# FingerMapper: Mapping Finger Motions onto Virtual Arms
to Enable Safe Virtual Reality Interaction in Confined Spaces

Wen-Jie Tseng  LTCI, INFRES, Telecom Paris, IP ParisPalaiseauFrance wen-
<EMAIL_ADDRESS>,  Samuel Huron  CNRS i3 (UMR 9217), SES, Telecom
Paris, IP ParisPalaiseauFrance<EMAIL_ADDRESS>,  Eric Lecolinet
LTCI, INFRES, Telecom Paris, IP ParisPalaiseauFrance eric.lecolinet@telecom-
paris.fr and  Jan Gugenheimer  TU DarmstadtDarmstadtGermany
LTCI, INFRES, Telecom Paris, IP ParisPalaiseauFrance jan.gugenheimer@tu-
darmstadt.de

(2023)

###### Abstract.

Whole-body movements enhance the presence and enjoyment of Virtual Reality
(VR) experiences. However, using large gestures is often uncomfortable and
impossible in confined spaces (e.g., public transport). We introduce
FingerMapper, mapping small-scale finger motions onto virtual arms and hands
to enable whole-body virtual movements in VR. In a first target selection
study (n=13) comparing FingerMapper to hand tracking and ray-casting, we found
that FingerMapper can significantly reduce physical motions and fatigue while
having a similar degree of precision. In a consecutive study (n=13), we
compared FingerMapper to hand tracking inside a confined space (the front
passenger seat of a car). The results showed participants had significantly
higher perceived safety and fewer collisions with FingerMapper while
preserving a similar degree of presence and enjoyment as hand tracking.
Finally, we present three example applications demonstrating how FingerMapper
could be applied for locomotion and interaction for VR in confined spaces.

FingerMapper; Confined Spaces; Body Re-Association in VR

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††price: 15.00††doi: 10.1145/3544548.3580736††isbn:
978-1-4503-9421-5/23/04††ccs: Human-centered computing Virtual reality

Figure 1. (a) We introduce FingerMapper that leverages (b) small and less
physically demanding finger motions to control a virtual arm. (c) We envision
that the user can use FingerMapper for VR in confined spaces (e.g., the
passenger seat of a bus) while allowing for virtual arm interactions with
fewer collisions. FingerMapper is available for a variety of interaction
styles: (d) swinging motions in slicing-style games, (e) object interaction,
and (f) locomotion in climbing-based games.

Figures 1a and 1b show FingerMapper, an interaction that uses small finger
motions to control the whole-body scale virtual arm in VR. Figure 1c depicts
that FingerMapper enables VR interaction when the user locates in confined
spaces like commuting in a bus. FingerMapper can also be integrated with
different VR interactions that are normally done by whole-body interaction,
including arm-swinging motion (1d), object interaction (1e), and locomotion
(1f).

##  1\. Introduction

Current Virtual Reality (VR) experiences often require whole-body movement to
increase presence (Slater et al., 1998; Usoh et al., 1999). Whole-body
movement is a significant factor in engagement and sensory immersion (Isbister
and Mueller, 2015), suggested by research on physicality in games. Although
having whole-body movement in VR enables a higher presence and enjoyment,
these techniques also require a larger physical space (Marwecki et al., 2018).

As VR Head-Mounted Displays (HMDs) are getting more mobile (e.g., Oculus
Quest), users can immerse themselves wherever they wish. One prominent example
would be using VR in transportation (e.g., planes (Williamson et al., 2019),
cars (Hock et al., 2017; McGill et al., 2017), and others (McGill and
Brewster, 2019; McGill et al., 2020)). However, these scenarios often happen
inside confined spaces (Gugenheimer et al., 2019; Li et al., 2021), where
whole-body movement is usually undesirable since VR users do not have enough
space for interaction and could even accidentally collide with bystanders or
surroundings.

In this paper, we present FingerMapper—a concept that maps small-scale finger
motions onto large-scale virtual arm motions (Figure 1ab). FingerMapper
enables VR experiences in confined spaces (e.g., the passenger seat in a bus,
Figure 1c) with fewer physical motions and collisions while preserving
presence and enjoyment. Although confined spaces are unavailable for whole-
body movements, we can benefit from the beyond-real interaction in VR (Abtahi
et al., 2022) by creating mapping metaphors between the VR user’s finger and
the virtual arm. Additionally, users can learn different relationships between
tracked and rendered motion in VR to control an avatar (Won et al., 2015). We
implement two design variations (_Attach_ and _Direct_) and present three
applications (Figure 1d-f) that support VR interaction, including large
swinging motions (FingerSaber), object interaction in space (FingerGrabber),
and locomotion (FingerClimber).

To understand the usability of FingerMapper, we first conducted a within-
subject user study (n=13) including both design variations (Attach and Direct)
with two baselines (hand tracking and ray-casting) in a target selection task.
While results show that FingerMapper had a higher task completion time and
lower Virtual Body Ownership (VBO) compared to hand tracking, both mappings
reduce the physical path length of participant’s hands and fatigue. Next, we
conducted a user experience study in a confined space (n=13) where
participants played FingerGrabber with FingerMapper (Attach) and hand tracking
inside the front passenger seat of a car. Results show that FingerMapper had
fewer collisions and a similar degree of presence and enjoyment to hand
tracking. Overall, participants reported a significantly higher perceived
safety using FingerMapper in a confined space compared to hand tracking. Most
participants (10 out of 13 in Study 2) reported they prefer using FingerMapper
in confined spaces to avoid colliding and keep feeling safe during the
interaction. We see FingerMapper as an alternative for scenarios where the
user wants whole-body motion without having the necessary physical space for
interaction. Finally, we discuss the potential scenarios of using FingerMapper
for future VR experiences (e.g., long-duration usage, context adaptive input).

Our work has three contributions. 1) The concept and implementation of
FingerMapper leveraging small finger motions onto virtual arms and hands to
enable VR interaction in confined spaces. 2) Insights from two studies show
that FingerMapper has less physical movement, less fatigue, and fewer
collisions compared to hand tracking. Participants also reported a similar
degree of presence and enjoyment, a higher perceived safety, and preferred to
use FingerMapper in confined spaces (10 out of 13 in study 2). 3) The
implementation of three applications (FingerGrabber, FingerSaber, and
FingerClimber) that demonstrate how to integrate FingerMapper for different
interaction scenarios.

##  2\. Related Work

Our work is related to confined spaces in VR and improving ergonomics for VR
interaction. For the second subsection, we discuss prior research based on the
framework of Abtahi et al. (Abtahi et al., 2022).

###  2.1. Confined Spaces as a New VR Context

The prevalence of VR products (e.g., HTC Vive, Oculus Rift) pushes the VR
experience from a well-controlled environment (e.g., laboratory, demo booth)
to the user’s private space. Plenty of new contexts emerge while people use VR
technologies in their homes. One prominent context is social interaction, for
example, including bystanders in the VR experience (Gugenheimer et al., 2017,
2018) or managing spaces between VR and non-VR users (Yang et al., 2018).
Recent HMDs such as the Oculus Quest are mobile and standalone systems. They
are easier to be carried around to new environments. Prior research has
explored using VR in different forms of transportation (e.g., planes
(Williamson et al., 2019), cars (Hock et al., 2017; McGill et al., 2017), and
others (McGill and Brewster, 2019; McGill et al., 2020)).

In this work, we focus on the confined space, where a user cannot fully extend
one’s limbs. This includes cars, planes, and other forms of public
transportation. An example would be maintaining presence and productivity in
VR while the user is confined in the rear seat of a car (Li et al., 2021). In
these cases, users are usually restricted to a small physical space (e.g., car
and shuttle) or surrounded by passengers (e.g., train), where whole-body
motion is almost impossible. We explore how to keep presence and enjoyment in
VR experiences within the context of the confined space by using beyond-real
interaction—mapping finger motions onto virtual arms.

###  2.2. Improving Ergonomics for VR interaction with Hands and Arms

#### Reality-Based and Illusory Interaction

Directly using reality-based interaction (e.g., manipulation using hand
tracking) benefits from our real-life experiences. Compared to ray-casting
with controllers (or other metaphors), manipulating with hands is more natural
and intuitive. Hand tracking benefits from the Degrees of Freedom (DoF) in
manipulating virtual objects. Therefore, it becomes an ideal input for VR. On
the contrary, ray-casting needs additional extensions to allow for rotation
and retraction (e.g., controlling the depth of the ray (LaViola et al., 2017))
because the manipulation of ray-casting is not hand-centered. Additionally,
simulating whole-body movements we have in the real-world experience also
enhances presence (Slater et al., 1998; Usoh et al., 1999) and engagement
(Isbister and Mueller, 2015) in VR.

Although reality-based interaction is natural, they sometimes cause fatigue
and require physical space for whole-body motions. Prior works leverage visual
dominance over proprioceptive cues (Burns et al., 2005) to create interaction
techniques that reduce fatigue without breaking the Sense of Embodiment (SoE)
and the reality-based metaphor (i.e., illusory interaction in (Abtahi et al.,
2022)). SoE has three working constructs: self-location, sense of agency
(being able to act and control), and body ownership (Kilteni et al., 2012).
Recent research investigates how to reduce the physical path length and
fatigue of limbs within the maximum arm’s reach while preserving VBO. Examples
include redirecting visual targets (Montano Murillo et al., 2017), gradually
directing the user’s hand to a comfortable posture (Feuchtner and Müller,
2018), or creating an offset between the physical and virtual hand (Li et al.,
2018; Wentzel et al., 2020). However, these techniques still require
sufficient physical space for whole-body motions and may be unavailable in the
confined space.

Using a self-avatar with an input metaphor similar to reality enhances the
sense of presence and embodiment in VR. High-fidelity avatars have better
distance estimation in the near field (Ebrahimi et al., 2018), and it takes
time for VR users to adapt to the dimensions and capabilities of self-avatars
in VR (Day et al., 2019). Although using a self-avatar has been state-of-the-
art, recent research explores avatars beyond reality. Won et al. (Won et al.,
2015) studied how participants control an extended third arm controlled by
feet in VR and showed they could learn and achieve higher task performance. In
a recent study, Kato et al. (Kondo et al., 2020) showed using visual-motor
synchrony can enable small VBO when mapping the right-hand thumb motion to the
left virtual arm in VR. FingerMapper is inspired by mapping smaller finger
motions to the virtual arm. Our approach can be an alternative when
interacting in a confined space but the user still wants to preserve SoE.

#### Beyond-Real Interaction as a Solution for the Confined Space

VR interactions can go further beyond replication of reality (Abtahi et al.,
2022). Previous research explores the beyond-real concept to enable distant
interaction (Poupyrev et al., 1996; Feuchtner and Müller, 2017) and object
manipulation (Frees et al., 2007). More recent examples aim at reducing
physical movements also fatigue for tapping (McIntosh et al., 2020) and target
selection tasks (Schjerlund et al., 2021). FingerMapper reduces physical path
length and fatigue within the maximum arm’s reach of the VR user. Inspired by
the beyond-real concept, we focus on mapping small-scale finger motions onto
the virtual arms for an energy-efficient interaction, reducing physical
movements to avoid fatigue for VR and collisions inside confined spaces. In
addition, we are inspired by the small-scale mid-air gestures like Gunslinger
(Liu et al., 2015), which redirects a ray-casting interaction from an energy-
efficient position at the user’s hip towards a pointing gesture. Dukes et al.
(Dukes et al., 2013) design a novel 3D interaction that scales the stroke
survivor’s impaired arm movements into full movements in VR to provide expert
action observation for post-stroke neurorehabilitation. Although these
approaches are not resolving physical constraints, FingerMapper shares the
concept of mapping smaller motions to more physically demanding interactions.
When the physical space is limited, FingerMapper maps smaller finger motions
to the virtual arm to enable a whole-body experience in VR. By reducing
physical movements, we expect FingerMapper to have fewer collisions and
enhance the VR user’s perceived safety.

##  3\. Interaction Design

Our design is to enable virtual hands and arms for VR interaction in confined
spaces. Hand tracking and ray-casting are unavailable because the former may
obstruct the surroundings, and the latter only provides a pointing metaphor.
FingerMapper aims to have the same functionality as hand tracking—having arm
motion for object interaction—but less physical motion and fatigue.

###  3.1. Mapping Functions

Two mapping functions (Attach and Direct) for controlling the virtual arm
using finger motions were implemented. The maximum arm reach was represented
by a sphere having an origin at the shoulder ($P_{s}$) and a radius of the
user’s arm length ($l_{arm}$, Figure 2a). We set $l_{arm}$ (from shoulder to
wrist) as 60 cm according to the average arm length (Pheasant and Haslegrave,
2005). The goal was to use hand tracking data (e.g., position and rotation of
finger joints and wrist) to calculate the virtual wrist position ($P_{w}$) and
create virtual arm movements that can cover the VR user’s maximum arm reach.

Figure 2. (a) Attach casts a ray along the proximal phalanx to the maximum arm
reach sphere. (b)(c) When user bends their finger, we compute a retraction
fraction with $d$ to retract the virtual wrist towards shoulder.

Figure 2a depicts the implementation of Attach mapping. We casts a ray along
the proximal phalanx to a sphere, whose origin is attached on the user’s
shoulder and its radius is the user’s maximum arm length. The wrist of a hand
model locates at this casting point. Figures 2b and 2c show the user retracts
the virtual arm toward the shoulder by bending their index finger.

#### Attach: Casting the virtual wrist onto the surface of the maximum arm
reach.

The Attach mapping design was based on homuncular flexibility in that humans
are flexible at learning a different relationship between tracked and rendered
motion (Won et al., 2015). First, we cast a ray along the direction from the
metacarpophalangeal (MCP) joint to the proximal interphalangeal (PIP) joint.
This ray reaches the surface of the maximum arm reach sphere (Figure 2a). We
chose the proximal phalanx since it is less intervened when bending the finger
inwards and called this intersection point the casting position ($P_{c}$). The
wrist of a hand model locates at $P_{c}$. Here, the user’s index finger is in
the pointing gesture, so the virtual arm is also fully stretched. They can
move their proximal phalanx of the index finger or their wrist to cast $P_{c}$
on the surface of the maximum arm reach, changing the virtual arm’s direction
and position.

Next, to reach positions inside the maximum arm reach sphere, we retracted the
virtual wrist along the direction between the casting point ($P_{c}$) and the
shoulder ($P_{s}$). A retraction fraction $r$ was the distance between the
fingertip and MCP joint ($d$) divided by the length of the index finger. As
the user bends their index finger, $r$ decreases and the virtual wrist moves
closer to the shoulder (Figure 2bc). Because the virtual wrist should not pass
through the VR user’s shoulder, the value of $r$ ranges from 0.15 to 1.
Therefore, the virtual wrist position ($P_{w}$) was the retraction amount
multiplied by the arm length and the unit vector from the shoulder position to
the casting point (Equation 1). Note that $P_{c}$ and $P_{w}$ are identical
when $r$ equals one. While the user retracts their index finger, $P_{w}$ moves
toward the user, along the direction from $P_{c}$ to $P_{s}$.

(1) $P_{w}=P_{s}+r\cdot l_{arm}\cdot\hat{\mathbf{v}}_{cs},\ where\ r\ is\
0.15<r<1\ and\ \mathbf{v}_{cs}=P_{c}-P_{s}$

Finally, because Attach only calculates $P_{w}$, we compute the direction of
the virtual upper and lower arm by solving inverse kinematics, in which
$P_{w}$ is the target, and $P_{s}$ is the origin. We constrained the virtual
upper and lower arm movements according to human arm anatomy. The idea is to
have more natural virtual arm movements. We applied 1€ Filter (Casiez et al.,
2012) to smoothen virtual arm movement.

Figure 3. (a) Direct maps two vectors, the MCP-PIP and the PIP-fingertip to
the virtual upper and lower arms. When the index finger is fully stretched,
the virtual arm follows the direction of two vectors and becomes fully
extended, (b) bent, and (c) retracted closer to the body.

Figure 3a shows the direct mapping in which the virtual arm angle follows the
direction of two vectors on the index finger. Figures 3b and 3c illustrate
when the user bends their index finger, the virtual arm moves accordingly and
retracts closer to the shoulder.

#### Direct: Re-associating finger segments to virtual arms.

The Direct mapping function is inspired by body re-association and the notion
that providing visuomotor synchrony could induce a portion of VBO (Kondo et
al., 2020). By using the similarity of index finger joints and virtual arm
joints, users control the virtual arm movements by bending their index
fingers. The idea is to use this similarity of posture between finger and arm
to give the user a better understanding of agency and VBO.

Two vectors, MCP to PIP joint (${\mathbf{v}_{1}}$) and PIP joint to fingertip
(${\mathbf{v}_{2}}$), are mapped onto the upper and lower virtual arms (Figure
3a). When the user’s index finger is straight, the virtual arm also remains in
the same direction and reaches the maximum arm reach. While the user bends
their index finger (Figure 3b), the virtual arm follows the direction of two
vectors on the index finger and bends inwards simultaneously. Equation 2 shows
the virtual wrist position ($P_{w}$) is the sum of ${\mathbf{v}_{1}}$ and
${\mathbf{v}_{2}}$ from the shoulder position ($P_{s}$). We scaled up by
multiplying half of the arm length ($l_{arm}$).

Figure 3c shows the arm length decreases by a fraction $r$ as the retraction
amount of virtual arm length while bending the finger. Here, $r$ equals the
distance between the fingertip and MCP joint divided by the length of the
index finger, ranging from 0.15 to 1. Because the virtual upper and lower arm
lengths are equal, the new length equals $r$ multiplied by half of the arm
length ($l_{arm}/2$). The virtual wrist position ($P_{w}$) becomes the sum of
two unit vectors of the index finger and multiplies the retracted arm length
from $P_{s}$ (equation 2). The user can control the virtual arm direction by
moving their fingers, and the whole virtual arm rotates when the user rotates
their wrist along the direction of the index finger. The difference between
Attach is that the arm direction follows the index finger’s angle. Our
implementation applied 1€ Filter (Casiez et al., 2012) to hand tracking data
for smoothening virtual arm motions.

(2)
$P_{w}=P_{s}+r\cdot(l_{arm}/2)\cdot(\hat{\mathbf{v}}_{1}+\hat{\mathbf{v}}_{2}),\
where\ r\ is\ 0.15<r<1$

###  3.2. Spatial Extension

While Attach and Direct enable virtual arm movements in three dimensions
within the maximum arm’s reach, we found that the sense of agency over the
virtual arm breaks when moving the wrist without the corresponding virtual
wrist movement. We observed this effect during the early prototyping process
of FingerMapper. The sense of agency is a construct of being able to control
and manipulate, and it is sensitive to any temporal discrepancies between the
execution of a self-generated movement and visual feedback (Kilteni et al.,
2012). Therefore, we developed a spatial extension function that changes the
virtual arm length to create virtual wrist movements, maintaining the sense of
agency and overall SoE while using FingerMapper. We applied this function to
both Attach and Direct.

The spatial extension was implemented with a similar approach to the Go-Go
technique (Poupyrev et al., 1996). The Go-Go technique is designed for distant
interaction in the virtual environment (from mid-range to far-range) and
requires enough space for whole-body movement (need to extend arm). On the
contrary, FingerMapper enables virtual arm interaction in the “close and mid-
range” space (within arm span) using mainly finger mapping. The two techniques
have distinct design objectives. In FingerMapper, users can move only their
fingers to reach a position in space and their wrists to support their motion.
The observed behavior often consists of both. Depending on the context of
usage, one of them would become more dominant but never exclude the other.
FingerMapper only applies spatial extension to preserve the sense of agency
while interacting within close and mid-range. The part of Go-Go is only
triggered during larger motions, and the close-range interaction is supported
only by FingerMapper. The Go-Go technique in a confined space would become
hand tracking in our scenario.

Figure 4a shows the default posture that the user takes a sitting position and
fixes their elbows to both lateral sides of the waist. The default posture
simulates using VR in a confined and restricted interaction space. The sitting
shoulder height is 20 cm below the eye, and the sitting elbow height is 33.5
cm below the shoulder, approximated using the average body dimensions
(Pheasant and Haslegrave, 2005). The chest position is 37 cm below the HMD,
and the chest height is in the middle between the sitting shoulder and elbow
height. We calculate the projected distance from the physical wrist to the
chest ($R$), as shown in Figure 4b. This distance is projected onto the plane
that always has its normal vector pointing upwards from the user’s head since
we need the forward and backward spatial offset. The projected distance ($D$)
of default posture is the criterion of spatial extension. We set the value of
$D$ as 18 cm through our first experimentation. As shown in Figure 4c, when
the user’s hand is within the range of $D$, The spatial offset is $D-R$. When
the user extends their hand out of the range of $D$ (Figure 4d), we compute
$R_{new}$ using the Go-Go function shown in equation 3. The spatial offset
then becomes $R_{new}-D$. These design decisions of the spatial extension were
made during the implementation process within the group of authors. The offset
is added to the radius of the shoulder sphere of Attach. Therefore, the user
can perceive the arm extending or shortening when they move their hand forward
and backward. For Direct, we increase or decrease the arm length by this
offset.

Figure 4. (a) The default posture of FingerMapper. (b) $R$ is the projected
hand-to-chest distance, and $D$ is the default distance for calculating
spatial offset. (c) When user moves their hands in the range of $D$, we reduce
the virtual arm length by the spatial offset (the blue arrow). (d) Otherwise,
we extend $R$ to $R_{new}$ through our extension function and increase the
virtual arm length.

Figure 4a shows the default posture where a VR user sits in a confined space
and places elbows to their waist. Figures 4b, 4c, and 4d depict spatial
extension function for preserving the sense of agency while using Attach and
Direct. When the user’s wrist exceeds distance D (18 cm), we extend the
virtual arm length to match the wrist movement.

(3) $R_{new}=\left\\{\begin{array}[]{ll}R&{if\ R\ <\ D}\\\
R+k(R-D)^{2}&{otherwise},\ k\ =\ 0.6\end{array}\right.$

###  3.3. Finger Motion

In an early prototype, we experimented with a rigid hand model as the virtual
hand that either points or grabs. However, this static representation felt
more awkward since the virtual hand model did not resemble the finger motions
we have in reality. To increase the sense of agency and VBO, we map the
tracking information of physical fingers to virtual fingers. Although index
fingers are occupied for positioning the virtual arm in space, the current
implementation still has more precise finger movement at this new location and
performs simple manipulations.

Figure 5. (a) ThumbButton uses the distance between the thumb fingertip and
middle finger knuckle to select. (b) GrabSelect uses the metaphor of holding
an object.

Figures 5a and 5b show two selection techniques for FingerMapper, ThumbButton,
and GrabSelect. ThumbButton uses the distance between the thumb fingertip and
middle finger knuckle as a threshold to trigger selection. GrabSelect is a
metaphor like grabbing an object with a selection threshold calculating the
average distance from the fingertips of the middle, ring, and pinky fingers to
the wrist.

###  3.4. Selection

Selection is essential in VR interaction. Despite having the actual finger
tracking data attached to the virtual hand, the current implementation
partially impedes the ability to select objects with a pinch since bending the
index finger also moves the virtual arm. To overcome this limitation, we added
functionality based on two hand gestures (_ThumbButton_ and _GrabSelect_) to
enable selection with FingerMapper.

In _ThumbButton_ , the user moves their thumb to touch the knuckle of the
middle finger, similar to pressing a button (Figure 5a). We track the distance
from the thumb fingertip to the middle finger knuckle (PIP joint). The user
triggers selection once detecting the thumb fingertip contacts the middle
finger knuckle. Figure 5b shows _GrabSelect_ , where the user holds their hand
with the middle, ring, and pinky fingers at the same time. We compute an
average distance from the middle, ring, and pinky fingertips to the wrist,
leveraging the metaphor of grabbing an object to trigger selection.

We tested both trigger mechanisms within the group of authors during the
implementation process of FingerMapper to make design decisions. While
performing the motion in GrabSelect feels more natural and closer to grabbing
an object, tucking in the middle, ring, and pinky finger results in a higher
distortion of the index finger position. These joint movements with the index
finger are because the fingers are controlled by the same muscle group
(Schwarz and Taylor, 1955). This distortion causes the Heisenberg effect of
spatial interaction (Wolf et al., 2020)—a user points or selects virtual
content using spatially tracked input devices, and a discrete input such as a
button press disturbs the tracking position, resulting in a different
selection point. Here, GrabSelect disturbed the index finger position, so this
effect exists. Therefore, We selected ThumbButton since the muscle group
involved in the thumb motion is independent of the index finger, resulting in
less disturbance of the selection.

###  3.5. Hardware Requirement

Our current setup requires 6-DoF tracking on the wrist, joints, and
fingertips. We use Oculus Quest hand tracking to acquire all the necessary
position and rotation data. The concept is also compatible with other VR
systems providing similar hand-tracking data.

##  4\. Applications

We highlight three usage scenarios of FingerMapper with different interactions
(e.g., object interaction, arm swinging motion, and locomotion). The goal is
to show that when the physical environment is unavailable for whole-body
movement, the user can switch to FingerMapper as an input metaphor for
confined space. Three applications were originally designed for whole-body
interaction. We only replace the regular hand tracking (or controllers) with
FingerMapper to show VR users can interact in a confined space using
FingerMapper in different scenarios.

###  4.1. FingerGrabber

FingerGrabber is an application inspired by VR sandbox games like Job
Simulator. The interaction in this type of game is often selecting, grabbing,
and manipulating any object in the virtual environment. This interaction style
can represent any generic selection and manipulation task a user has to
conduct in VR. The virtual objects in these VR applications are usually
scattered within the arm span of the user. Here, we argue that FingerMapper
could be a good solution by reducing physical movement to enable the gaming
experience in confined spaces.

Since such games often strive for a well-written narrative, FingerGrabber
added game mechanics to have a more engaging experience. The user is in front
of a pit that is successively filled with different types of balls (e.g.,
basketball, volleyball, and football shown in Figure 6a). Figure 6b shows that
the user grabs each ball and place it in one of the three corresponding
baskets. We implement the game using Attach mapping and _ThumbButton_ ,
allowing the player to conduct the full range of interactions necessary for
this type of game, i.e., select, grab, and manipulate.

Figure 6. (a) FingerGrabber is a sandbox that the player selects and
manipulates objects surrounding them (e.g., in our application, basketball,
volleyball, and football). The player has to select the ball using Attach
mapping and put (b) the ball into the baskets in VR.

Figure 6a illustrates FingerGrabber, where the VR user selects three types of
balls (e.g., basketball, volleyball, and football) and puts them into the
corresponding bucket. Figure 6b shows a user manipulates an object with
FingerMapper, enabling object interaction in VR.

###  4.2. FingerSaber

The VR rhythm game, BeatSaber, inspired us to use fingers to perform the arm
swing motion (Figure 7a). In BeatSaber, different colored cubes move toward
the user and follow the beats of the music. The user’s goal is to slice every
cube in the direction indicated on them. The original concept of BeatSaber
revolves around having a whole-body experience with large arm movements and
sometimes even moving their whole body. To enable FingerSaber, we chose the
Attach mapping and positioned two virtual lightsabers in the user’s virtual
hand. Since no selection is needed, we kept the lightsabers constantly
attached to the user’s hand without using a grabbing motion. Our technique
enables the BeatSaber experience to work using small finger motions instead of
large arm motions without modifying the game mechanics.

Figure 7. (a) We use Attach mapping to control virtual arms with lightsabers
for swinging motion. (b) The user has to slice cubes in the correct direction
and corresponding color.

Figure 7a depicts FingerSaber, in which the VR user moves the index finger to
replace arm-swinging motions with FingerMapper. Figure 7b shows the goal is to
slice every incoming cube in the indicated order.

Figure 8. (a) The user selects a grip with GrabSelect. (b) While gripping, the
user bends the finger to pull the body upwards and create a vertical
displacement in VR.

Figure 8a shows FingerClimber, where a VR user performs locomotion with
FingerMapper. Figure 8b depicts that the VR user climbs up by selecting
different climbing grips with our technique.

###  4.3. FingerClimber

FingerClimber is inspired by current VR games like The Climb, where the user
goes to the top of a mountain using different climbing grips attached to the
cliff. In this game, the user usually stretches their whole arm and sometimes
the whole body. We used Direct and hoped this mapping would help the user to
control the specific shape of the virtual arm. The goal is to avoid users
climbing with constantly stretched-out arms. As the selection mechanism, we
used _GrabSelect_ since it imitates the natural motion of holding onto a
climbing grip (Figure 8a). The user grabs a climbing grip with the virtual arm
and pulls themselves up using a downward motion with the finger (Figure 8b).

##  5\. Study 1: Target Selection

We conducted a target selection study to compare Attach and Direct to common
inputs (e.g., hand tracking and ray-casting). The goal was not to outperform
the baselines since none of them can achieve our motivation—arm and hand
interactions for VR in a confined space. The comparison in this study was to
position our prototype techniques between full embodiment (hand tracking) and
full efficiency (ray-casting). We observed how good users could control
FingerMapper and how they impacted metrics (e.g., presence, VBO).

###  5.1. Apparatus and Setup

The study was conducted with Oculus Quest 1 either in our lab or remotely at
the participants’ homes. Participants were seated on a chair. Five remote
participants used their devices at home to avoid unnecessary physical contact
and crowds. We instructed remote participants to clear a space that did not
cause obstructions while using hand tracking. The study application was given
to participants as a .apk file. The other participants used the lab setup on
the same headset. To treat both setups as similar as possible, remote and lab
participants were given the instructions via a website and could ask questions
in person or remotely. We disinfected the HMD cushion for lab participants for
hygiene concerns.

###  5.2. Study Design

Figure 9. We evaluated four techniques (a) Attach, (b) Direct, (c) hand
tracking, (d) ray-casting. The pink hand is for clarifying the real hand
position for this figure only. Participants only could see their virtual arms.

Figure 9 shows four techniques used in the study, starting with the
FingerMapper techniques, Attach and Direct, then two baselines, hand tracking
and ray-casting.

The study was a within-subject design with one independent variable
(technique), including Attach, Direct, hand tracking, and ray-casting (Figure
9). We were interested in what degree we could reduce physical movements and
fatigue, so we compared with hand tracking. Additionally, ray-casting was
included as a second baseline for understanding efficiency. Here, we used an
implementation similar to (Bowman and Hodges, 1997), where a ray is casting
from the fingertip of the index finger (Figure 9d). A red pointer was located
on the fingertip to visualize the selection point. In ray-casting, the pointer
was on the tip of the ray, indicating the contact point when casting on a
target.

We used a world-fixed layout (Figure 10b) in the target selection task of the
previous work (Montano Murillo et al., 2017; Wentzel et al., 2020). Here,
interactable objects were spatially distributed around the user. We argue that
this arrangement represents the application scenario with arm and hand
interactions in a confined space. The layout dimension was determined by the
participant’s arm span (A). The target layout had 18 spheres on two layers
located at the depth of the maximum arm reach (0.44A) and a half of this range
(0.22A). The closer layer had 3 $\times$ 4 targets evenly distributed between
0.76A $\times$ 0.4A, and the other layer had 3 $\times$ 2 targets that were
placed evenly between 0.25A $\times$ 0.4A from the participant. Targets ranged
evenly in the height from 0.4A to 0.8A to simulate a seated interaction space.
We calculated the distance between every two targets in the layout and
separated them into short-, medium-, and long-range by using the 25th and 50th
quantile of distances. Each type of range had an equal amount of tasks in each
condition, and they were randomized within 30 tasks of each condition. Each
participant had to complete 120 tasks (4 techniques $\times$ 30 tasks). The
order of technique was counterbalanced by the 4 $\times$ 4 Latin square.

For dependent variables, we measured task time, the physical path length of
the used wrist, and the virtual path length of the pointer in VR for each
task. The path length data was divided by the target distance of each task as
a ratio for comparison. The interaction volume was calculated by recorded
physical fingertip trajectory. In the post-test questionnaire, we used NASA-
TLX (Hart and Staveland, 1988) for the subjective fatigue, the avatar
embodiment questionnaire (Gonzalez-Franco and Peck, 2018) with three subscales
(ownership, agency, and location) for VBO, and Igroup Presence Questionnaire
(IPQ) (Schubert et al., 2001; Regenbrecht and Schubert, 2002) for presence.
Participants responded with a 7-point Likert scale inside VR. Finally, the
tracking quality of the Oculus Quest was recorded through every frame during
each task for filtering out the data of tracking loss.

###  5.3. Procedure and Task

Participants were introduced to the purpose of our study and signed the
consent form. We measured the arm length111We define arm length as the
distance from the shoulder to the wrist. Please see the figure in the
supplementary material., index finger length, and arm span for calibration.
After participants put on the HMD and entered the application, they had to re-
center themselves in VR and calibrate their body dimensions.

Figure 10. (a) Participants remained default posture before touching the
starting cube. (b) The layout with 18 targets. Two green spheres appeared when
the participant touched the starting cube.

Figure 10 illustrates the study scene where 18 targets are distributed in a
world-fixed layout. Participants touch the starting cube (10a) and reach one
of the two green targets to start the task (10b). After selecting one green
target, they have to reach the other one as fast as possible.

Participants had to maintain the default posture (Figure 4a), placing their
elbows on both sides of their waist to simulate a confined space. They touched
a white cube in front of them to begin (Figure 10a), and two randomly selected
spheres became green as the targets (Figure 10b). They were free to choose
their right or left hand for a task, but they had to start and finish the task
with the same hand. If they used the right hand to select the first target,
the second one had to be selected with the right. Using the wrong hand for the
second sphere was considered an error. This approach is common in these types
of studies (Wentzel et al., 2020) and allows users to keep the natural
behavior. The task started as the participant touched one of the green targets
using the red pointer. This target turned blue, and the participant had to
move the pointer to the second target fast and steadily, and this task was
marked as finished. A successful task came with a sound effect, and
participants returned to the default posture, waiting for a two-second
countdown, and continued to the next task by touching the white cube.

Participants had to practice and succeed in at least ten tasks before starting
each condition. They were asked to practice until they felt confident and
comfortable performing the task with each technique. This setup should prevent
a learning phase at the beginning of the study exposure for each condition but
result in different training duration for each participant. The average task
count of practicing was 11.1 times ($SD=1.6$), showing that participants had a
similar training time. The overall average success rate of practicing was 98%
(Attach: $M=97.3\%,SD=6.5$, Direct: $M=100\%,SD=0$, hand tracking:
$M=97.3\%,SD=5.1$, and ray-casting: $M=97.4\%$, $SD=5.1$). These results
indicate that participants’ mastery of each technique was similar. After each
condition, they filled out a post-test questionnaire in VR and took a
30-second rest. After finishing all the conditions, we asked for their
subjective feedback and preferred interaction if they used VR in a confined
space. Each condition took about ten minutes, and the whole study finished in
one hour.

###  5.4. Participants

Volunteer participants (n=13) took part in the study via convenience sampling
(five females and eight males, age: $M=26.3,SD=3.4$). Six participants had
experience developing VR or research, and the rest used VR occasionally.

Figure 11. Results of (a) task time in seconds, (b) the path length ratio of
physical wrist, and (c) virtual pointer. Path lengths were divided by target
distance for each task as a ratio for comparison. Having ratio one means the
path length was equal to the target distance in that task. The error bar
represents the 95% CI, and the dotted line shows the average.

Figure 11 shows the results of Study 1. The error bar represents the 95
percent confidence interval, and the dotted line shows the average. Figure 11a
depicts the task time in seconds. Attach and Direct had a higher task time
(around two seconds) compared to both baselines (about one second) on average.
Figure 11b shows the path length of the physical wrist, which was divided by
the target distance for each task as a ratio for comparison. Having ratio one
means the path length was equal to the target distance in that task. Here,
hand tracking has the largest ratio then comes Attach and Direct, and ray-
casting has the smallest ratio. This indicates our techniques reduced physical
path lengths. Figure 11c illustrates the path length ratio of the virtual
pointer. Hand tracking has the lowest ratio then comes to attach, ray-casting,
and direct. All pointing metaphors had a higher ratio probably due to
overshooting.

###  5.5. Results

The following results were based on 1560 selection tasks. The hand tracking
condition had overall more tracking loss than other techniques, probably
because FingerMapper and ray-casting had more static and close-up postures, so
they were easier to track. In total, 197 (12.6%) tasks were removed. The
accuracy of each condition was Attach 98.4%, Direct 99.4%, hand tracking
99.0%, and ray-casting 98.1% (overall 98.7%). Each technique had a high
success rate and showed participants could control the techniques to complete
the task. We only considered successful tasks and excluded the task time and
path lengths that exceeded three standard deviations from the average in each
condition. There were 30 tasks (2.2%) removed among 1363 well-tracked tasks.

For task time and path length ratios, one-way within-subject ANOVA with
Greenhouse-Geisser correction was performed and followed by the pairwise
comparison with Bonferroni correction for the post-hoc analysis. We used the
Friedman and Wilcoxon test for the post-hoc comparison of the Likert-scale
data.

Task Time. There was a significant difference in technique on the task time
($F_{3,36}=90.03,p<.01,\eta^{2}=0.88$), as shown in Figure 11a. The post-hoc
comparison revealed that the mean task time of Attach ($M=1.71s,SD=0.24$) and
Direct ($M=2.05s,SD=0.44$) were significantly higher than hand tracking
($M=0.84s,SD=0.25$) and ray-casting ($M=0.90s,SD=0.19$) ($p<.01$). There was
no significant difference between Attach and Direct ($p=.09$), neither hand
tracking and ray-casting in the post-hoc analysis ($p=.68$).

Figure 12. The scatter-plot shows the position (per second) of the index
fingertip for all participants. The bounding box represents the area of
interaction from the (a) top view and (b) front view. Black squares indicate
HMD positions.

Figure 12 shows the position of the index fingertip for all participants. We
highlight the bounding box to illustrate the interaction space from the top
view (12a) and front view (12b). Black squares indicate HMD positions. We can
observe hand tracking had the biggest volume, then came Attach and Direct.
Ray-casting had the smallest.

Path Length Data. All the path length data was divided by the target distance
for each task. These metrics represented the relationship between physical
movement and virtual distance. Figure 11b shows the path length ratio of the
physical wrist. We found a significant difference in the motion of the
physical wrist ($F_{3,36}=51.47,p<.01$, $\eta^{2}=0.81$) in technique. In the
post-hoc comparison, ray-casting ($M=0.42,SD=0.10$) had a significantly lower
path length of the physical wrist compared to the other three conditions
($p<.01$). The results indicated no significant mean difference in the path
length of the physical wrist between Attach ($M=0.69,SD=0.14$) and Direct
($M=0.66,SD=0.20$) ($p=.94$). Hand tracking ($M=0.98,SD=0.09$) had a
significantly higher path length ratio compared to Attach and Direct
($p<.01$).

There was a significant difference in the path length of the virtual pointer
($F_{3,36}=11.53,p<.01,\eta^{2}=0.49$). The virtual path length ratio was
calculated as the virtual path traveled divided by the target distance (Figure
11c). For the virtual pointer of ray-casting, we recorded the path length when
the virtual pointer was not intersecting with targets. Because the virtual
pointer (cursor) was at the end of the ray, there were additional movements on
this virtual pointer, resulting in the average ratio was 2.09 for ray-casting
(Figure 11c). These metrics were an indicator of how much unnecessary movement
was done. Hand tracking ($M=1.28,SD=0.09$) had a significantly lower path
length ratio of a virtual pointer than the other three techniques ($p<.01$).
Other comparisons revealed no significant mean difference in the virtual path
length (Direct-Attach: $p=.054$, Ray-Attach: $p=.51$, Ray-Direct: $p=.65$).
Attach ($M=1.83,SD=0.40$), Direct ($M=2.30,SD=0.89$), and ray-casting
($M=2.09,SD=0.43$) all resulted in significantly more unnecessary motion,
indicating overshooting or correction behavior while selecting. Overall, both
finger mappings reduced the physical motion but increased the virtual motion
compared to hand tracking.

Figure 13. The results of (a) NASA-TLX, (b) ownership, agency, and location
subscales from the avatar embodiment questionnaire, (c) the overall score of
IPQ. The errorbar represents the 95% CI.

Figure 13 shows the results of the questionnaire data. The error bar
represents the 95 percent confidence interval. Figure 13a illustrates the
results from NASA-TLX. For the mental demands, hand tracking and ray-casting
received the lowest score. Attach and direct received an average score of 4
out of 7 in mental demanding. This also shows a similar pattern in the
performance, effort, and frustration subscale. For the physical demand, hand
tracking has the highest score then comes FingerMapper and ray-casting. For
the temporal demand, hand tracking and ray-casting received a higher score
than attach and direct. Figure 13b depicts the results of avatar embodiment
(from -3 to 3). FingerMapper has a neutral score in the ownership subscale for
attach, and a neutral toward slightly negative score for direct. For the
agency subscale, the attach and direct mapping has a slightly positive score.
For the location subscale, both FingerMapper received a neutral score. Figure
13c shows the scores of the presence questionnaire, where FingerMapper
received scores around average (4 out of 7).

Figure 12 shows the physical position of all participant’s index fingertips
from two perspectives (HMD movement included). We observed the pattern of
reaching toward the target layout. Participants’ movements were closer to the
center in FingerMapper functions and ray-casting than hand tracking. Black
points represent the HMD position. Since we conducted Study 1 in an open space
without any constraints, participants still had some body movements. We
calculated these maximum bounding cubes to indicate interaction volume,
showing how much space the participants used with each technique. These
volumes allowed us to quantify how much we could reduce this volume. The hand
tracking had the largest interaction volume ($0.888\ m^{3}$) followed by
Direct ($0.782\ m^{3}$), Attach ($0.602\ m^{3}$) and ray-casting ($0.506\
m^{3}$). Attach could reduce the interaction volume by 32.2% to be comparable
to ray-casting. Also, we analyzed the interaction volume data by participants
and conditions. By performing a logarithmic transformation, we found a
statistically significant difference between technique
($F_{3,36}=32.8,p<.01,\eta^{2}=0.73$). In a post-hoc analysis, hand tracking
($M=0.44,SD=0.14$) had a statistically significant higher volume than Attach
($M=0.21,SD=0.1$), Direct ($M=0.22$, $SD=0.12$), and ray-casting
($M=0.11,SD=0.05$) ($p<.01$ for three comparisons). There was no difference
between Attach and Direct, but both had a statistically significant higher
volume compared to ray-casting (Ray-Attach: $p=.02$, Ray-Direct $p=.04$).

Fatigue and NASA-TLX. Figure 13a shows the results of NASA-TLX. We were
particularly interested in the mental and physical demand, and our analysis
revealed significant differences (Mental:
$\chi^{2}_{3}=24.64,p<.01,\eta^{2}=0.63$, Physical:
$\chi^{2}_{3}=19.73,p<.01,\eta^{2}=0.51$). The mental demand of hand tracking
($M=1.62,SD=1.19$) and ray-casting ($M=2.31,SD=1.80$) were significantly lower
than Direct ($M=4.62,SD=1.94$) and Attach ($M=4.54,SD=2.15$) ($p<.05$). For
the physical demand, hand tracking ($M=5.85,SD=0.90$) had a higher score
compared to Attach ($M=3.85,SD=1.14$), Direct ($M=4.05,SD=1.75$), and ray-
casting ($M=2.54,SD=1.66$) because participants had to reach out their arm in
the hand tracking condition. Here, comparisons between Attach-Hand and Ray-
Hand found significant differences in the post-hoc analysis ($p<.05$). For the
other four subscales in NASA-TLX, there were significant differences in
temporal demand, effort, and frustration (Temporal:
$\chi^{2}_{3}=13.27,p<.01,\eta^{2}=0.34$, Effort:
$\chi^{2}_{3}=12.20,p<.01,\eta^{2}=0.31$, Frustration:
$\chi^{2}_{3}=8.17,p=0.043,\eta^{2}=0.21$), and there was no significant
effect on the subscale of performance
($\chi^{2}_{3}=6.36,p>.05,\eta^{2}=0.16$). One significant mean difference in
temporal demand was found in the post-hoc comparison where Attach
($M=2.92,SD=1.44$) has a lower score than hand tracking ($M=4.38,SD=1.39$)
($p<.05$).

VBO and Presence. Figure 13b shows the score of three subscales for VBO. The
values were averages of individual questions and ranged between -3 and 3. A
score higher than zero represents the feeling of VBO, and a score lower shows
the lack thereof. There were significant differences in techniques on three
subscales we measured (Ownership: $\chi^{2}_{3}=28.97,p<.01,\eta^{2}=0.74$,
Agency: $\chi^{2}_{3}=18.22,p<.01,\eta^{2}=0.47$, Location:
$\chi^{2}_{3}=14.56,p<.01,\eta^{2}=0.37$). For the post-hoc comparison in the
ownership subscale, we only found hand tracking ($M=2.49,SD=0.46$) was
significantly higher than Attach ($M=0.51,SD=1.14$) and Direct
($M=-0.39,SD=1.59$) ($p<.01$). For the agency subscale, hand tracking
($M=1.65,SD=0.58$) was significantly higher than Direct ($M=0.48,SD=1.17$) and
Attach ($M=0.83,SD=0.66$) ($p<.05$). No significant mean difference was found
in the post-hoc comparisons of the location subscale.

Finally, Figure 13c shows the General Presence in IPQ. Please see the
supplementary material for all subscales (Spatial Presence, Involvement, and
Experienced Realism). There were significant differences in General Presence
($\chi^{2}_{3}=14.51,p<.01,\eta^{2}=0.37$) and Experienced Realism
($\chi^{2}_{3}=11.5,p<.01,\eta^{2}=0.30$). No mean differences were found in
the post-hoc comparison of General Presence and Experienced Realism. No
significant difference was revealed in Spatial Presence
($\chi^{2}_{3}=4.42,p=.22,\eta^{2}=0.11$) and Involvement
($\chi^{2}_{3}=3.92,p=.27,\eta^{2}=0.10$).

###  5.6. Discussion

In the following, we position both FingerMapper mapping functions between two
extremes: hand tracking and ray-casting.

Performance. We found using FingerMapper leads to a significant increase in
the task time. Parts of this can be explained by the learning curve that we
observed. Despite the initial training phase, participants still showed
improvements during the study. Additionally, Attach and Direct showed a higher
mental demand, effort, frustration compared to hand tracking and ray-casting.
Participants also tended to a lower performance222The scale of performance is
reversed in NASA-TLX. A low-performance score means the user felt perfect
considering how successful (or satisfied) while performing a task, and a high-
performance score means the user felt failure while doing a task. using
FingerMapper. This is probably due to the novelty of the technique and the
initial phase, where users needed to get used to the different mappings.
Overall, we argue the task time is still within an acceptable range since
eight participants reported in the questionnaire that they would use one of
the finger mappings to interact in confined spaces (two Direct, six Attach,
and five ray-casting over 13 participants).

Motion and Fatigue. Our goal was to reduce fatigue, physical motion, and
interaction volume for virtual arm interaction in a confined space. Results
indicated that FingerMapper had significantly lower scores on the physical
demand than hand tracking. We also found that ray-casting had an even lower
score, positioning Attach and Direct in between both of them.

Attach reduced the necessary physical path length down to approximately 67.8%,
positioning it again between ray-casting (57.0%) and hand tracking (considered
here as 100%). Additionally, we found that Attach reduced the overall
interaction volume compared to hand tracking by 32.2%. For the average of the
individual interaction volume, Attach reduced this metric down to 46.7% of
hand tracking. We argue Attach (0.602 $m^{3}$) and ray-casting (0.506 $m^{3}$)
have similar maximum interaction volumes while allowing the case to perform
large virtual arm motions in confined spaces. Also, ray-casting has
substantially less DoF, requiring additional extensions to allow for rotation
and retraction (e.g., controlling the depth of the ray (LaViola et al.,
2017)). Without these extensions, ray-casting cannot enable manipulation as
FingerMapper. While hand tracking had little additional virtual motions, both
FingerMapper and ray-casting showed a similar (Figure 11c) level of additional
virtual distance traveled, indicating participants had a quick burst in the
direction of the target with overshooting and had an additional correction
phase afterward.

VBO and Presence. Here we mainly consider Attach, Direct, and hand tracking
because the concept of “body ownership” is, in general, difficult to apply to
an abstract pointing device such as a ray (Lin and Jörg, 2016). However, to
control the visual of virtual arms in each condition, we used a tracked
visualization of the user’s hand. This probably led some participants to rate
VBO toward the hand and not the actual ray as we intended. We still report the
VBO score of ray-casting for completeness, but we do not provide further
interpretation of this score.

Initially, we expected Attach and Direct can achieve a VBO score similar to
hand tracking. However, Direct could not preserve any degree of VBO compared
to Attach. Although Direct still preserved some VBO in the agency subscale, it
was dominated by the visual of virtual arms. Therefore, Direct had neutral or
even negative scores in the ownership and location subscales. It was less
preferred when asked whether participants would use it in a confined space.
Nevertheless, Attach could preserve a general sense of ownership, agency, and
even location scores.

In General Presence, all conditions received an average score above four on
the 7-point scale. We believe all techniques could keep a rather high level of
presence and did not break the user experience because General Presence scores
were all above four. Nevertheless, hand tracking had the highest score
followed by ray-casting, Direct and Attach. In Spatial Presence and
Involvement, four techniques did not show statistically significant
differences in their score. The reason might be all participants were in the
same virtual environment and interacted with the same task. Experienced
Realism ranged between three to four, probably caused by the virtual
environment being almost clear for study control.

Observations and Qualitative Feedback. We observed participants with little to
no VR experiences tended to express frustration when they could not fully
control FingerMapper during practicing. They also preferred ray-casting for
using VR in a confined space. Since the task in the study can be achieved in a
simple way physically (i.e., hand tracking), spending extra effort to complete
this easy task with FingerMapper indeed troubled some participants.

The participants with more VR experiences could learn and control the mapping
faster than others. One participant who frequently uses VR for gaming
mentioned (P13), “At the beginning, I struggled a little with controlling, but
once I understood the mapping, I think I can use this mapped arm to do more VR
interaction.” FingerMapper, despite a higher task time, has lower fatigue and
physical motion. Results show that Attach can preserve the user’s presence and
partially some degree of VBO.

##  6\. Study 2: User Experiences in the Confined Space

Study 1 was to understand the exact interaction time and experience metrics in
a controlled environment. In Study 2, we wanted participants to experience
using FingerMapper inside a confined space, compared to hand tracking, the
current state-of-the-art for VR interaction. Our focus was on experience
metrics (presence, enjoyment) and safety metrics (perceived safety, amount of
collisions).

###  6.1. Apparatus and Setup

While the setup in study 1 was in an open space, study 2 was in the front
passenger seat of a standing car (Figure 14, Dacia Logan MCV) parked in our
institution. The main goal was to test our technique inside a realistic
representation of a confined space. We used FingerGrabber deployed on an
Oculus Quest 2 for the scenario because it involves object interaction. For
the techniques, we used the same version of hand tracking and Attach in Study
1. We chose Attach because it outperformed Direct in VBO and performance.
Participants were seated on the front passenger seat, using the stationary
boundary of the Oculus Quest. The distances from the chest to the surroundings
were roof: 50 cm, windshield: 85 cm, right car door: 40 cm, and left car door:
110 cm.

Figure 14. A participant seated in the front passenger seat.

Figure 14 shows the setup of Study 2, where participants sat inside the front
passenger seat of a car and played FingerGrabber with Attach and hand
tracking.

###  6.2. Study Design

The study was a within-subject design with one independent variable techniques
having two levels (Attach and hand tracking). Each participant had to
experience FingerGrabber (subsection 4.1) with Attach and hand tracking.
FingerGrabber aims to simulate VR experiences similar to Job Simulator, where
the virtual objects would scatter within the arm space of the user for
selection and manipulation tasks. These VR applications may be unavailable in
a confined space because the virtual objects may be out of reach. Here, we
argue that Attach could be a good solution, reducing physical movement to keep
the user safe during the interaction and preserving presence and enjoyment.

We did not include pointing techniques, such as ray-casting, since they
require adapting the game or application. We envision FingerMapper as an
alternative to generate input stimuli for virtual arm interactions that can be
used across all current applications relying on full-scale hand tracking
without modifying applications.

The order of the two conditions was counterbalanced. To measure the user
experience, the participant had to fill out a questionnaire after each
condition, including SUS presence questionnaire (Usoh et al., 2000), avatar
embodiment (Gonzalez-Franco and Peck, 2018) for VBO, and Intrinsic Motivation
Inventory (IMI) (McAuley et al., 1989) for enjoyment. We added two 7-point
Likert scale questions for perceived safety in the confined space: “I feel
safe when I was in the game,” and “I feel worried about touching the car
interior or other passenger when playing.” The study was video recorded.
Therefore, we observed and counted collisions in each video footage to see if
Attach could reduce physical space for VR interaction. A collision was defined
as the participant physically contacting the interior parts of the car. At the
end of the study, participants had to rate (7-point Likert scale) which
interaction techniques they prefer for using VR confined spaces.

###  6.3. Procedure

Participants were introduced to the study and signed the consent form. All
participants were new to the system and did not attend Study 1. To reduce the
“wow” effect, each participant practiced with the system in a training session
prior to the data collection. They were free to practice Attach or hand
tracking in an open space until they felt comfortable with the interaction (on
average five minutes) until they could successfully select and put the target
at the right place three times in a row. This step ensured each participant
had a similar level of understanding and skill despite their prior VR
experience. When they sat in the car, we gave the following instruction,
“imagine you are sitting inside a car of your friend (or a family member),
please adjust the seat into a comfortable position as you usually do.” After
adjusting the seat, the participant put on the Oculus Quest 2. The game lasted
two minutes. Here, the goal was not to run performance-oriented evaluation
(done in Study 1) but to observe users interacting in a realistic environment.
The impact of the confined space (collisions) and perceived safety were
independent of the duration of exposure. Collisions will happen right away,
affecting the perceived safety. After each condition, they filled out the
prior mentioned questionnaires in VR. When the participants completed both
techniques, the experimenter interviewed them on their overall experience in
VR and their preference of techniques. The whole study took an hour to
complete.

###  6.4. Participants

Participants (n=13, six females and seven males) ages ranged from 18 to 33
($M=26.0,SD=4.0$). All participants were recruited from our institute using
convenience sampling. On a 5-point Likert scale, participants rated their
expertise in VR between below average and average ($M=2.2,SD=0.8$). The
frequency of using VR was rated between almost never and sometimes
($M=1.8,SD=0.8$). Overall, the sampled participants were mostly novice VR
users. Participants reported normal or corrected vision and no motor
impairments. The study was carried out following local health and safety
protocols. Each participant was compensated with 10€ for their participation.

###  6.5. Results

Figure 15a shows Attach received a lower score in VBO compared to hand
tracking. Dependent t-test found hand tracking had a higher score in ownership
($Mean_{Diff}=-1.15$, $95\%\ CI[-1.92,-0.38]$), agency ($Mean_{Diff}=-0.98$,
$95\%\ CI[-1.55,-0.41]$, and location ($Mean_{Diff}=-1.03$, $95\%\
CI[-1.53,-0.52]$) of the avatar embodiment questionnaire. In each subscale, we
revealed a significant difference (Ownership:
$t_{12}=-3.27,p=0.007,Cohen^{\prime}s\ d=0.90$; Agency:
$t_{12}=-3.73,p=0.003,Cohen^{\prime}s\ d=1.03$; Location:
$t_{12}=-4.44,p=0.0008,Cohen^{\prime}s\ d=1.23$). However, the VBO scores of
Attach were still above neutral, which is in line with the results in Study 1.
Keep in mind that the scale of the avatar embodiment questionnaire ranges from
-3 to 3. Our results would be equivalent to a rating of above 4 (above
average) on a 7-point Likert scale.

Figure 15. The results of: (a) virtual body ownership; (b) presence,
enjoyment, and perceived safety; and (c) collision counts (Hand Tracking:
M=4.9; FingerMapper: M=0.3). The errorbar represents the 95% CI.

Figure 15a shows the results of avatar embodiment on three subscales:
ownership, agency, and location. Hand tracking received a higher score than
Attach. However, Attach still preserves a small embodiment. Figure 15b depicts
the results of a 7-point Likert scale on the presence, enjoyment, and
perceived safety. Attach has a similar presence and enjoyment compared to hand
tracking. Besides, Participants felt safer and less worried when using Attach
compared to hand tracking. Figure 15c indicates that Attach has significantly
fewer collisions compared to hand tracking in Study 2.

For presence (Figure 15b), a paired t-test found no statistically significant
difference between techniques ($Mean_{Diff}=-0.3$, $95\%\ CI\ [-0.9,0.3]$,
$t_{12}=-1.21,p=0.25,Cohen^{\prime}s\ d=0.34$). Although there was also no
statistical significance in enjoyment ($Mean_{Diff}=-0.1$, $95\%\ CI\
[-0.7,0.5]$, $t_{12}=-0.51,p=0.62$, $Cohen^{\prime}s\ d=0.14$), both Attach
($M=5.0$) and hand tracking ($M=5.1$) received positive scores, showing
participants enjoyed using FingerGrabber.

Our results show that Attach benefited from having less physical movement that
resulted in fewer collisions with the environment compared to hand tracking.
Re-watching all the video recordings of the participants, one author coded
each instance (location, time) of a collision with any part of the car. We
found that Attach had fewer collisions ($M=0.3,SD=0.5$) compared to hand
tracking ($M=4.9,SD=3.5$) (Figure 15c). A dependent t-test found a significant
difference between the two techniques ($Mean_{Diff}=-4.5$, $95\%\ CI\
[-6.5,-2.5]$, $t_{12}=-5.02,p=.0003$, $Cohen^{\prime}s\ d=1.39$). Most of the
collisions happened on the right car door (84%). Additionally, we found a
difference in the participant’s perceived safety scores between the two
techniques. Figure 15b shows the response to “I feel safe when I was in the
game,” and “I feel worried about touching the car interior or other passenger
when playing.” For “feel safe”, participants had a higher rating in Attach
(M=5.7) than Hand (M=4.5). For “feel worried”, Attach (M=2.8) received a lower
score than Hand (M=5.8). There were significant differences of the feeling of
safety ($Mean_{Diff}=1.2$, $95\%\ CI\
[0.3,2.0].t_{12}=2.96,p=0.01,Cohen^{\prime}s\ d=0.82$) and feeling of worry
($Mean_{Diff}=-3.1$, $95\%\ CI\ [-4.0,-2.2].t_{12}=-7.41$,
$p<.001,Cohen^{\prime}s\ d=2.05$). These results show that participants
perceived a higher safety while using Attach. Finally, 10 out of 13 preferred
to use Attach (FingerMapper) over hand tracking when asked which technique
they would like to use for VR applications in confined spaces.

###  6.6. Discussion

In the confined space, our results show that FingerMapper (Attach) benefited
from its fewer physical movements and kept presence and enjoyment comparable
to hand tracking. Most participants (10 out of 13) mentioned that reducing
collisions was their main concern, and they preferred using FingerMapper
because it requires less physical space, so they feel safer. FingerMapper was
able to almost completely avoid collisions. In our study, only four
participants collided one time with the right door at the beginning of the
condition. P5 mentioned, “FingerMapper respects the physical environment
around you. So you make small movements and do not have collisions with the
physical space. This way, we can be really into the VR experience but [avoid]
the experience of colliding with something that’s bringing you back to
physical reality.” The subjective rating on perceived safety and preferences
also reflected that participants preferred FingerMapper as the interaction for
confined spaces.

Participants were aware of crossing over the space of the driver’s seat. They
felt that hand tracking is almost impossible to be applied in a confined
space. P9 said, “if I touched the door or window, I know it’s not that
dangerous because if I touch them, there won’t be any accidents. However, if I
know there’s a driver on the left-hand side, Then I will avoid [going] too far
with my arms.” P10 also mentioned, “If a ball refreshed at the same position
where I know, I probably will touch the interior of the car to grab the ball.
If some people are sitting there and I touched those people, I probably will
just avoid grabbing the ball in that direction and try to do the other
direction.” Participants mentioned that during the interaction they had a
mental model of the physical environment they were in, and rather avoided an
interaction than collide with the environment.

FingerMapper can overcome the confined space and presents a safe (perceived
safety, amount of collisions), ergonomic (less physical motion in Study 1),
and enjoyable interaction (enjoyment) while preserving presence and VBO.
Overall, participants’ subjective rating showed a strong preference towards
using FingerMapper in confined spaces for VR interaction ($M=5.3,SD=0.9$)
compared to hand tracking ($M=3.2,SD=1.5$).

##  7\. Lessons Learned

As VR technologies become more mobile, users are able to engage in immersive
virtual environments wherever they want. The confined space (e.g., commuting
in a passenger seat) would be one of the new contexts where it does not allow
for whole-body motion. We can observe smartphone behavior to understand what
these confined spaces could look like. People use a smartphone and smartphone
games during commutes or in small spaces (e.g., a toilette booth). If VR
becomes further distributed, users would also want to engage with immersive
applications in such spaces.

Despite having a higher task time and smaller VBO, Study 1 shows that
FingerMapper could reduce physical movements and fatigue compared to hand
tracking. In the confined space of Study 2, FingerMapper had fewer collisions
than hand tracking and could enable a similar level of presence and enjoyment
inside a VR experience. Additionally, participants had a significantly higher
perceived safety using FingerMapper. Although it took a short training phase
to master, participants preferred using FingerMapper for VR interaction in
confined spaces. In the following, we discuss insights into FingerMapper
gained from the design process and two studies.

#### Reducing Fatigue in Long Duration VR Experiences with FingerMapper

FingerMapper does not necessarily have to be used inside a confined space.
Current VR interactions start to optimize for ergonomics to enable a longer
and more comfortable experience (Zielasko and Riecke, 2020; Montano Murillo et
al., 2017; Wentzel et al., 2020; Evangelista Belo et al., 2021). Our approach
can be an option when the user is tired and wants a lighter experience in
terms of physical fatigue. This approach challenges the dominant notion that
VR user needs to perform the same actions as they do in real life (e.g.,
walking long distances). FingerMapper provides users the feeling as if they
would perform these interactions without actually performing them.

#### Context Adaptive Interaction: Confined Space and New Metrics

Through the exploration of FingerMapper, we found that participants started to
value perceived safety and avoid collisions while using VR in a confined
space. Current VR interaction techniques are frequently optimized around
metrics such as presence or performance. FingerMapper demonstrated that
participants preferred the interaction technique with lower performance
metrics but higher safety. We argue that future research metrics of VR
interaction techniques will start to include constructs such as safety, social
acceptance, and public perception.

##  8\. Limitations and Future Works

Although FingerMapper can enable virtual arm interaction for VR in a confined
space while having less physical movement and fatigue, fewer collisions, and
higher perceived safety, there are still some limitations to be improved.
First, the displacement between the fingers and virtual arms resulted in lower
VBO. One potential solution could be adding haptic feedback at the fingertip
(e.g., tactile feedback) when the virtual arm intersects with a target to
enhance the overall embodiment. Second, we introduce Attach and Direct, but
the mapping function can be designed in many different forms, giving rise to
future work exploring different mappings.

The current studies focused on the controlled study and did not evaluate
FingerMapper inside a public confined space (e.g., a bus). The reason is that
we were initially interested in comparing the individual techniques in a
controlled environment to have a fair comparison to regular hand tracking.
Future work could conduct further studies in public spaces to understand the
social implications of finger mappings. Our studies explored the effect of
FingerMapper by observing path length with a small sample size, which is one
limitation of the current work. Future research could aim for a large sample
size experimental setup to verify the effect of re-associating body parts in
both position and rotational movement.

To enable VR interaction in confined spaces, one can either adapt the
application to require less physical movement (im-henri, 2022; Melnick, 2020)
or the input. FingerMapper focused on adapting the input since it allows for
having one implementation (might even be on the system level) that creates a
mapping from a confined space interaction toward a whole-body interaction in
VR. This allows the designers of VR applications to abstract from the context
in which the application would be used and always design for a whole-body
experience. However, the challenge of adapting the input is to find an
appropriate mapping that has the same DoF while using less physical space and
still being usable. This inevitably leads to trade-off decisions and creates a
set of limitations for the proposed input adaptation.

While FingerMapper provides a mapping for the basic input mechanisms of whole-
body hand-tracking interaction (object selection and manipulation), our trade-
off is losing efficiency (higher selection time, less precision).
Additionally, the kinematic structure of fingers is different to the structure
of arms (e.g., a throwing motion with arms is possible because of the DoF of
the connecting joints, while the connecting joints of the finger have a
different DoF). This results in some actions (e.g., throwing) being more
difficult to perform using FingerMapper. However, we argue that FingerMapper
focuses on enabling the basic functions of interacting in VR (object selection
and manipulation) and presenting a mapping that can be generally applied to
most applications and allow for a safe interaction in confined spaces. Future
work should explore how complex actions can be mapped to small-space
interaction techniques.

##  9\. Conclusion

FingerMapper enables whole-body VR interaction in VR, allowing users to
control arm and hand interactions in confined spaces through small finger
motions. Three applications highlight the different functionalities and
scenarios, including large slicing motions, object interaction, and
locomotion. Two studies show that FingerMapper can reduce physical movements,
fatigue, and collisions. Osur techniques reach a similar level of presence and
enjoyment in the confined space with higher perceived safety and user
preferences. We see FingerMapper as an alternative to hand tracking—if the
user interacts in a confined space, and ray-casting—if the user needs more
functionality and embodiment of the avatar. Overall, we argue input techniques
in VR should be similarly easy to adapt to the context as the tracking options
are (e.g., room-scale and stationary tracking). The concept of FingerMapper
would work with any application when the environment is not allowed for whole-
body motion while preserving the VR user’s experience and safety.

###### Acknowledgements.

We thank our anonymous reviewers for their suggestions on improving this paper
and all participants. This work was partially conducted within the Investments
for the Future Program (PIA), CONTINUUM (ANR-21-ESRE-0030), funded by French
National Research Agency (ANR) and the Living Lab 5G project financed by the
Banque Publique d’Investissement (BPI).

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