systems using the analytic lens of social practice theory, focusing on two
social practices in particular: measurement and validation. We argue that
paying attention to how the elements of measurement and validation practices
in motion capture combine and stabilize can help excavate harmful assumptions.
Specifically, we show how attending to errors—the targets of specific
improvement at the time of development—within the practices of measurement and
validation offers a useful point of entry into understanding these practices
and assumptions.

##### All sociotechnical systems rest on assumptions

Assumptions exist for good reasons. We must assume that certain things are
true in order to stabilize provisional knowledge into facts through the
accumulation of supporting evidence, or to alter our knowledge in the face of
contradictory evidence (Rosenberg, 2013). Multiple narratives of the history
and philosophy of science illustrate how the production of scientific
knowledge depends on assumptions about the natural world that, once
stabilized, can be set aside from the focus of subsequent experiments
(Hacking, 1983; Latour, 2003; Shapin and Schaffer, 1985), at least until they
must be revisited in the face of confounding evidence. Nuclear models have
been revised as assumptions about subatomic particles’ properties were updated
(Barad, 2007), as have assumptions about immunity and illness (Martin, 1999).
These revisitations demonstrate that even as prior assumptions become embedded
in what we know about the world and the sociotechnical systems we build to
operate on it, they remain latent artifacts of once-provisional knowledge now
taken as given.

Because they remain as the ground upon which knowledge and technologies are
built, unpacking assumptions is necessary to understand how they have shaped
the practices around testing and verifying the science and technology that was
built upon them. Unpacking assumptions is particularly important as
technologies proliferate to interact with broader populations and changeable
environments to ensure that they remain sound and do not cause harm, given
their scope of use. Particularly with data-centric technologies, where data
collected from within one social milieu might be used to develop products for
different or broader social settings (Koh et al., [n. d.]), it also becomes
necessary to reveal and probe the assumptions that stabilize the ‘ground
truth’ against which the system is validated. Finally, unpacking assumptions
must then account for the histories through which systems were developed
(Edwards et al., 2013), and the stabilizing of what is taken to be ‘ground
truth’ within them.

Figure 1.  The elements of social practices. Sociotechnical practices, like
motion capture, inherit assumptions that are baked into their elements,
including earlier versions of those elements. Assumptions that give rise to
the practice in the first place also spread by way of those elements being
combined and recombined. As practices evolve and stabilize, the assumptions
that undergird them solidify.

A conceptual diagram with three large, grey nodes connected by lines, forming
a triangle. At the top node, labeled ’Competence’, there is text that reads
’Physical and mental skills, multiple forms of shared understanding, practical
knowledgeability’. The bottom left node, labeled ’Material’, contains
’Objects, technologies, infrastructures, tools, hardware, bodies, stuff’. The
bottom right node, labeled ’Meaning’, includes ’Social and symbolic
significance, image, convention, aspiration, belief, identity’. In the center
of the triangle, there is a bidirectional arrow between two terms: ’Practice
Stabilization’ and ’Assumption Solidification’. This represents the dynamic
interplay between material, competence, and meaning in social practices.

##### Analytical lens: social practices as the unit of inquiry

The analytical frame we deploy is informed by social practice theory (Shove et
al., 2012; Shove, 2007). This approach is motivated by a well-established
tradition of examining “the social” within technology by way of practices,
most notably led by figures like Donna Haraway (Haraway, 1988) and Lucy
Suchman (Suchman et al., 1999). Rather than taking an object, an individual,
or a system as a unit of inquiry, social practice theory focuses on behaviors
and activities that span across communities and across time. The emphasis is
on understanding how stability and change emerge in the process of the
“elements” of a social practice linking up or breaking apart in specific
cultural contexts: materials, competences, and meanings (Shove, 2007; Shove et
al., 2012), as illustrated by Figure 1. Materials are objects, technologies,
infrastructures, tools, hardware, bodies, and stuff; competences are physical
and mental skills, multiple forms of shared understanding, and practical
knowledgeability; and meanings are the social and symbolic significance,
image, convention, aspiration, belief or identity at play (Shove et al., 2012;
Higginson et al., 2015).

For example, the practice of “running” is composed of the materials human
bodies, running clothes and shoes, perhaps headphones and a smartphone; the
competence running or jogging for a sustained period of time; and the meaning
of moving one’s body for the sake of health or athletics. These elements link
up and stabilize the practice of “running” writ large by way of being repeated
by many different people at different times and in different places. In other
words, a critical mass of these individualized repetitions constitute a
“social practice” that takes on its own career trajectory. In another example,
“voguing” only stabilized as its present day dance style practiced by many
people in many different places once it went mainstream in the 1990s following
a feature in documentaries and a Madonna music video, even though it had been
around for decades before that (Becquer and Gatti, 1991), originally practiced
by Black and Latinx queer communities in New York City in gatherings called
“balls” (ISTD, 2023; Wolde-Michael, 2019). The practice of voguing began to
take on its own career, as depicted in Figure 2.

Figure 2.  Stabilizing “voguing” as a distinct dance form 1960s-1980s:
critical mass of repetitions.

A diagram similar to the previous figure but layered multiple times
illustrating the elements of the dance form ”voguing.” Three overlapping ovals
represent the components ’Competence,’ ’Material,’ and ’Meaning.’ ’Competence’
includes performing model-like poses and a runway walk that includes angles,
boxes, and lines with the body and arms on the beat. ’Material’ encompasses
bodies, costumes, music, ballrooms in Harlem (NYC), and balls. ’Meaning’
refers to identity, belonging, and dignity through gatherings of mainly Black
and Latinx LGBTQ communities for dance competitions known as ’balls.’ These
elements overlap to depict how voguing is a complex, culturally rich dance
form with deep historical roots and connections to specific communities.

The elements of social practices shape each other over time. That is,
materials affect meanings and meanings affect competences, and so on;
different social practices can share one or more elements, creating an
evolving nexus of practices. For example, the practice of measuring is
conditioned on the availability of the competence of applying an agreed-upon
metric, for example to a body. That competence is an element shared with many
different practices, ranging from XR system design to medical diagnostics.

All practices and sociotechnical practices, like motion capture, inherit
assumptions baked into earlier versions of elements. These assumptions spread
and solidify as the practice evolves. As we demonstrate in the following, a
social practice theory approach is useful for understanding how the practice
of measuring bodies first stabilized based on a certain set of a assumptions
in very specific contexts, and how these continue to be stabilized in machine-
driven measurements of bodies in motion capture systems. The basis for this
approach is paying heightened attention to the assumptions that continue to
flow from the first instances of measuring bodies for motion capture, in other
words: how assumptions inevitably continue to stabilize alongside that (now
machine-driven) practice.

##### Social practices reveal assumptions in technologies.

A social practice theory approach is committed to understanding established
patterns of action and social structure, including in their material
dimension. This, explicitly, includes dominant (cultural) assumptions (Shove
et al., 2012). Assumptions often both motivate the emergence or profound
reconfiguration of a social practice in the first place, and they are
profoundly related to the element ‘meaning.’ For example, the assumption that
power ought to be linked to masculinity led, according to the technology
historian Mar Hicks (Hicks, 2018), to the downfall of the British computing
industry after World War II: as soon as computers, originally associated with
secretarial work and femininity and little power, became seen as central to
power within organizations and society at large, highly skilled woman computer
experts were replaced by low-skill male managers. The practice of computer
work profoundly changed based on assumptions around power and masculinity and
re-stabilized with a new meaning that was ‘gender-flipped.’ Assumptions that
we carry today around innate ability to do computer work stem, in some parts,
from that particular moment in time. Therefore, assumptions not only motivate
(changes of) social practice, but are also the result.

As we posit here, revealing assumptions and how they get baked into
sociotechnical systems increasingly plays a role in understanding the harms
these systems can cause, particularly along the fault lines of intersectional
identifiers such as race, gender, and class (Bolukbasi et al., 2016;
Buolamwini and Gebru, 2018; Obermeyer et al., 2019; Stypinska, 2021; Eubanks,
2017). For example, the assumption that Black people’s lung capacity is
‘naturally’ lower led to ‘race-norming’ in spirometer technology, causing
Black workers having to demonstrate a higher degree of impairment than white
peers before receiving medical treatment (Braun, 2014). A similar example is
‘race-norming’ in neuropsychological tests which, until recently, were in part
used in settlement payout processes in traumatic brain injury cases in the NFL
(Hobson, 2021). Here, too, the underpinning assumption was Black individuals
generally score lower in neuropsychological tests (Gasquoine, 2009), which led
to automatic statistical adjustments of test scores with the result of injured
Black players having to demonstrate a much higher degree of injury (i.e. much
lower test score) than white players– a practice that was recently ended
(Belson, 2022). A social practice theory approach allows us to not only
identify what assumptions are at play, but also to examine how assumptions
become stabilized and infrastructural – an examination that is particularly
important for understanding how assumptions become an implicit part of
technology design and human-computer interaction.

##### Principal sociotechnical practices of motion capture: measurement and
validation.

Social practices overlap and are composed of constituent practices. ‘Motion
capture’ is a sociotechnical practice that is composed of multiple other
practices, including ‘measurement’ and ‘validation’ as well as others like
‘hardware design’, ‘innovation,’ and ‘documentation’. The practices of
measurement and validation, however, take on specific significance in motion
capture: they are foundational to both the technology and the practice, as we
will demonstrate in subsequent sections.

Figure 3.  The elements of measurement practice for motion capture.

A diagram with three interconnected circles. The top circle, labeled
’Competence,’ details ’Applying an agreed-upon metric to bodies in space using
tools.’ The bottom left circle, labeled ’Material,’ lists ’Cameras, bodies,
markers, bodysuits, software, agreed upon metrics, recording studios, room
calibration tools.’ The bottom right circle, labeled ’Meaning,’ describes
’Creating representations of bodies in space: the ability to transform
measures into representations that are useful for the applications of motion
capture technology.’ In the center, where all three circles intersect, are the
words ’Practice Stabilization’ and ’Assumption Solidification,’ implying the
foundational role of these concepts in the practice of motion capture
measurement.

The materials of measurement practice in motion capture are cameras, bodies,
markers, bodysuits, software, agreed-upon metrics, recording studios, room
calibration tools; the competence is applying an agreed-upon metric to bodies
in space using tools; and the meaning is creating representations of bodies in
space, i.e., the ability to transform measures into representations that are
useful for the applications of motion capture technology (Figure 3). These
elements link up into measurement practice which, in motion capture, is
concerned with the standardization of generating quantitative knowledge (such
as of body features) i.e., quantifying known sources of error.

The materials of validation practice in motion capture are statistical models
of bodies and movement, measurements of bodies; the competence is applying an
agreed-upon framework for evaluating a specific set of measurements; and the
meaning is improving motion capture technology, i.e., evaluating a set of
measurements to ensure they are a reasonable proxy for the real world (Figure
4). These elements link up into validation practice which, in motion capture,
is concerned with the conformity to or violation of standards for generating
quantitative knowledge (based on previously established measurement
practices), i.e., identifying and managing errors.

Figure 4.  The elements of validation practice for motion capture.

The diagram features three interconnected circles representing different
aspects of motion capture validation practice. The top circle, labeled
’Competence,’ includes the text ’Applying an agreed-upon framework for
evaluating a specific set of measurements.’ The bottom left circle, labeled
’Material,’ lists ’Statistical models of bodies and movement, measurements of
bodies.’ The bottom right circle, labeled ’Meaning,’ refers to ’Improving
motion capture technology: evaluating a set of measurements to ensure they are
a reasonable proxy for the real world.’ At the center of the interconnected
circles is ’Practice Stabilization’ above ’Assumption Solidification,’
suggesting these are central to the process of validating motion capture
practices.

Measurement and validation practices in motion capture converge in the element
‘meaning’: both practices are meaningful as part of design and innovation in
motion capture technology. In both practices, assumptions most prominently
shore up in errors, because what is considered an ‘error’ at any point is a
locus of attention to what can be controlled and minimized (through
measurement) and identified and managed (through validation).

##### Errors offer key insights into motion capture practices.

Critical archives and science and technology scholars have described how
errors reveal departures of technical models from social expectations (Amrute,
2019; Lin and Jackson, 2023; Vaughan, 2005). Similarly, recent calls for
human-centered design for machine learning point to errors as an opportunity
to re-imagine those systems (Kogan et al., 2020; Chancellor, 2023). This work
seeks to reframe data creation and analysis as a site of critical and of
practical intervention—where errors often point to such a site (Muller et al.,
2019; Passi and Jackson, 2017, 2018; Cabrera et al., 2023; Miceli et al.,
2022; Passi and Sengers, 2020). Fundamentally, the phenomena that
practitioners attend to _as_ errors point to the aspects of their practices of
measurement and validation that are of active and ongoing concern.

Lin and Jackson (Lin and Jackson, 2023) examine the “practice of errors”
shared among machine learning practitioners, calling attention to the
competences and skills involved in error management, the potential of errors
for new discovery, and the fact that errors reveal hidden social structures
and arrangements. They propose that errors always expose infrastructural
assumptions, and that they have the capacity to create new or recombine social
arrangements. Lin and Jackson’s work converges with our social practice theory
approach in that it is explicit about competences and the material context of
the work of domain experts in technology design, and in that it sheds light on
meaning-making across individuals and communities of practice. Most
importantly, Lin and Jackson’s empirical study underlines the significance of
errors for understanding the stabilization and continuous reconfiguration of
measurement and validation practices, because it demonstrates that errors can
reveal existing social practices by highlighting elements or their relations,
that errors can lead to new social practices via the incorporation of new
elements, and that errors can trigger the re-configuring of social practices.

Figure 5.  Analytical lens: errors provide insight into practices
stabilization and assumptions solidification.

A diagram featuring a cyclical process with three magnifying glasses
interconnected by arrows. Each magnifying glass focuses on a word: the first
on ”Errors,” the second on ”Practices,” and the third on ”Assumptions.” The
arrows indicate a directional flow from errors to practices, practices to
assumptions and a large arrow pointing back to errors.

Taken at a moment in time, and within the moment of practice stabilization,
errors thus serve as an important site for understanding social practices and
their elements. Different types of errors documented in different moments in
time index the agreements and concerns that accompany specific sets of
assumptions about what is relevant to the system. Therefore, it is evident
that errors and the documentation of their evolution should be a key focus
when working to understand stabilization and change in measurement practice
and validation for motion capture technology.

##  3\. Methods and Conceptual Analysis

We apply the analytical lens of social practice theory to the motion capture
literature on measurement and validation. Because we are interested in a
historical view of the social practices of measurement and validation as they
have been shaped and reshaped within motion capture, a systematic literature
reviews offer a rigorous approach to uncover elements of social practices over
time. We focus on the measurement of errors specifically as a lens through
which to examine the assumptions inherent in the practices of measurement and
validation more broadly. To those ends, we conducted a systematic literature
review of measurement, validation, and errors in anthropometry (referring to
the measurement of the human body) and motion capture (referring to tracking
technologies of 3D bodies in space). We characterize our findings in §4 and
§5.

###  3.1. Empirical Studies of Social Practices

Any empirical examination of the dynamics of a social practice is specific to
that practice, its elements, and related practices. Thus uncovering
assumptions in the social practices of measurement and validation for motion
capture requires a practice-specific approach. Key here are three concerns:
the process of standardization of measurements, the process of validation, and
the significance of errors as insight into these social practices.

Social practice scholars are typically concerned with observing and analyzing
practices in the field by way of ethnography, for example, understanding the
practices of technology use in the home (Iinuma, 2004). At the same time,
however, a social practice lens explicitly encompasses a view for the historic
development of a practice and its elements. This is because of its commitment
to understanding the moments in which stability and change happen in a social
practice: both are staked on a relationship with history and context.
Materials and objects, for example, are always newer versions of what was
before. (The modern car, for example, is still an updated version of a
carriage (Shove et al., 2012).) This study is particularly interested in these
historic continuities as it focuses on tracing the emergence and stabilization
of assumptions in practices of measurement and validation in motion capture
technology. A systematic literature review is one appropriate method for this
tracing exercise, since the literature is where measurement standards and
validation practices are documented. A systematic literature review is,
therefore, well within the scope of a social practice theory approach.

###  3.2. Systematic Literature Review

In accordance with prior HCI literature (e.g., Bandy, 2021; Zhang et al.,
2023), we relied on the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) to guide our empirical data collection process (Page et
al., 2021). As such, we (1) developed a search strategy and conducted a
keyword search of relevant databases, (2) screened the results of our keyword
search for title and abstract relevance, (3) supplemented the keyword search
through a citation mapping search, (4) screened the results of the citation
mapping search for title and abstract relevance, (5) screened the results of
both searches for full-text relevance, and (6) collected and synthesized data.

####  3.2.1. Database Search Strategy

We began with keyword searches in various academic databases. To reach the
relevant literature, we considered keywords along two domains: related to body
measurement (anthropometry and measurement of ‘body segment parameters’)
generally and to motion capture specifically. For both domains, we searched
accessible databases containing publications in the biomedical sciences
(Scopus, PubMed, Wiley, and Taylor & Francis). We additionally searched
databases focused on the social sciences (JSTOR, Springer) for anthropometry,
and databases focused on computer science and electrical engineering (IEEE,
ACM) for motion capture. All databases except for PubMed were chosen because
they each hosted multiple papers surfaced by a test query (following the query
structure in Table 1) on Google Scholar. PubMed was then added due to its use
in prior motion capture literature reviews (e.g., Poitras et al., 2019). Based
on the fact that they encompassed the papers surfaced by our test query, we
expect that our database selection was robust; however, we additionally
included a citation mapping component in order to identify work that may have
been excluded.

We restricted our search to results with titles containing words related to
error or validity assessment (e.g. “error,” “variability,” “reliability,”
etc.) and titles or abstracts referencing the specific measurement domain
(e.g. “anthropometry”, “motion capture”, etc.) to ensure that we were only
capturing research explicitly attending to error measurement and validation
practices within those domains. We conducted our search between March 29th and
August 7th, 2023. Databases, search queries, and filters (§3.2.3) are shown in
Table 1.

Table 1. Search Strategy. Of note, we included ‘error’ rather than ‘measurement’ in our keyword search because measurement broadly is the domain in which anthropometry and motion capture sit, and the focus on error measurement more narrowly allows us to better target work related to implicit assumptions. Similarly, we excluded ‘variability’ and ‘variation’ from our anthropometry-focused keyword search because variation (between individuals or populations) is a standard unit of measurement in anthropometric studies; searches including these keywords returned results that were overly broad.  Application Domain | Databases | Search Query | Filters
---|---|---|---
Anthropometry | JSTOR |  |
Springer |  |  |
Scopus |  |  |
PubMed |  |  |
Wiley |  |  |
Taylor & Francis |  Title contains: |  |
accuracy OR error OR errors OR validity OR |  |  |
validation OR validate OR reliability OR |  |  |
consistency OR critical
|  |  |
Abstract or Title contains: |  |  |
anthropometry OR anthropometric OR |  |  |
(body AND segment AND parameters) | Full text accessible online |  |
Written in English |  |  |
Peer-reviewed research |  |  |
Full-length paper |  |  |
Primary research |  |  |
Motion Capture | IEEE |  |
ACM |  |  |
Scopus |  |  |
PubMed |  |  |
Wiley |  |  |
Taylor & Francis |  Title contains: |  |
accuracy OR error OR errors OR validity OR |  |  |
validation OR validate OR variability OR |  |  |
variation OR reliability OR consistency OR critical
|  |  |
Abstract or Title contains: |  |  |
“optical tracking” OR “motion capture” OR |  |  |
“motion analysis” OR “motion tracking” OR |  |  |
“human motion” OR “3D camera” OR |  |  |
stereophotogrammetry |  |  |

####  3.2.2. Citation Mapping Search Strategy

To ensure that our keyword search was not overly narrow, we built a citation
map based on the keyword search results, which we used to identify additional
papers related to error measurement for inclusion. We used ResearchRabbit,
333https://www.researchrabbit.ai/ a ”citation-based literature mapping tool”,
to identify similar papers based on overlapping citations. We selected
ResearchRabbit over other citation mapping platforms because it (1) allowed
for us to build maps starting from one or multiple papers; (2) separated
suggested papers into ”Earlier,” ”Similar,” and ”Later” work, allowing for a
clearer understanding of the directionality of citations; and (3) provided
full functionality with a free account. However, we note that ResearchRabbit
is not without limitations: its documentation is not publicly available,
meaning that the specific databases it searches to collect paper metadata and
the algorithms it uses to suggest related work are not precisely known.
Because our review is focused on the history and foundations of error
measurement in motion capture, and because the ”Earlier” and ”Later” work
suggestion algorithms in ResearchRabbit are better-documented (Tay, 2021), we
used the “Earlier Work” suggestions to export a set of papers that were cited
by our previously surfaced papers. Finally, in cases when work that had been
frequently cited by the papers surfaced by our database search was not
included in ResearchRabbit (e.g., gray literature like (Dempster, 1955;
Chandler et al., 1975)), we manually added it to our set of results.

####  3.2.3. Filtering and Screening Strategy

After retrieving the raw results of our search query and of our citation
mapping search, we restricted our analysis to peer-reviewed articles featuring
primary research with full text available online. We further required articles
to be written in English because it is the only language in which our entire
research team is fluent. Following the application of these filters, one
researcher screened the title of each paper for relevance. Relevant titles
were those that included keywords related to either measurement of a specific
error type or validation of a broader motion capture system. One researcher
then screened the abstracts of each remaining paper. Abstracts were considered
relevant if they indicated that the paper was specifically about validation or
error measurement. Finally, one researcher conducted a full review of the text
of each remaining article to determine relevance, again keeping those that
were substantively about error measurement for anthropometry or motion
capture, or validation of motion capture systems.

####  3.2.4. Data Extraction and Analysis

In our data extraction process, one member of the research team read each
paper in full and manually recorded metadata, including whether the paper
addressed a specific type of error (and if so, what type) or represented a
full-system validation approach, how error or validation was measured, and
what data was presented for measurement (if human subjects were used, we
recorded their number and demographics). Each of those observations represent
details explicitly addressed by the surfaced papers.

We initially conducted this full review on a sample of fifty papers surfaced
through keyword search. Based on this sample, we identified a set of distinct
validation and error types within the motion capture literature. We used these
error types as keywords to classify the remainder of the papers. If additional
common topics appeared as we tagged new papers, we added them to the keyword
list as well. For example, we added tags to specify whether another motion
capture system was being used as a “ground truth,” whether that system was
explicitly referred to as a “gold standard,” and to indicate when papers
proposed systems for use in in-home rehabilitation. We used these keywords to
assess trends and standard practices in motion capture measurement and
validation.

While conducting our initial fifty-paper review, we observed that methods for
measuring different types of error in motion capture systems were proposed and
stabilized during distinct periods of time. (We describe these periods, which
we call ”eras,” in detail in §4.) In other words, competences in error
measurement were developed shortly after new materials for motion capture were
introduced, both of which linked up with the meaning of motion capture design
and innovation. Subsequent work tended to adopt and apply those competences.
As a result, although we did not apply date filters in surfacing our original
set of papers, we restrict our analysis of each respective error type in §4 to
the time period during which its measurement approach was stabilizing.

###  3.3. Limitations

A primary limitation of our method is the risk of bias in both selecting and
analyzing the relevant literature. The choice of databases for each error type
may have excluded some relevant work hosted on databases other than the ones
we searched. We conducted a citation mapping search to mitigate this but we
cannot guarantee that it has no impact on our results – the specific papers
suggested by ResearchRabbit also may differ from what a different citation
mapping tool may have surfaced, and will still not include research hosted on
non-academic sites (e.g. by corporate research & development teams). The
choice to restrict our work to English-language research also excludes
valuable global perspectives. Finally, the identities and viewpoints
represented in our research team also constitute a source of bias. Although
the research team is composed of individuals from a variety of backgrounds, we
are all white, cisgender researchers from the United States and Europe. In
many ways, our bodies look like the ones that motion capture was designed for.
This necessarily hinders our ability to understand the personal impact of
motion capture’s assumptions about the human body. We also note that none of
the authors are primarily motion capture researchers. While this limits the
extent to which we are exposed to norms and current practices in the field, it
also allows us to examine measurement and validation practice vis-a-vis the
history and error typology of motion capture with fresh eyes. Lastly, we want
to underscore a conceptual limitation of this paper: whilst assumptions are
the graspable thing we are concerned with in this paper, we are not able to
cover the siblings of assumptions: politics and ideologies. Due to our focus
and scope, the question how these relate to our critical interrogation of
assumptions remains unexplored in this paper. This includes, for example, are
more focused discussion of how motion capture technology remains an ableist
system since it is not only based on a very limited set of data on few
homogeneous bodies, but also because it is entangled with ableist ideologies
that run across society and technology design at large.

Figure 6.  PRISMA flow diagram.

A PRISMA flow diagram showing how many records were identified, screened, and
ultimately included in our systematic search. We identified 11,352 papers from
databases, filtering out 3,368 as duplicates; 302 non-English papers; and 471
papers without an accessible full text original research work available. This
left us with 7,211 papers, after which we applied relevance filters to exclude
6,410 based on title; 367 based on abstract; 116 based on full text; and 64
based on date. This left us with 254 records from our database search. We
supplemented that with a citation mapping process, working from our selected
papers as seeds. We identified 57 additional records via citation mapping, of
which we excluded 30 for title relevance, 1 for abstract relevance, 1 for full
text relevance, and 1 for date relevance; leaving us with 24 additional
records. The total number of records thus included in our systematic review
was 278.

##  4\. Findings: A History of Errors and Assumptions in Motion Capture

Following the PRISMA methodology, we analyzed 278 papers related to the
measurement in both anthropometry and motion capture technology, as well as
validation of motion capture systems (Figure 6). Our analysis revealed what we
categorize as three eras in the history of motion capture research: Foundation
(1930-1979), Standardization (1980-1999), and Innovation (2000-present). We
find that each era centered on a subset of six _error types_ : anthropometric
errors, errors in body segment parameter estimation, marker placement errors,
soft tissue artifacts, instrument errors, and algorithmic errors. Errors make
the convergence and stabilization of social practices of measurement and
validation legible (Figure 7), and are thus an important site for
understanding social practices from a historical lens. Although we
characterize these eras as discrete historic periods, this does not imply that
there are necessarily uniquely distinct, linear eras – just that these eras
highlight key moments during which practices stabilized and different elements
were created or became more prevalent. The stabilization and reconfiguration
of social practices is messy and contingent; there are moments when practices
stabilize, reconfigure, or dissolve as elements shift. These events are always
the result of specific histories and contexts.

Figure 7.  The convergence of measurement and validation practices for motion
capture.

A diagram that visually represents the convergence of the social practices of
measurement and validation within the field of motion capture. On the left, a
cluster of three nodes illustrates the ’Social Practice of Measurement’, which
includes nodes for ’Material’, ’Competence’, and ’Meaning’, all
interconnected. The ’Meaning’ node extends towards the right, bridging the gap
to another cluster of nodes representing the ’Social Practice of Validation’,
sharing the ’Meaning’ node with the measurement practice and connecting to its
own ’Material’ and ’Competence’ nodes. Highlighted within the diagram are
numerous red ’X’ marks scattered between each of the ’Meaning’, ’Competence’,
’Material ’ node clusters, symbolizing the identification and management of
errors within these processes. Accompanying text elaborates on the function of
each practice: the left side emphasizes standardization and quantification of
known error sources in measurement, while the right side focuses on adherence
to or deviation from established standards in validation, particularly
concerning error management. The central ’Meaning’ node’s text underscores its
relevance to motion capture technology design and innovation, linking it to
broader practices within the field.

In each era, technological advances provided new materials for measurement and
contemporaneous researchers developed new competences in making inferences
about the human body (recall Figure 3). As a result, each era introduces a
different set of assumptions underlying the meanings of modern motion capture
– and each era measures a different subset of error types corresponding to
those assumptions. We propose this historical framework as a tool for the HCI
community to understand how motion capture as a social practice has stabilized
over time (Sloane and Zakrzewski, 2022).

###  4.1. Foundation Era, (c. 1930-1979).

The foundations of motion capture technology are based on methods for
measuring the human body. Work from the Foundation Era focused extensively on
anthropometry, i.e., the measurement of bodies (§4.1.1). A key focus within
this was on the estimation of body segment parameters (§4.1.2). Error
measurement during this era was thus correspondingly concerned with these two
efforts (Table 2). Already during this era, we begin to see a stabilization
around concurrent validity assessments: the validation of a new system, tool,
or method based on its agreement with a preexisting reference that is
considered a ‘gold standard.’ We see a similar stabilization around
reliability assessments, which quantify the extent to which repeated
measurements of the same subject match one another. Both of these remain
crucial components of what is understood by ‘validity’ in modern motion
capture today.

####  4.1.1. Anthropometric Errors

We observe two main types of anthropometric error of concern in the 9 papers
we analyzed from this era: _intra-_ and _inter-observer error_. Intra-observer
error is defined by the differences in repeated measurements taken by the same
observer on the same subject within a short period of time. During this time
period, intra-observer error is also referred to as intra-subject error,
extrinsic error (Davenport et al., 1934), and reliability (Lincoln, 1930;
Meredith, 1936). Inter-observer error is defined by the differences in
measurements taken by different observers on the same subject. During the same
time period, it was also referred to as precision (Marks et al., 1989),
intrinsic error (Davenport et al., 1934), and objectivity (Marshall, 1937).

Table 2. Focus of error measurement in the Foundation Era. Error Type | Description |  Papers
---|---|---
Anthropometric Errors | Errors in the measurement (height and weight; length, circumference, mass, and volume of segments) of the human body |  (Lincoln, 1930; Davenport et al., 1934; Meredith, 1936; Marshall, 1937; Gavan, 1950; Munro et al., 1966; Jamison and Zegura, 1974; Kemper and Pieters, 1974; Martorell et al., 1975)
Errors in Body Segment Parameter Estimation | Errors in the estimation of the mass, center of gravity, and moment of inertia of individual segments of the human body |  (Dempster, 1955; Hanavan, 1964; Clauser et al., 1969; Chandler et al., 1975; Hatze, 1980)

Each type of anthropometric error was concerned with repeated measurements of
the same subject (i.e., reliability), reflecting attention to the
_competences_ of anthropometric measurements. In particular, researchers at
this time proposed methods for error mitigation (employing multiple measurers
(Lincoln, 1930) or measuring based on more identifiable bony landmarks (Gavan,
1950; Martorell et al., 1975)). Some connected _materials_ and competences by
quantifying inter-observer error from an institutional lens, finding that
measurements of the same subjects with the same equipment across two
institutions differed systematically (Kemper and Pieters, 1974). Despite the
focus on competences, the historical context indicates that _meanings_ were
also contingent: noted eugenicist Charles Davenport, for instance, argued that
anthropometry is “a form of psychometry,” where these measurements have
physiognomic (and essentially racialized) implications (Davenport et al.,
1934).

####  4.1.2. Errors in Body Segment Parameter Estimation

Motion capture systems use estimated body segment parameters to infer the
force required to generate an observed motion – a crucial component of systems
designed, for example, to identify injury risk in athletes and workers. In the
Foundation Era, the models through which body segment parameters are
estimated—some of which are still in use today—were developed. We analyzed 5
papers from this time.

(a)  (b)

Figure 8.  Models of the human body used to define and estimate body segment
parameters. Although Hanavan’s model (a) is more simplistic than Hatze’s (b),
both rest on crucial assumptions about the rigidity of human body segments
that do not reflect reality. Reproduced from Hanavan (Hanavan, 1964) and Hatze
(Hatze, 1980).

Two models of the human body that have been used to define and estimate body
segment parameters. The earlier model on the left is a very simplistic model
that divides the body into regular geometric shapes (e.g., cylinder, sphere,
cone). The later model on the right is a less simplistic model that looks
similar to a mannequin.

The earliest influential findings on body segment parameters come from
research funded by the US Air Force and therefore was conducted on subjects
who were exclusively male, overwhelmingly white, and typically of slender or
athletic builds. For instance, Dempster (Dempster, 1955) conducted a study to
identify the optimal ergonomic design of a cockpit based on pilots’ range of
motion—a classic challenge in interaction design. As part of that work, he
acquired, dismembered, and determined the body segment parameters of eight
older, white male cadavers. In a similar work, Chandler et al. (Chandler et
al., 1975) dismembered and measured the BSPs of six male cadavers “in good
overall condition”444Defined by the authors as without “congenital anomalies,
major surgical alterations, general or localized structural atrophy, excessive
wasting, or obesity.” in order to better design impact protection systems.
Both studies presented simple formulas designed to calculate BSPs based on
anthropometric measurements (height; weight; limb length; and, crucially, limb
volume) taken of a living body.

The measurements presented by the US Air Force—especially the Dempster
study—quickly became accepted as “ground truth” for BSPs. This gave rise to
the competence of concurrent validity assessment for motion capture:
subsequent research used Dempster’s data to validate newly proposed BSP
formulas. Instead of requiring cumbersome limb volume measurements, these new
formulas relied on strong, simplifying assumptions about the human body
(specifically, that body segments are rigid and have uniform densities), and
then compared their results to Dempster’s in order to show that they were
valid (Hanavan, 1964; Hatze, 1980). Meanings are also implicated by this
process, which represents an understanding about the kinds of bodies that are
and should be measured when estimating human body segment parameters. As such,
we can observe a key moment of _stabilization_ of the social practices of
measurement and validation of BSPs—in this case with respect to concurrent
validity and reliance on frozen male cadavers—during this era.555For a
detailed history of body segment parameter estimation practices prior to 1964,
see Drillis et al. (Drillis et al., 1964).

###  4.2. Standardization Era (c. 1980-2000).

The social practices of measurement and validation during the Standardization
Era re-stabilized around the development of the first major commercial motion
capture systems. These were marker-based motion capture systems, which depend
on fixed camera sensors detecting markers attached to specific points on the
outside of bodies to infer the associated skeleton and movements (recall
§1.1). The key errors attended to during this era were related to the
_materials_ of these new standardizing technologies, the _meanings_ implicit
to how they represented and measured bodies, and the new _competences_ that
they both allowed for and required. Thus we highlight three types of errors:
marker placement errors, corresponding to competences of the users of these
technologies; soft tissue artifacts, corresponding to assumptions about the
types of bodies motion capture systems are designed for; and instrument
errors, corresponding to discrepancies associated with the materials
themselves (Table 3). In parallel, new measurement and validation strategies
were being negotiated as these social practices continued stabilizing,
shifting, and re-stabilizing.

Table 3. Focus of error measurement in the Standardization Era. Error Type | Description |  Papers
---|---|---
Marker Placement Errors | Errors in the identification of body landmarks |  (White et al., 1989; Bell et al., 1990; Small et al., 1993; Neptune and Hull, 1995; Della Croce et al., 1999; Leardini et al., 1999)
Soft Tissue Artifacts | Errors in the estimated motion of bones attributable to the fact that the observable surface of the body moves independently of bones |  (Cappozzo, 1991; Lafortune et al., 1992; Maslen and Ackland, 1994; Cappozzo et al., 1996; Fuller et al., 1997; Holden et al., 1997; Reinschmidt et al., 1997)
Instrument Errors | Errors in reconstructing 3D coordinates attributable to the capture of raw position data by the system |  (Haggard and Wing, 1990; Ehara et al., 1995; Klein and DeHaven, 1995; Dabnichki et al., 1997; Ehara et al., 1997; Everaert et al., 1999; Richards, 1999)

####  4.2.1. Marker Placement Errors

Making inferences about the movement of a human body with marker-based motion
capture systems requires identifying specific locations on a subject’s body
(with markers), relating them to an anatomical model of a human body, and
inferring movement of the anatomical model based on observed movement of the
markers. In this context, marker placement error, or anatomical landmark
misplacement, refers to the errors from the ‘misplacement’ of markers with
respect to certain bone locations; we analyzed 6 papers on this topic from
this time. Here, the attention on marker placement errors varies with palpable
joints (externally identifiable by touching the body, e.g., elbow) or internal
joints that require inference or imaging to identify (e.g., hip).666For a
detailed history of anatomical landmark misplacement estimation practices
prior to 2005, see Della Croce et al. (Della Croce et al., 2005).

The anatomical model required to infer movement required assumptions relating
the exterior surface of the body to its internal joints. In particular, these
assumptions are shaped by meanings around what needs to be measured to define
a body – in other words, anatomical models are adapted to individuals only to
the extent to which they can be scaled based on a predefined set of
anthropometric measurements (White et al., 1989). Whether within or between
observers, subjects, or systems, anatomical models were assumed to be stable,
with error measurement focused instead on variability due to marker placement,
following competences of reliability assessments initially developed for
anthropometry (§4.1.1) (Della Croce et al., 1999). At the same time, a
competence emerged of using imaging as a ground truth for marker placements in
concurrent validity assessments (following §4.1.2) (Bell et al., 1990; Neptune
and Hull, 1995; Leardini et al., 1999), despite research from as early as 1993
(Small et al., 1993) showing that the identification of anatomical landmarks
was subjective even in x-rays.

####  4.2.2. Soft Tissue Artifacts

In marker-based motion capture systems, markers are placed exterior to the
body; in between the markers and the bones of interest are skin, muscle, fat,
scar tissue, tendons, viscera, and so on. Markers therefore move independently
of bones as subjects’ soft tissues move, introducing both random and
systematic soft tissue artifacts (STA). Systematic errors can emerge from both
individual people (e.g., greater error in individuals with more or looser soft
tissues due to body type or age) and task type (e.g., greater error in more
intense movements).777For a detailed history of soft tissue artifact
estimation practices prior to 2005, see Leardini et al. (Leardini et al.,
2005), or Camomilla et al. (Camomilla et al., 2017) for a more recent
editorial. The measurement of soft tissue artifacts stabilized in the 1990s;
we analyzed 7 papers related to STA measurement published during this time.
These practices primarily measured STA by comparing skin-mounted markers to
bone-mounted (or already implanted (Cappozzo et al., 1996)) pins (Lafortune et
al., 1992; Fuller et al., 1997; Holden et al., 1997; Reinschmidt et al.,
1997), although less invasive imaging methods emerged as well (Maslen and
Ackland, 1994; Cappozzo et al., 1996).

We note several key normative assumptions about the bodies that motion capture
is designed for, carried over from the meanings of body measurement
highlighted by Dempster’s early cadaver studies, that re-emerged during this
time. The subjects in foundational STA studies were overwhelmingly male (26
out of 33 subjects for whom sex was specified), able-bodied (all other than
the seven subjects recruited due to their bone-mounted surgical pins), and of
unremarkable weight (2 out of 37 total subjects were noted to be “overweight”
according to their BMIs). We again observe how historical assumptions about
standard bodies are implicit in these technologies while new elements also
take on new logics about bodies (their internal composition and the artifacts
of their exteriors, such as loose skin).

####  4.2.3. Instrument Errors

The rise of the new materials of marker-based systems additionally led to
efforts to measure instrument errors. In this context, instrument error refers
to inaccuracies in reconstructing the position of marker coordinates as a
result of systematic or random errors in the capture of the raw data by the
system. Like STA estimation, the competences underlying assessment of
instrument error were developed in the 1990s. We identified 7 works measuring
instrument error during this time; most emphasize validation methods based on
comparisons to rigid objects with known sizes, locations, or movements
(Haggard and Wing, 1990; Ehara et al., 1995, 1997; Klein and DeHaven, 1995;
Everaert et al., 1999; Richards, 1999; Dabnichki et al., 1997).888For a
detailed review of instrument error measurement practices prior to 2005, see
Chiari et al. (Chiari et al., 2005). ASTM, a standards organization, named an
analogous procedure as the official “Standard Test Method for Evaluating the
Performance of Optical Tracking Systems that Measure Six Degrees of Freedom
(6DOF) Pose” (E57 Committee, 2022) in 2016, reflecting a _stabilization_ of
these practices now codified in technical standards.

###  4.3. Innovation Era (c. 2000-present).

As sensing technologies became cheaper, faster, and more accessible, and as
modern machine learning expanded, we observe another shift in the social
practices around measurement and validation in motion capture. Broadened
access—through lower cost and less obtrusive systems–diversified the use cases
of motion capture systems and the social practices of their design. For
instance, consider the shift in materials: alternative sensors like IMUs can
collect movement data without expensive camera systems; markerless systems
like depth and 2-D video cameras allow unobtrusive surveillance or
interaction. At the same time, improved machine learning techniques allow for
increased complexity in the underlying body models that motion capture systems
rely on to estimate movement and force. Measurement and validation practices
in this era focus primarily on measurement of algorithmic errors (e.g., of
body models), which we discuss below in §4.3.1, and validation of novel
systems (i.e., asserting that a new technology—which may include new sensors,
new processes, and new algorithms—is competent at a historical task), which we
discuss in §5.

Table 4. Focus of error measurement in the Innovation Era. Error Type | Description |  Papers
---|---|---
Algorithmic Errors | Errors attributable to the algorithms or models used to analyze captured data |  (Myers et al., 2004; Wang et al., 2013a; Bassani et al., 2017; Sekiguchi et al., 2020; Lai et al., 2021; Mohtajeb et al., 2021; Langley et al., 2021; Tsai and Huang, 2022; Knodel et al., 2022; Moghadam et al., 2023; Caron-Laramée et al., 2023)

####  4.3.1. Algorithmic Errors

The development of new materials (sensors, computing capabilities) in motion
capture systems corresponds to increased attention on algorithmic errors in
body models (competences) used to infer force and motion (Table 4).
Specifically, researchers are interested in the deviations between those
models and each other, or those models and some external measurement taken as
a ground truth. We analyzed 11 papers related to measuring error in body-model
related algorithms during the Innovation Era.

Error in newly proposed body models is often assessed based on their agreement
with previous methods (as in the concurrent validity competences used to
assess BSP estimation models, discussed in §4.1.2) (Langley et al., 2021;
Knodel et al., 2022; Caron-Laramée et al., 2023; Moghadam et al., 2023; Myers
et al., 2004; Bassani et al., 2017). Concurrent validation may also rely on
external sources of data (as opposed to previously existing algorithms using
the same data), such as MRIs and EMGs (Lai et al., 2021; Mohtajeb et al.,
2021). Building on competences for measuring instrument error (§4.2.3), some
researchers build ‘dummy’ body segments in order to assess the performance of
their body models against known quantities of size and movement (Sekiguchi et
al., 2020; Myers et al., 2004). Reliance on benchmark data has also become
increasingly common. Researchers increasingly use annotated motion capture
datasets like COCO999https://cocodataset.org/ or open source motion capture
libraries like OpenPose101010https://github.com/CMU-Perceptual-Computing-
Lab/openpose as “ground truths” against which to validate their algorithms
(Bassani et al., 2017; Tsai and Huang, 2022; Moghadam et al., 2023).111111The
design of our systematic review would not recover recent critical scholarship
on the politics of training data for computer vision tasks, e.g., (Denton et
al., 2021; Miceli et al., 2022; Scheuerman et al., 2021). Future attention to
motion capture datasets (e.g., in http://mocap.cs.cmu.edu/info.php) in line
with existing critiques would, for instance, also reveal a lack of diversity
and representations of bodies and their gestures, interactions, and
environments. Others, however, have pointed out that this may negatively
impact algorithms’ abilities to generalize robustly, as benchmark data may not
contain examples of use cases likely to be seen in deployment (a lack of low-
light or nighttime images in COCO, for example (Chun et al., 2019)).

Another way that has emerged to assess algorithmic error is reformulating the
problems that algorithms seek to solve—for example, creating more easily
measurable proxies rather than detecting subtle, hard-to-measure body
attributes. While this reformulation is standard practice (e.g., (Passi and
Sengers, 2020)), it also reframes the intentions of a system. Fall detection
tools, for example, are intended to identify when elderly or gait-impaired
individuals exhibit movement patterns that typically precede a fall. However,
researchers often train and test models on healthy actors who are asked to
walk with predefined abnormal gaits—thereby assuming that these performed
gaits are extremely similar to those of individuals with genuine fall risks
(Chen et al., 2022; Wang et al., 2013b).

Finally, much of the work in the Innovation Era involves leveraging lower cost
and less obtrusive sensors to create systems that function well for home use,
or are able to work well on populations with atypical body movements (e.g.,
individuals with Parkinson’s or individuals recovering from surgery or a
stroke). Research in this era has primarily focused on the validation of these
novel systems by comparing their performance to what is that of a ‘gold
standard’ marker-based motion capture system, typically in laboratory
environments. We explore this practice in detail in §5.

###  4.4. The Evolution of Measurement and Validation Practices in Motion
Capture

Figure 9.  Measurement and validation practices converged over the meaning of
motion capture of bodies over time. Even though the social practices of
measurement and validation were present in each era, both the elements and how
they linked up evolved over time. By comparing these diagrams across eras, we
highlight how the elements were combined (e.g., converging on meaning) and
took on more significant roles (e.g., the elements of validation became more
salient over time).

A series of three sets of diagrams representing the social practice of
measurement and validation in different eras. In the ’Foundation Era’ at the
top, there are two separate structures for ’Social Practice of Measurement’
and ’Social Practice of Validation’, each with three connected nodes labeled
’Competence’, ’Material’, and ’Meaning’. The nodes under ’Validation’ are
smaller than those under ’Measurement’.

In the ’Standardization Era’ in the middle, the structures for ’Measurement’
and ’Validation’ connect at the ’Meaning’ node. The ’Competence’ and
’Material’ nodes in the ’Validation’ structure are slightly larger than before
but still smaller than those in the ’Measurement’ structure.

The ’Innovation Era’ diagram at the bottom shows a complex interconnection
where the ’Meaning’ node links both ’Validation’ and ’Measurement’ social
practices, indicating overlap between the diagrams. Here, the sub-nodes of
’Competence’, ’Material’, and ’Meaning’ are all equal in size. These diagrams
visually narrate the historical development and increasing complexity of
motion capture practices from 1930 to the present.

Reflecting on the social practices of each era, we can characterize the
evolution of the social practices of measurement and validation, as in Figure
9. Considering this evolution, we know that specific elements, i.e., the
specific materials, meanings, and competences, shifted across the eras. For
instance, calipers (materials) and limb volume calculations (competences)
played a significant role in the Foundation Era, whereas benchmark datasets
(materials) and calibration (competences) were more typical as elements in the
Innovation Era. This evolution can also highlight how these social practices
shifted over time.

In the _Foundation Era_ (§4.1), motion capture is exclusively evolving around
anthropometry, i.e., the measurement of bodies. Validation practices exist,
but they are not integral to the practice of motion capture. Error measurement
during the Foundation Era is concerned with the estimation of body segment
parameters. In the _Standardization Era_ (§4.2), practices of measurement and
validation being to converge in motion capture, particularly around the
meaning of measuring bodies for the design and improvement of motion capture
technology. Error measurement during the Standardization Era is concerned with
marker placement, soft tissue artifacts, and instrument errors. In the
_Innovation Era_ (§4.3), practices of measurement and validation are closely
converging around the meaning of motion capture generally: both constitute
what it means to _do_ motion capture. Measurement and validation practices in
this era focus primarily on measurement of algorithmic errors and the
validation of novel systems.

The social practice lens then lets us explicitly compare across eras in Figure
9. Here we can visualize this convergence around the meaning of motion
capture, as well as how the social practice of validation for motion capture
came to be more salient. A more mature practice in the Innovation Era, we next
explore the social practices of validation in the Innovation Era in more depth
in §5.

##  5\. Findings: Validation Practices and ‘Gold Standards’ in Motion Capture
in the Innovation Era

Table 5. Motion capture validation practices and their roots. Validation Method | Description |  What elements of motion capture measurement underlie this validation practice?
---|---|---
Concurrent Validity | A new method or system is compared to a previously validated reference |  Measuring errors in body segment parameter estimation (newly proposed methods were validated through agreement with Dempster) and soft tissue artifacts (external sensors were validated by comparison with imaging data or sensors mounted directly on bones)
Reliability | Repeated measurements of the same subject are compared to one another |  Measuring anthropometric and marker placement errors (intra-observer and inter-observer variation)
Robustness | System measurements are compared to one another as inputs change |  Measuring instrument errors (originally to identify ideal conditions for system use) as well as soft tissue artifacts and marker placement errors (to identify which marker placements minimize STA)
Construct Validity | Whether or not a system meaningfully performs its intended use case |  Measuring algorithmic errors, including assessment of specific marker placements developed for clinical and diagnostic use
User
Studies | Examination of user behaviors and needs |  Some roots from original attempts to measure body segment parameters for the purpose of determining space requirements of aircraft operators; also an increasing adoption of HCI methods within motion capture research
Subgroup Validity | Relative performance of a system over people of different demographic or physical characteristics |  An interrogation of the assumptions dating back to the Foundation Era that system performance in general can be well-represented with research on thin, white, male bodies

Validation came to play a more central role during the Innovation Era (recall
Figure 9). Our PRISMA analysis uncovered more variation in the approaches
taken to validation in the Innovation Era than in previous eras. Moreover,
attention to the _social practice_ of validation also helps reveal the origins
of this practice via their relationships to elements from earlier eras (Table
5).

In the literature from this era, we find a range of approaches. First, we find
that concurrent validity assessments, in which new technology is compared to a
‘gold standard,’ are considered both necessary and sufficient for validating
new motion capture technology, and we attend to how meanings, competences, and
practices around ‘gold standards’ come together and shift. Reliability
assessments, in which new systems are shown to take repeatable measurements,
are considered a necessary but not sufficient component of validity. Construct
validity, which asks whether motion capture systems provide measurements that
meaningfully correspond to clinical or other standards, and robustness, which
measures the ability of a system to maintain performance as its input
parameters change, are assessed much less frequently. Crucially, we find that
subgroup validity, or whether a system performs equally well for individuals
with different physical or demographic characteristics, is almost never
tested.

###  5.1. Concurrent Validity

Concurrent validity is by far the most common validation approach our
systematic review surfaced, appearing in 97% (N=228) of the literature. In
this approach, a new system or algorithm is compared to a previously validated
reference system. Agreement is typically measured through metrics like
correlation coefficients or through methods like the Bland-Altman limits of
agreement. Although these metrics are simple, they reflect how meanings are
operationalized. For example, should a gait parameter be measured as a
continuous variable (level of extension of a joint) or a discrete event (peak
joint extension)? The choice will impact how accuracy is interpreted and how
future practices are shaped.

We observe stabilizing practices driven primarily by an agreement in materials
and meanings: 90% (N=210) of the time, the reference to which a system is
compared is a marker-based motion capture system; 50% (N=105) of the time such
a system is used, it is specifically Vicon-
branded.121212https://www.vicon.com/ In 62% (N=145) of papers, we found that
authors explicitly referred to their reference systems as ‘gold standards.’
Once a system has been shown to have even good agreement with an existing
‘gold standard’ through a concurrent validity assessment, it is eligible to be
promoted to ‘gold standard’ itself. For example, GAITRite, a walkway outfitted
with pressure sensors, was validated against Vicon in 2005 (Webster et al.,
2005). Researchers found that GAITRite had a strong, but not perfect,
correlation with Vicon in measuring stride time. Twelve years later, a
different team used GAITRite to validate the ability of instrumented shoe
insoles to measure temporal gait parameters – and explicitly referred to
GAITRite as a “gold standard” in the process (Jagos et al., 2017).

A small number of papers we reviewed pointed out that this ‘gold standard’
status carries strong assumptions about what types of error matter. Several
research teams argued—correctly—that differences between their proposed
systems and the reference systems could have been the result of errors in
either system, due to the fact that ‘gold standard’ systems are known to be
impacted by marker placement errors and STA (Agostini et al., 2017; Potter et
al., 2022). The assumptions that remain implicit are also revealing. A common
validation approach, for example, involves treating STA in “gold standard”
systems by treating it as part of the “ground truth”. Rather than attempting
to account for STA, researchers fix markers for “gold standard” systems
directly to the other system’s sensors, so that any STA impacts both systems
equally (Kamstra et al., 2022). Agreement between systems—but not the
underlying motion of the body—can thus be more easily measured.

###  5.2. Reliability

In 31% (N=73) of the papers we analyzed, concurrent validity was combined with
one or more forms of reliability assessment. Most often, this was a test-
retest reliability assessment, in which researchers confirm that repeated
measurements done of the same subject performing the same motion are highly
similar to one another (similar to the intra-observer method used to quantify
anthropometric errors: §4.1.1). 29% (N=68) of the total papers we examined
included such assessments. Inter-observer reliability, on the other hand, was
undertaken by just 3% (N=6) of all papers we reviewed.131313We note that we
did not consider manual or semi-manual motion analysis systems in our review.
These types of systems rely on humans manually annotating landmarks of
interest in 2D video data in order to measure kinematic aspects of the human-
annotated images. Validation of these systems typically does feature inter-
rater reliability metrics (e.g., (Herrington et al., 2017)). However, inter-
rater reliability remains an important metric for any system with skin-mounted
markers or sensors, even ones that do not rely on human annotation of data.
Those systems are still subject to inter-observer variation in marker
placements. For inter-observer reliability, researchers test whether their
methods reach similar results when conducted by different measurers (again
recalling anthropometric measurement practices). This may entail, for example,
calculating the similarity between gait parameters measured by experts in an
IMU-based system and clinicians who do not have experience with IMUs (Esser et
al., 2012), or examining consistency of measurements taken across different
hospitals (Cho et al., 2018).

###  5.3. Robustness

Less common, but still appearing in 9% (N=21) of all papers, was robustness,
which is taken to mean the ability of a system to provide consistent
measurements as its inputs change; e.g., as cameras move further away from
subjects or as time elapses since a system’s most recent calibration. To
understand whether systems are likely to work across different conditions,
researchers examine how well their systems perform with deliberately
introduced errors or noise, or with changes in sensor number, orientation, and
position (Pagnon et al., 2021). Other studies consider specific challenges
likely to be faced in the real world; for example, the robustness of a
workplace safety tool to occlusions—something of particular interest for
monitoring injury risk in workers who carry large objects (Steinebach et al.,
2020).

While these elements of validation are crucial for deploying safe systems,
they have not become a standard part of the social practice of validation. A
concurrent validity assessment without a robustness component tacitly assumes
that a system or technique will continue to be implemented by experts under
ideal (laboratory) conditions. However, laboratory setups do not accurately
reflect the multitude of environments in which motion capture systems are
beginning to be deployed. As these systems are increasingly used in home,
clinical, and employment settings, failing to consider robustness may put
vulnerable populations at additional risk.

###  5.4. Construct Validity

Researchers paid mixed attention to _construct validity_ , in this context
defined as a set of competences to evaluate whether or not a system
meaningfully performs its intended use case. We identified construct validity
assessments in 15% (N=34) of the studies we analyzed. This typically involved
‘discriminative validity’: is the system able to tell the difference between
things that are different (Grooten et al., 2018)? A common version of this
discriminative task is to evaluate whether systems can tell the difference
between movements conducted by “healthy” populations and those affected by
some disorder that could affect their movements (Fanton et al., 2022; Jakob et
al., 2021; Teufl et al., 2019; Grooten et al., 2018; Asaeda et al., 2018; Aich
et al., 2018; Summa et al., 2020). Rather than pure discriminative assessment,
some researchers measure whether a system provides clinically relevant
information by using it to create scores that can be compared to clinical
ratings, like the Foot Posture Index or the Unified Parkinson’s Disease Rating
Scale (Agostinelli et al., 2021; Tanaka et al., 2019; Mann et al., 2014;
McGinnis et al., 2015; Mentiplay et al., 2013; Galna et al., 2014).

###  5.5. User Studies

We identified just seven instances in which researchers conducted user
studies. Three teams assessed user comfort after multi-hour use of their
measurement systems (Buckley et al., 2023; Auepanwiriyakul et al., 2020;
Öhberg et al., 2021); one measured response to biofeedback provided by their
device as well as comfort (Bajpai et al., 2022). Other approaches included
validating that a system to assess athletic performance did not change
athletes’ behavior (Shepherd et al., 2017), and measuring whether the findings
of a system designed to measure muscle stress during lifting tasks aligned
with individuals’ perceptions of their own levels of effort (Hubaut et al.,
2022). The most in-depth user study we analyzed collected feedback from both
patients and healthcare providers on the deployment of an interactive remote
physical therapy system (Mishra et al., 2015).

###  5.6. Subgroup Validity

We found a disturbing lack of subgroup validity assessments in motion capture
validation – only 12% (N=29) of studies considered the relative accuracy of
their systems over people of different demographic or physical
characteristics. Most of these (N=18) considered the difference in performance
of their systems between “healthy” individuals and those with a specific
health status, like Parkinson’s disease or having previously had a hip
replacement (e.g. (Bravi et al., 2020; Guinet et al., 2021; Teufl et al.,
2019; Jagos et al., 2017)). Subgroup validation over other kinds of
demographic and physical characteristics was much rarer. Four papers
considered differential accuracy across participant sex (Peebles et al., 2021;
Hindle et al., 2020; Jung et al., 2023; Campagnini et al., 2023), three across
age groups (Eltoukhy et al., 2018; Auepanwiriyakul et al., 2020; Petró and
Kiss, 2022), and two across different body types and weights (Agostini et al.,
2017; Petró and Kiss, 2022). However, these studies represent the exceptions,
and not the rule.

Subgroup data was in fact rarely reported at all – only one of the studies
that we identified reported the racial breakdown of their participants (most
studies reported height, weight, and sex/gender expressed as a binary
variable). That study made a point of deliberately recruiting individuals of
varying races and body shapes, although sample sizes were small and they did
not explicitly assess performance over those groups (Fanton et al., 2022). In
many cases, however, subgroup-specific performance can be hugely important to
the potential validity of a tool. A team attempting to develop a device to
support low-cost approaches for stroke rehabilitation in Ethiopia, for
example, conducted a concurrent validation with a Vicon system in San
Francisco. They did not report on the racial breakdown of their subjects
(although they did report that 30/31 subjects were right handed), but a host
of work on subgroup validity in machine learning (e.g., (Buolamwini and Gebru,
2018; Wang et al., 2022; Banerjee et al., 2021; Seyyed-Kalantari et al.,
2020)) suggests the potential shortcoming of such an approach (Hughes et al.,
2019).

The validation of a system on a subject pool that is potentially very
different from the population on which it will be deployed has major
implications for the system’s ability to function in the real world. It also
reveals that these practices have and will continue to stabilize around skewed
demographics and differential system performance across groups. The few
studies that have tested subgroup validity underscore the seriousness of this
problem. Durkin and Dowling (Durkin and Dowling, 2003), for example, examined
whether body segment parameters systematically varied across intersectional
groups defined by age and gender (and deliberately recruited individuals with
diverse bodies within each group). They found significant between-group
variation, which remains unaccounted for in most formulas for estimating BSPs
(Durkin and Dowling, 2003). Despite this, the formulas that Dempster developed
on eight old, white, male cadavers continue to be treated as a ‘ground truth’
for calculating body segment parameters today, including by systems considered
by many to be ‘gold
standards’141414https://docs.vicon.com/display/Nexus215/Plug-
in+Gait+output+specification, archived at
https://web.archive.org/web/20230907011629/https://docs.vicon.com/display/Nexus215/Plug-
in+Gait+output+specification. Researchers have shown that the Dempster-derived
formulas do not generalize equally well to individuals who are not older white
men (Durkin and Dowling, 2003), but it is also clear that the formulas do not
generalize perfectly to individuals of _any_ demographic who are not dead. The
cadavers used in these studies had gone through a level of processing (e.g.,
freezing) that impacted their densities, likely changing their moments of
inertia in particular. Standard processes for taking anthropometric
measurements of live humans assume that they are standing and breathing. As
such, measurements like height or center of gravity might therefore be
systematically different between living individuals and cadavers (Cardoso et
al., 2016). Recalling how such assumptions are deployed in practice, and that
these systems are used in as varied settings as health diagnosis,
rehabilitation, and workplace safety surveillance, these practices will have
real-world impacts, where variation in body types and composition is obscured
and and present-day systems optimize for older, white, male (deceased) bodies
by design.

##  6\. Discussion

The errors we have outlined above correspond to specific, now-hidden
assumptions that ground modern instances of motion capture systems and can
have major impacts on those who interact with them. Assumptions about the
‘average’ automobile occupant have led to crash tests using dummies
approximating a normative male body, which affected automobile safety design
in ways that have led to higher injury rates for female occupants (Linder and
Svensson, 2019; Perez, 2019). Similarly, assumptions about the biological
basis of race have lead to racial discrimination in technologies used for
medical diagnosis (Braun, 2014). Worker safety protocols that make assumptions
about certain kinds of bodies and movements create risks of occupational
injury for those who do not conform to that model (Messing et al., 2022). Each
of these cases results—at least in part—from assumptions about subgroup
validity. Specifically, when designers assume that a model of an entire
population is valid for discrete subpopulations, they place those
subpopulations at risk.

In the case of motion capture, our analysis—conducted through the lens of
social practice theory—reveals the dearth of subgroup validity testing within
the practice of motion capture. Additionally, it identifies the process
through which chains of concurrent validation link even contemporary motion
capture systems through the use of ‘gold standards’ to the early estimations
of body segment parameters based on only a few white, ‘athletic,’ male
cadavers. Our systematic review of how error is treated in the motion capture
literature reveals how the elements (materials, competences, and meanings) of
measurement and validation practices point to precisely these assumptions and
how they are embedded in motion capture systems, in ways that potentially lead
to real-world harms.

##### The histories of social practices are crucial to understanding present-
day technologies.

This work offers a template for the theoretical analysis of social practices,
connecting to existing calls for critical historical perspectives (Bødker et
al., 2023). A social practice theory approach allows us to reveal
assumptions—particularly those that assert that a small, privileged group can
stand in for the whole—and examine how they have become stabilized and
infrastructural as part of technology design. This lens allows us to
understand how assumptions achieve significance and permanence in
sociotechnical systems.

The context of body measurement, generally, and motion capture, specifically,
makes the stakes clear. In the 19th century, Samuel Morton and Louis Agassiz
exerted great effort to demonstrate inherent racial differences through
measurements of human cranial capacity, skull features, and height (Menand,
2001; Gould, 1996), and Francis Galton attempted to infer character traits
like sexuality or criminality from facial features (Sekula, 1986). Long-since
dismissed as pseudo-science, the implications of their work (e.g., racial
differences between cranial capacity and intelligence) have been far more
durable in public imaginaries of race and bodies – despite both these
measurement practices and their implications also being debunked. This is
illustrated by the persistence of arguments about race, cranial capacity, and
intelligence (Winston, 2020), as well as by efforts to revive physiognomy
through artificial intelligence (Stark and Hutson, 2021).

Given the dramatically limited efforts to ensure subgroup validity for motion
capture technologies identified above, it becomes clear that efforts to apply
motion capture to physiognomic goals, whether it be pose estimation or so-
called ‘emotion recognition,’ are deeply fraught and unlikely to generalize,
even before considering the epistemic bankruptcy of physiognomy. Indeed, a
suite of recent research projects dedicated to inferring interior states from
motion and pose (Rasmussen et al., 2023; Ahad et al., 2021; Lusi et al., 2017;
Yang et al., 2020; Kilic and Tuceryan, 2023) demonstrate both the durability
of historical fantasies of physiognomy but also the need to interrogate such
projects’ underlying assumptions through sociotechnical audits and to address
the pernicious (Sloane et al., 2022), harmful implications of such research
(Stark and Hutson, 2021), long before they approach deployment.

##### Errors point to elements of social practices, assumptions, stakes, and
potential interventions

Attention to the social practices of measurement and validation is of critical
importance as motion capture approaches proliferate. However, algorithmic
systems that use ‘gold standard’ approaches to validate their outputs
propagate the forms of validity held by that gold standard. While the stated
motivation of these validation practices are to show that systems work or will
be useful, the elements of validation instead stabilize around tasks that are
easier to demonstrate. Motion capture is increasingly being developed as
marker-free systems that can be deployed at low cost and in public settings.
Systems that over- or underestimate the risk of injury in labor-intensive jobs
or lead to misdiagnoses of medical conditions, for example, present clear risk
of harms; more broadly such designs will likely put already-marginalized
populations at greater risk (Bennett and Keyes, 2020; Ymous et al., 2020).

Identifying populations who are excluded or misrepresented by measurement and
validation practices within the context of motion capture can identify
contexts where such systems should not yet be deployed without additional
testing, or where they should not be deployed at all. This may be particularly
relevant for applications where training data is generated by actors
portraying medical conditions or physical activities that are not ‘genuine’
instances of these phenomena. While one might assume that actors’ portrayals
of a condition—for example, one’s likelihood of falling (see §4.3.1)—are
reasonable proxies for ‘the real thing’, these assumptions require
interrogation before they are deployed in any use case. A key lens through
which to interrogate these assumptions comes from the fields of disability
studies and assistive design. The former underscores the significance of lived
experience of people with disabilities and their intersectional identities in
technology design (Schalk, 2022; Williamson, 2019; Hamraie, 2017), pointing
out that people with disabilities are often already technology designers
(Hamraie and Fritsch, 2019). The latter relies on user-centered design
principles that emphasize the importance of user involvement in the design
process – in other words, making clear that “designers cannot play the role of
disabled users, even if they try to recreate their conditions of life”
(Thomann et al., 2016).

##### Social practices yield key insights into the design of systems and the
study of interactions

It is well established in HCI research that technologies and systems change
human behavior and vice versa (Klemmer et al., 2006; Coeckelbergh, 2019). As
we interact with our social world through technologies, we continually adapt
and comport ourselves differently with that knowledge in mind – and this
affects how we, quite literally, move through the world. For example, a person
who switches from an analog to a smartwatch is likely to adapt their ‘watch-
checking motion’ in order to activate the smartwatch screen. Social practice
theory emphasizes exploring how that change in human-machine interaction
occurs and becomes a stable phenomenon that we can observe in the world. The
premise is that interactions and behaviors change first the elements of
individual practices, and then social practices at large. Rather than
exploring just (technology design) intentions on the one hand and (technology
design) impacts on the other, the study of the dynamics of technology design
and interactions moves decidedly into focus, and with it, a view into the
assumptions that underpin them. As our analysis of the assumptions
underpinning measurement and validation practice in motion capture technology
has shown, assumptions can become solidified as they are baked into the
material nature of a technology practice. This conceptual approach is
particularly useful for understanding how ‘bias’ enters hardware-driven
systems—such as sensor-centric technologies like motion capture, volumetric
technology, or other camera-based systems—and becomes an infrastructural
condition.

##### Accountable technologies require understanding social practices

In interrogating social practices of measurement and validation, the
assumptions about what constitutes measurement, error, and validity can
contribute to socio-technical audits of technology to ensure it has adequate
epistemological grounding (Sloane et al., 2022; Rhea et al., 2022) for its
context of use.

To this end, understanding the social practices of measurement and validation
for motion capture are necessary precursors for developing a sociotechnical
audit of motion capture systems (Sloane et al., 2022). Understanding
assumptions made about bodies by motion capture systems (the measurements
needed to define them, how their skeletons and soft tissues move, how they
vary systematically across a population) can aid the design of such audits,
directing attention toward whether the use of golden standards in validating a
system was justified or appropriate and whether motion capture systems perform
as expected for subjects of varying body types, physical abilities,
demographics, and so on (i.e., whether the system displays subgroup validity).
Fleshing out assumptions is the basis for more holistic AI audit approaches
that push beyond questions of disparate impact (Sloane, 2021) and that center
on the question of a system’s stability and its status of a valid measurement
instrument (Sloane et al., 2022; Rhea et al., 2022; Sloane, 2022; Ma et al.,
2023). In other words, focusing on assumptions allows for AI audits that are
not purely technical in nature, but that examine whether an AI functions in
alignment with its intended purpose while simultaneously inspecting the
validity of the principles underpinning its design (Sloane et al., 2022;
Jacobs and Wallach, 2021).

Many data-driven technologies other than motion capture incorporate their own
set of social practices for measurement and validation. The work presented
here provides a model through which these social practices can be
interrogated, historically and ethnographically. Researchers and technologists
who wish to use the social practice theory lens for the design or assessment
of technologies can start off with the following questions: What are the key
social practices comprising the sociotechnical system at play (for example,
algorithmic risk assessments or facial recognition technology)? What are the
materials, competences, and meanings of these practices? As these elements
link up and stabilize the practice, what assumptions are simultaneously
stabilized? Where do these assumptions come from and they potentially harmful?
The visuals provided in this paper may be of additional help for this
exercise. Peeling back the layers of social practices in technology in this
way cannot only help with executing more holistic AI audits, it can also, for
example, help to systematically assess AI ethics interventions (Sloane and
Zakrzewski, 2022) or find effective ways of complying with AI regulation
(Sloane and Moss, 2022).

##  7\. Conclusion

We offer a template for the theoretical analysis of social practice in the
design and use of technologies. By focusing on the social practices of
measurement and validation for motion capture, we connect with broader
histories of social and technical representations of bodies while shedding
light on the tenuous foundations of modern systems. We uncover key stabilizing
moments of measurement and validation across three historical eras and propose
a typology of errors, revealing shifting attention and inculcation of past
elements through validation practices that entrench, rather than challenge,
them. This type of analysis can reveal how assumptions built into technologies
shift, stabilize, and become infrastructural in the design and use of
technologies.

###### Acknowledgements.

This work was supported by funding from the Notre Dame-IBM Technology Ethics
Lab. We thank Todd Bryant, Luke DuBois, Kaustav Sarkar, and Harsh Palan for
their invaluable explanations of motion capture processes.

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