RotoWrist: Continuous Infrared Wrist Angle Tracking using a
Wristband
Farshid Salemi Parizi

Wolf Kienzle

Eric Whitmire

farshid@cs.washington.edu
Facebook Reality Labs, University ofWashington, United States

wkienzle@fb.com
Facebook Reality Labs, United States

ewhitmire@fb.com
Facebook Reality Labs, United States

Aakar Gupta

Hrvoje Benko

aakarg@fb.com
Facebook Reality Labs, United States

benko@fb.com
Facebook Reality Labs, United States

Figure 1: RotoWrist is a wristband that can track 2-DoF continuous relative angle of the wrist with respect to the arm. Left: A
user playing Beat Saber with an Oculus Rift-S VR system. The left hand orientation is estimated using RotoWrist while a Rift-S
controller tracks the right hand. Middle: Visualization of wrist angle tracking in a Unity test application. Right: RotoWrist
tracking relies on eight time of flight IR modules embedded in a wristband.

ABSTRACTCCS CONCEPTS

We introduce RotoWrist, an infrared (IR) light based solution forcontinuously and reliably tracking 2-degree-of-freedom (DoF) relative angle of the wrist with respect to the forearm using a wristband.
The tracking system consists of eight time-of-flight (ToF) IR light
modules distributed around a wristband. We developed a computationally simple tracking approach to reconstruct the orientationof the wrist without any runtime training, ensuring user independence. An evaluation study demonstrated that RotoWrist achievesa cross-user median tracking error of 5.9° in flexion/extension and
6.8° in radial and ulnar deviation with no calibration required asmeasured with optical ground truth. We further demonstrate theperformance of RotoWrist for a pointing task and compare it against
ground truth tracking.

• Human-centered computing → Ubiquitous and mobile computing; Pointing devices; Pointing.

KEYWORDS
wrist pose; time-of-flight sensor; virtual and augmented reality;
hand tracking; wearable device; wristband.
ACM Reference Format:
Farshid Salemi Parizi, Wolf Kienzle, Eric Whitmire, Aakar Gupta, and Hrvoje
Benko. 2021. RotoWrist: Continuous Infrared Wrist Angle Tracking using a
Wristband. In 27th ACM Symposium on Virtual Reality Software and Technology (VRST ’21), December 8–10, 2021, Osaka, Japan. ACM, New York, NY,
USA, 11 pages. https://doi.org/10.1145/3489849.3489886

1

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republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
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ACM ISBN 978-1-4503-9092-7/21/12. . . $15.00
https://doi.org/10.1145/3489849.3489886

INTRODUCTION

Wearable devices have grown immensely popular and are enabling
computing in many areas of people’s lives across a wide rangeof scenarios. Tracking the movement of the hand has long been
important in developing natural and intuitive interaction paradigms
for computing [32]. Specifically, the dexterity of human wrist joints
typically enables a broad range of motion [23], making the wrist a
promising modality to drive input for wearable computing.
Existing hand tracking methods most often rely on computer
vision [5, 24, 27, 29, 35, 39] with cameras in the environment or
on a head-mounted display (HMD). These approaches yield highaccuracy, but require line-of-sight and in some cases, infrastructure

VRST ’21, December 8–10, 2021, Osaka, Japan

Salemi Parizi, et al.

Radial Deviation
Extension

Yaw

Pitch

can provide continuous input using their wrist, in eyes-away contexts where line-of-sight to a camera might be difficult to maintain.
For example, user might point while keeping their arm motionlessat their side or with their hands beneath a table.
Our primary contributions are:
(1) A simple, low-power hardware architecture consisting ofeight time-of-flight IR light modules embedded in a custombuilt wristband that enables continuous wrist angle tracking.
(2) A tracking algorithm that can reliably estimate 2-DoF orientation of the wrist without user training.
(3) A system characterization and user evaluation demonstrating tracking accuracy of 5.9° in flexion/extension and 6.8° inradial/ulnar deviation across users without any calibration,compared to an optical motion capture system.
(4) A study comparing RotoWrist’s pointing performance to ahigh-precision wrist and forearm tracker.

Ulnar Deviation

Flexion
Pronation

Supination

Figure 2: Diagram illustrating different wrist motions. RotoWrist continuously captures wrist flexion/extension andradial/ulnar deviation. It does not attempt to capture pronation/supination.

support, that limit mobility and versatility. The growing demand forcomputing in ubiquitous contexts motivates researchers to consider
wrist-worn devices (e.g., smartwatches and smart bands), whichhave become increasingly mainstream. However, most research
efforts around wristband-based sensing focus on discrete, gesture
based interactions [6, 8, 9, 13, 14, 36, 43, 45]. Although discrete
gestures are useful, they are just one part of the interaction language
needed for mobile wearable computing. For common tasks likepointing at targets, drawing, sliding, or swipe-based text input,
an input device that supports continuous tracking will be mostappropriate.
In this work, we present RotoWrist, an infrared (IR) light-based
device that performs continuous 2-degree-of-freedom (DoF) wrist
tracking from the wristband. RotoWrist works without requiring
any user training. The RotoWrist system consists of a wristband
that uses eight ToF IR modules to continuously estimate the 2-DoF
angle of the wrist with respect to the forearm in real-time. Each
low-power sensor measures the absolute distance from the hand.
As the user moves their wrist, the relative distance between thehand and each sensor changes. RotoWrist combines these eightmeasurements into a 2-DoF wrist orientation—flexion/extension
and radial/ulnar deviation, as depicted in Figure 2.
Among related work in sensor-based tracking of the wrist, RotoWrist is most similar to WristWhirl[9]. In RotoWrist, we prioritized robustness and accuracy and designed the system fromground up to support continuous, absolute tracking without any
user calibration. While the WristWhirl system is capable of sensing
continuous wrist motion, it did not investigate tracking performance and focused primarily on the accuracy of detecting 8 distinct
gestures. Not only does RotoWrist support real-time continuous
tracking that is benchmarked against a ground truth system, but wefurther establish RotoWrist’s ability to operate in a cross-session
and cross-user fashion.
RotoWrist offers a rich input source for a variety of wearable
devices, including smartwatches and HMDs. With RotoWrist, a user

2

RELATED WORK

Existing hand tracking methods are based on sensors that are either
outside-in (off-body) or inside-out (on-body). Outside-in sensors
with cameras [5, 24, 27, 29, 35, 39] or radio frequency[21, 38] employ
external sensing infrastructure that limits users’ mobility for dailyinteraction and making these technologies less suited to mobile
and ubiquitous uses. In comparison, inside-out sensing approaches
provide much better mobility support. Therefore, in the rest of
this section we have focused on inside-out hand tracking and havesplit the related work into two main categories: 1) tracking wrist
movement by leveraging the internal anatomy of the wrist and 2)
tracking using the external contours of the hand.

2.1

Inferring wrist angles from internal signals

A number of research projects explored electromyography (EMG)
where electrodes are placed below the elbow to detect hand gestures [16, 31]. EMG systems are complex and require the user towear a bulky array of electrodes below the elbow. Although thesesystems continuously track user wrist and fingers, they require avery exhaustive training and/or calibration. Other researchers haveleveraged electrical impedance tomography [45], bio-capacitive
sensing [28, 36], IR tomography [22], acoustic tomography [7, 11]
and more recently ultrasound [14] for discrete hand gesture recognition from a wristband. These methods result in discrete, gesturebased interactions like detecting a fist vs stretch, directional sweep,
finger pinches, and not continuous tracking. Furthermore, thesesystems are often very sensitive to the positioning and slippage ofthe device and therefore often require per-session training.

2.2

Inferring wrist angles from external signals

Skin surface deformation due to wrist movement can be detected bypressure/stretch sensors [6, 15, 34]. Other researchers have leveraged ultrasound beam forming [13] and acoustics [19, 25] to recognize discrete gestures. Other approaches to determining handpose on wearable devices employ vision sensors such as a camera
or optical sensor on the inner side of the arm or wrist [9, 18, 41, 44].
Digits [18] used infrared illumination from the wrist to track handpose. Opisthenar uses an embedded wrist camera to recognize static

RotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband

hand poses [44]. Although this method can enable full hand tracking, the form factor and power consumption limits practicality.
FingerTrak use 4 thermal cameras around the wrist to reconstructfull hand pose though cannot reconstruct relative angle of the wristwith respect to the forearm and consumes around 3 watts of power
which limits practicality in a mobile setting [12].
WristWhirl [9] is most relevant to RotoWrist. It uses an arrayof proximity sensors that can detect gestures with high quality.
Our work advances upon WristWhirl in four significant aspects:
1) RotoWrist investigates and reports real-time angular deviations
from the ground truth. WristWhirl did not investigate tracking performance, focusing primarily on the accuracy of detecting 8 distinct
gestures including 4 directional marks, and 4 free-form shapes. 2)
For angular wrist pose estimation, we establish RotoWrist’s crosssession and cross-user performance. In contrast, WristWhirl only
reported within-session performance and required the user to calibrate every session. 3) In addition to raw sensing accuracy, we
further investigate RotoWrist on a pointing task and compare its
performance with ground truth. It’s unclear how well WristWhirlcan be used for pointing tasks. 4) RotoWrist improves upon WristWhirl’s hardware design by adopting optical ToF sensors which arerobust to different skin tones, lighting conditions, and indoor vs.
outdoor use. WristWhirl’s IR photo diode based approach is prone
to ambient IR noise, lighting changes, and skin tone dependencies
owing to variations in IR light reflection from the skin.
There is also a line of work on using a ring in tandem with a wristband for finger tracking and gesture interaction. Magic Finger [42]and Light Ring [17] enables users to interact with surfaces using
optical sensor embedded in a wearable ring form factor. WRIST [43]explores combining IMU data from a smartwatch and smart ringfor distal pointing and gesture interaction. Researchers have also
used magnetic tracking for wrist and finger tracking [3, 4, 26].
In addition, many commercial augmented and virtual reality
systems primarily use a handheld controller to continuously trackusers’ hand pose. While these solutions offer great accuracy andspecific affordances, they are still limited by mobility constraints.
Tracking the hand without controllers and without line-of-sightaffords the potential to consider new freehand interactions. RotoWrist is the first wrist-mounted device that can reconstruct wrist
angle continuously by using eight ToF IR modules sitting tightly on
the wrist. A key innovation of RotoWrist is that it does not require
users any training before using the system thus making it work outof the box.

VRST ’21, December 8–10, 2021, Osaka, Japans2

IMPLEMENTATIONThe RotoWrist system consists of a sensing wristband that incorporates eight ToF IR modules and a controller arm band that handles
powering the sensing wristband and communicating the data backto a host PC. The following sections provide details of the RotoWrist
hardware, capabilities and algorithm.

3.1

Sensing Wristband

One of the challenging aspects of designing a wristband is to manufacture one that can fit different people and whose sensing is robust
to different wrist sizes. In addition, the form-factor of the wristband
should be minimally invasive. We design the wristband as a top

s4

s1

s5
s8

s6

s7
s1
s2
s3
s4
s5
s6
s7
s8

Figure 3: RotoWrist consists of a controller arm band that
handles communicating the data back to host PC and eighttime-of-flight modules embedded in a wristband.
and bottom part that connect together using a hinge mechanism.
All parts are made using a 3D printed Nylon material. The hinge(Figure 1 right) on the side allows the band to open and close. On
the other side, we have leveraged an elastic band to securely fasten
the top and bottom parts. As shown in Figure 3 left, there are several
hooks on the top part that the elastic band can be connected tobased on the wrist size. To further support people with different
wrist sizes, we built two sizes of the sensing wristband – one witha diameter of 120 mm and the other 180 mm.
As shown in Figure 3 (bottom right), we built a custom flex PCB
that incorporates eight time of flight modules (VL6180X) and a 22pin SMD connector. These modules are equally spaced around thewristband and they sit perpendicular to and at most 8 mm from thewristband’s surface. Each individual sensor determines the distanceto the nearest object within a 25° cone by emitting pulsed infrared
light and timing the returned reflection. The eight TOF modules
consume only 13.6 mA.

3.2
3

s3

Controller Armband

The sensors on the wristband are connected to a custom madedaughterboard through a very thin 22 pin assembly cable1 . Since
the recommended operating voltage for the ST ToF moudel is 2.8 V,
the daughterboard also incorporates two bidirectional level-shifting
translators (TXB0106). As shown in Figure 3 (bottom left), thedaughter board sits on an Arduino Due. The sensors on the wristband are connected to the microcontroller (Atmel SAM3X8E ARM
Cortex) on the Due board over I2 C, which triggers measurement. A
timer-based algorithm is set to take measurement every 20 ms fromall the sensors resulting in a tracking frame rate of 50 Hz. A SparkFun Bluetooth Mate Silver module also sits on top of the daughter
1 A22XSR22XSR36R254B

VRST ’21, December 8–10, 2021, Osaka, Japan

Salemi Parizi, et al.

board to facilitate sending the data to a host PC over Bluetooth.
Using Velcro tape, the armband also hold a small battery pack thatpowers the Arduino and other components on the board.

3.3

hTracking Algorithm

We use a ToF-sensing array to track wrist orientation by comparing
the distance measurement from each sensor to the hand. The conical fields of view of each individual ToF sensor overlap significantly,
so several sensors simultaneously produce an in-range sensor measurement. As parts of the hand move in and out of the field of viewof each ToF sensor, the distance reading from that sensor becomes
smaller and larger.
We use a simple tracking algorithm that reliably tracks wristorientations across different users. As shown in Figure 3, we nameeach sensor in a clockwise fashion starting on the radial side from
S 1 to S 8 . As expected, we observe that the topmost (S 2 , S 3 , S 4 ) andbottommost (S 6 , S 7 , S 8 ) sensors have the most linear effect when
there is a flexion/extension activity (i.e. pitch) and the sensors on
the radial (S 8 , S 1 , S 2 ) and ulnar (S 4 , S 5 , S 6 ) sides are most responsive
to radial/ulnar deviation (i.e. yaw). We model these observations
using Equations 1 and 2 where D Si refers to the distance value thatis returned by the sensor Si at a certain point in time. For each of
the equations, there is a weight term associated with each of thepysensors (Wsi and Wsi ) which defines the individual weight given toa particular sensor and (Bp and By ) refer to a bias offset value.
Pitch =

Õ Wpsi

i=2,3,4

Yaw =

D Si

Õ Wy
si

i=1,2,8D Si−

Õ Wp !
si

i=6,7,8−D Si

Õ Wy !
si

i=4,5,6

D Si

+ Bp

(1)

+ By

(2)

These 14 parameters (7 each in pitch and yaw) are learned in auser evaluation explained in Section 5. We formulated the problem
as a non-linear optimization problem which we solved using aLevenberg-Marquardt algorithm.

4

k1

USER EVALUATION SETUP

A ground truth system is required to evaluate RotoWrist orientation accuracy. Since the wrist is not a rigid body and the skin is
stretchable, tracking the angle of the wrist with respect to the arm ischallenging. The most precise method of measuring the wrist angle
is to use radiographs to measure the carpal bone angle on lateralwrist [20]. Prior research has looked at building a custom-madecalibration device that has pivot joints with potentiometers to measure the wrist angle [37]. Since building such devices would affectthe IR reading from our wristband (due to the calibration device
occluding the IR sensors) and doing radiographs is not practical, we
used a fifteen-camera OptiTrack2 motion capture system to record
the real-time pose of the wrist, forearm, and hand stub at 240 Hz.
We placed retro-reflective spheres on both the controller armband
and sensing wristband in known locations, as shown in Figure 4.
The participant wore the wristband and armband on their left hand
2 OptiTrack Prime 17W

w4
w2

w1

w3

x

a1

y

a2
z

a4
a3

Figure 4: Retro-reflective markers placed on the wristband,armband and middle finger knuckle to facilitate tracking.
and a researcher placed a small IR retro-reflective marker on theirmiddle finger metacarpophalangeal joint.
We placed the armband retro-reflective markers in a way thattwo of the four are in the direction of the arm (markers a 2 and a 3 ),and two are perpendicular to the arm (markers a 1 and a 2 ) as shownin Figure 4. The markers (a1, a2, a3) define our arm coordinate
frame:z® = a 2 − a 3x® = a 1 − a 2y® = z® × x®

(3)

We also placed four retro-reflective markers on the sensing wristband. Markers w 1 , w 2 , and w 3 are used to help track the arm orientation in Section 6. w 4 is placed on the top middle part of thewristband and, together with k 1 , defines the wrist orientation vector
as h® = k 1 − w 4 . The placement of w 4 and k 1 markers is chosen sothat when a person has a neutral hand pose (i.e. zero pitch and yaw),h® is parallel to z® and perpendicular to x.
® Given the wrist orientation
vector h® and the arm coordinates (®
x, y,® z®), pitch and yaw of the wrist
can be defined as follow:® y)pitch = arccos(h.
®

5

® x)
yaw = arccos(h.
®

(4)

USER EVALUATION 1: ANGULAR
ACCURACY

We performed two focused studies to evaluate the performance of
RotoWrist. The first study evaluates the dynamic tracking accuracyof our system compared to a motion capture ground truth system.
Maintaining performance across worn sessions (i.e., reworn at alater time) or between different people is a challenge for almost all
bio-sensing and wearable systems, since misalignment could resultin signal change. The following sections evaluates the robustnessof RotoWrist in different scenarios.

RotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband

VRST ’21, December 8–10, 2021, Osaka, Japan

Yaw apx = 200 ×

i=2,3,4

i=6,7,8ÕÕ

i=1,2,8

1−
D Si

i=4,5,6

1
D Si

Orientation Error
Pitch
Yaw

7
6
5

Degrees

We recruited 14 people (25-59 years old, Mean: 32) to evaluate
the tracking accuracy of our system. The study took less than 30
minutes and participants were compensated for their time. Upon
arrival, we asked the participants to sit on a chair while putting
their left arm on a table that was in front of them. A researcher
helped the participants put on the wrist and arm band and theretro-reflective marker on the left hand of the user as explained in
Section 4. Based on their wrist sizes, five people wore the smaller
sized wristband and the other nine were given the larger wristband.
The wristband was placed where a user might typically wear asmartwatch (roughly at the tip of the ulna) as depicted in Figure 3.
Using the elastic bands, the wristband was fit to the user making
sure they can move their wrist without discomfort. The locationof the armband is roughly placed in the middle of the forearm in a
way that the markers are facing the user. For consistency, we asked
users to maintain a fist pose throughout the study. Anecdotally,
RotoWrist is robust to different finger poses, though we have notfully evaluated this effect.
The evaluation was conducted in two sessions and each session
had two phases. Phase 1, Defined motion: Participants were asked tohold their wrist in a neutral position in the beginning of each session
and then perform three flexion/extension and three radial/ulnar
deviation movements, exercising their full, comfortable range of
motion. Phase 2, Random motion: Participants performed 4 minutesof free-form movements while being asked to exercise all possible
wrist joints motions. After the two phases in the first session, thewristband was taken completely off. The users were instructed
to take a minimum of 2 minutes of break. After the break, theresearcher asked the participants to put the wristband back on, andthe same procedure was given to the user and the two phases wererepeated.
Once data has been recorded from both the 50 Hz RotoWrist
system and the 240 Hz motion capture system, we must align andsynchronize the two data streams. Such a synchronization requires
the comparison of correlated events in each data stream, but the raw
sensor data is uncorrelated with the ground truth wrist orientation
given by Equations 4. To address this challenge, we use the timealignment technique presented in [40]. We compare the groundtruth wrist orientation with an approximation of pitch and yaw
specified by Equation 5 and 6. We use these signals to achievealignment at the start and end of the recorded data streams. We
then resample the motion capture system through interpolation to
50 Hz to achieve frame-by-frame alignment with the RotoWrist.
!
Õ
Õ
1
1
Pitchapx = 150 ×
−
(5)
D Si
D Si

4
3
2
1
0
Cross-User

Cross-Session

Per-Session

Figure 5: Shows the root mean square error and standard deviation among 14 participants using the three models.

Although using a motion capture system to capture the groundtruth of the orientation of the wrist with respect to the forearmis practical, this method does not capture the full complexity of
the human hand anatomy. For example, in a neutral pose (i.e both
pitch and yaw are zero), we expect that the h® is perfectly alignedwith z®. This is not always true due to the geometry of the hand.
Therefore in evaluating RotoWrist, we add a one time offset, toforce the pitch and yaw estimation from Equations 1 and 2 to startat zero. This offset is only required to compare RotoWrist estimateswith the wrist orientation derived from ground truth and is notnecessary for normal operation. We evaluate the tracking accuracyof RotoWrist in three different categories: 1) cross-user where no
calibration is needed, 2) cross-session, and 3) per-session where auser can perform a short calibration.

5.1

Cross-User Evaluation

As stated in Section 1, a widespread input solution should be robust and extensible (e.g. supporting different users and contexts
out of the box). Therefore, in this section, we evaluate how our
tracking holds between users. Notably, we learned the heuristic
model parameters presented in Section 3.3 on only phase 1 (defined
motion) from the first session of all participants except one andtested on all of the data (including phases 1 and 2) from the left out
user. We repeat this for all of the participants. We learn a total of 14
parameters for this evaluation: 12 individual sensor weights, and 2
bias terms. The measured root mean square error (RMSE) for pitch
and yaw across users is 5.9° and 6.8° respectively.

!
(6)

After synchronization, we formulated the problem as a nonlinear optimization problem which we solved using a LevenbergMarquardt algorithm. The cost function for this optimization problem was set to be the difference in the pitch and yaw estimation from
Equation 1 and 2 and the angles calculated from motion capturesystem using Equation 4 (Section 4).

5.2

Cross-Session Evaluation

To quantify how the performance holds when reworn by the sameuser, we ran a leave-one-session-out cross validation for each of
our participants. We learned the same heuristic model parameterspresented in Section 3.3 on only phase 1 (defined motion) fromsession one. After learning these parameters, we test on all data(phases 1 and 2) from session two. The pitch and yaw RMSE indegrees across all users is 4.2° and 4.0° respectively.

VRST ’21, December 8–10, 2021, Osaka, Japan

Pitch

1.0

Salemi Parizi, et al.

Error distribution for pitch

Yaw

1.0

Error distribution for yaw

12

43
35

0.8

0.8

27
10
19

0.6

0.4

0.4

0.2

0.2

11

CDF

0.6

0.0

Cross-User
Cross-Session
Per-Session

Pitch (degrees)

3

8

-5
-13
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6

-29

0.0
0

5

10

Error (degrees)

0

5

10

Error (degrees)

-37
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4

-53

5.3

Per-Session Evaluation

Although we don’t expect users to calibrate RotoWrist each session,
to better shine a light on how the tracking improves with a verysmall calibration procedure, we evaluate RotoWrist’s per-session
accuracy. To do this, we learned the heuristic model parameters(Equations 1 and 2) from phase 1 of each session and tested on phase
2 of the same session. The pitch and yaw RMSE in degrees using
this calibration reduces to 3.5° and 3.6° respectively. We summarizedthese results in Figure 5.
Figure 6 shows the cumulative distribution function (CDF) of
orientation accuracy between all three evaluations discussed above.
Figure 7 shows the spatial distribution of orientation tracking errorof the cross-user model projected onto a 2D plane among all users.
As depicted and expected, due to kinematics of the wrist, users
performed more negative yaw (flexion) and negative pitch (radialdeviation) movement which is aligned with the functional rangesof the wrist joint motions [30]. Due to the geometry of the wrist,
the trapezoid bone and the thumb are placed slightly outside of theforearm axis and because of that the performance of RotoWrist is
relatively better in radial than ulnar deviation as shown in Figure 7.

6

-69
-50
-44
-38
-32
-26
-20
-14
-8
-2
4
10
16
22
28
34

2

Yaw (degrees)

Yaw (degrees)

Figure 7: Heatmap showing spatial distribution of error
among all users for cross-user evaluation.

USER EVALUATION 2: POINTING

We conducted a second study to evaluate RotoWrist on a pointing task. Through this study we aim to understand whether RotoWrist’s accuracy is sufficient for a pointing task and how thisperformance compares with respect to ground truth wrist tracking
and ground truth forearm tracking. Forearm tracking can also beenabled via wrist-worn sensing [2, 33], but involves large, fatiguing
arm movements, and thus is a contrasting point of comparison towrist tracking. We further evaluate how well RotoWrist pointing
adheres to Fitts’ law.

6.1

-61

-50
-44
-38
-32
-26
-20
-14
-8
-2
4
10
16
22
28
34

Figure 6: Cumulative distribution function of pitch and yaw
orientation tracking error.

DesignSince the yaw motion range is smaller than the pitch, more researchis needed into how the angular movement across different wrist
rotational axes is mapped to the on-screen cursor. Prior work [10]Figure 8: 2D pointing study setup. Users sit on a chair anduse the space bar of a keyboard for target selection.
has shown that a mismatched mapping function between the different wrist motion axes can still yield useful results. To keep things
simple, we conducted the pointing evaluation along the yaw andpitch axes separately and in our 2D pointing study, we mapped
the yaw and pitch movements to a relative cursor movement (inthe x and y direction) on a nearby large display. For each of thetwo axes (yaw and pitch), we had three independent variables:
Method (GTF - Ground Truth Forearm Tracking, GTW – Groundtruth Wrist Tracking, R - RotoWrist), Distance (Pitch: 100, 200, 400
pixels; Yaw: 50, 100, 200 pixels) , Width (20, 40, 60 pixels) resulting
in 2 × 3 × 3 × 3 = 54 conditions. The distance and width conditions

RotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband

VRST ’21, December 8–10, 2021, Osaka, Japan

Table 1: Pointing Study: 3-way ANOVA Main effects and pairwise comparisons. No interaction effects were found.

Yaw MT

Yaw IT

Pitch MT

Pitch IT

Condition
MethodDistance
Width
MethodDistance
Width
MethodDistance
Width
MethodDistance
Width

ANOVA
F (2, 22)= 9.252, p < 0.005, η 2 = .457
F (2, 22)= 40.897, p< 0.001, η 2= .788
F (2, 22)= 48.817, p< 0.001, η 2= .816
F (2, 22)= 7.558, p < 0.005, η 2 = .407
F (2, 22)= 7.064, p < 0.005, η 2 = .391
F (1.29, 14.19)=8.54, p<0.01, η 2=.437
F (2, 22)= 13.034, p< 0.001, η 2= .542
F (2, 22)= 34.115, p < 0.001, η = .756
F (2, 22)= 36.413, p< 0.001, η 2= .768
F (2, 22)= 6.862, p < 0.01, η 2 = .384
F (2, 22)= 11.004, p<0.001, η 2=.500
F (2, 22)= 19.947, p< 0.001, η 2= .645

allowed us to investigate a realistic range of task difficulties. The
yaw and pitch distances are different since yaw has a lower angularrange of wrist motion. Based on initial pilots, we mapped the wrist
and forearm motion to pixels as follows: 1) For RotoWrist and GTW
tracking, we translated a single degree of angular wrist motion
along the yaw-pitch axis to 4.35 pixels along the x-y axis respectively. For GTF, a 1cm arm motion along the horizontal-verticalaxis (rotating the forearm from the elbow) translated to 20 pixels
along x-y respectively. In our pilot study, these numbers allowed
all users to be able to reach the targets.
For each condition, participants performed 5 repetitions. We
followed a within-subjects design where each participant performed
all conditions. We counterbalanced Axis and Method among theparticipants using a Latin square and randomized the Distance andWidth. We recruited 12 participants (25-50 years of old) for the
study. All except one was right-handed. The study took 75 mins
and participants were compensated for their time. In total, we had
12 participants × 54 conditions × 5 repetitions = 3240 trials.

6.2

Procedure

Participants sat on a chair in front of a computer display. To allow
participants to use the keyboard on a hard surface, we placed atable in front of them as shown in Figure 8. We used the same datacollection setup explained in Section 4 and calculated ground truth
wrist orientations using Equation 4. The study software was written
in PyGame.
Prior to beginning, participants were given a short instructionalsession in which they familiarized themselves with the tasks andhow should they move their wrist or arm to move the cursor and select the target. The on-screen cursor appeared as a black dot and thetargets appeared as red circles. We implemented a standard pointing task [1] where the next target with the appropriate distanceand width appeared after the current selection. For selection, weused a keyboard and asked the users to hit the space-bar when they
feel the black dot is in the red target. As is standard, we asked theusers to perform the task as quickly and as accurately as possible.
Participants were provided regular breaks.

6.3

Pairwise Comparisons
GTW < GT F (p < .05), GTW < R(p < .05)
50 < 100 < 200 (p < .05 ) for all
60 < 20(p < .001), 40 < 20(p < .001)
GTW < GT F (p < 0.005)
50< 200(p< .05), 100 < 200(p < .05)
60 < 20(p < .05), 40 < 20(p < .05)
GTW < GT F (p < .005), GTW < R(p < .05)
100 < 200 < 400(p < .05) for all
60 < 40 < 20(p < .05) for all
GTW < GT F (p < .05), GTW < R(p < .05)
100< 400(p< .05), 200< 400(p< .05)
60 < 20(p < .005), 40 < 20(p < .005)

MeasuresWe measure Movement Time (MT) – the time it took for the user tocomplete a trial, and Incorrect Trials (IT) – the number of incorrect
trials out of five for each condition. An incorrect trial is when a user
misses a target (i.e the space-bar is hit while the cursor is outside
the target) or if the movement time is an outlier. We removed theincorrect trials from the movement time measurement.

6.4

Results

For each axis, we conducted 3-way repeated measures ANOVAs
on movement time and incorrect trials. Greenhouse-Geisser corrections were applied for violations of sphericity. The results weresimilar for both axes. For both yaw and pitch, for Movement Timeand Incorrect Trials, we found significant main effects of all three
Method, Distance, and Width. No interaction effects were found.
Table 1 shows the main effects statistics.
Pairwise comparisons with Bonferroni corrections (Table 1) showedthat the effects of distance and width broadly adhered to Fitts’ law
with Movement Time increasing with increasing distance and decreasing with increasing width. Since there are no interaction effects, we focus on the effect of Method on Movement Time andIncorrect Trials.
6.4.1 Effect of Method. Figure 9 shows the movement time andincorrect trials for each method for both axes. Ground truth wrist
is faster than ground truth forearm or RotoWrist according to thepairwise comparisons, although the difference is only 0.25s. Thisshows that users can complete the pointing task using RotoWrist
across all distances and width while only being slightly slower thanground truth wrist tracking and similar to ground truth forearm
tracking. A similar trend is observed in incorrect trials.
6.4.2 Adherence to Fitts’ Law. Figure 10 illustrates the movement
time as a function of index of difficulty (ID) for the three different
methods among all participants. A linear regression model wasused to fit the data. All three methods show adherence to Fitts’law, albeit a weak one given the lower R 2 values. This shows thatalternate modeling approaches should be investigated to bettermodel pointing based on hand-tracking.

VRST ’21, December 8–10, 2021, Osaka, Japan

Incorrect Trial

Movement Time

0.8

0.8

1.0

0.6

1.0

0.4
0.5

GT-Forearm

GT-Wrist

0.0
GT-Forearm

RotoWrist

GT-Wrist

0.0
GT-Forearm

RotoWrist

GT-Wrist

0.0
GT-Forearm

0.0

0.5

0.2

0.2

RotoWrist

0.4

Incorrect Trial

1.5

1.0
1.5

0.6

2.0

1.2

2.0

1.2
1.0

Yaw

GT-Wrist

1.4

Pitch

RotoWrist

Movement Time

Salemi Parizi, et al.

Figure 9: Mean and standard deviation of movement timeand incorrect trials for pitch and yaw axis.

MT for Yaw (S)GT Forearm

RotoWrist

2.0

2.0

2.0

1.5

1.5

1.5

1.0

1.0

R 2 = 0.11
MT=1.05ID + 0.18

0.5
1

MT for Pitch (S)GT Wrist2

3

MT = 1.01 + ID × 0.13

1

2

3

2.0

1.5

1.5

1.5

R 2 = 0.24
MT = 1.02 + ID × 0.19

2

1.0
0.5

4

R 2 = 0.11
MT = 0.93 + ID × 0.14

24

MT = 1.00 + ID × 0.16

1

2.0

0.5

R 2 = 0.13

0.5
4

2.0

1.0

7.1

1.0

R 2 = 0.08

0.5
4

2

1.0
0.5

3

4

R 2 = 0.12
MT = 1.11 + ID × 0.14

24

Index of Difficulty

Figure 10: Yaw and Pitch Movement Time vs Fitts’ ID

Overall, the results show that RotoWrist enables pointing tasks tobe performed with performance that is only slightly below groundtruth wrist tracking. Although the RotoWrist has similar performance in movement time and error rate to ground truth forearm
tracking, it is still the preferred choice compared to ground truth
forearm tracking since it requires less movement from the user
and can be performed in a more subtle and comfortable manner.
This was profoundly echoed by the participants feedback where
they mentioned more fatigue when performing ground truth forearm movements for pointing task. Using a wearable device like
RotoWrist to track wrist pose is also easier from an implementation perspective than enabling forearm tracking, which requires
another point of instrumentation in the world or on the head (e.g.,
HMD-mounted cameras to track the arm).

7

Figure 11: A Rift-S controller is added to the controller armband to facilitate 6-DoF tracking of the hand.

Pointing

AR and VR platforms represent a promising direction for nextgeneration computing platforms. For many applications, being able
to point at objects precisely is of a great importance. The handheld
controller is a useful input device that is common in VR, but moving
the hands without the encumbrance of holding a physical handheld
controller could lead to more natural and casual experiences.
To demonstrate the potential of RotoWrist as a pointing device,
we demonstrate how it can be used to play Beat Saber, one of themost popular VR experiences. The game is normally controlled
by two handheld controllers, and is particularly challenging as it
requires precise targeting and low-latency.
To build this demo, we attached a Rift-S handheld controller
to the top of the controller armband as shown in Figure 11. The
controller tracked the user’s arm pose with respect to the headset.
During a simple calibration phase, we determine the transformationfrom the pose of the Rift-S controller to the pose of the hand. At
runtime, we add the wrist angles estimated from RotoWrist to thetransformed pose of the controller to accurately capture the 6-DoF
pose of the hand.
We asked ten users to try our demo for one song and provide
feedback. We purposefully had them play the game using a Rift-S
handheld controller on the right hand while having our device on
their left hand so that they can compare the two input devices. Almost all users were very excited about the idea of playing BeatSaber
without a handheld controller and were impressed by the tracking
accuracy. Two people mentioned that they felt that RotoWrist wasmore delayed than the Rift-S handheld controller but not enoughto make them uncomfortable. Further improvements in speed andaccuracy may be possible by using a Kalman filter to proactively
estimate the next wrist orientation.

APPLICATIONS

The ability to track one’s wrist orientation reliably and precisely
without user training enables a wide range of applications. We
envision these applications to be with or without a HMD. In thissection, we discuss and demonstrate two features that RotoWrist
can enable.

7.2

Free-form Hand Drawing

To highlight RotoWrist’s ability to perform fine-grained continuous tracking (beyond gestural swipes), we implemented a drawing
application driven solely by the user’s wrist orientation. Since thewrist has smaller range of motion in radial/ulnar deviation, theRotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband

20

Pitch (degrees)

15
10
5
0
5
10
60

40

20
Yaw (degrees)

020

Figure 12: Handwriting examples using wrist-only motion.

drawing canvas shifts to the left by a few pixels each frame toenable an infinite canvas. This free-form drawing can be used to
draw shapes for gestures recognition or can be used for handwriting reconstruction. Figure 12 shows a user that used RotoWrist to
write "hello" in the mid air. With appropriate visual feedback, we
anticipate the performance to be even better.

8

DISCUSSION

In this work, we demonstrated RotoWrist, a wearable wrist-worndevice that enables precise tracking of the orientation of the wrist.
Due to the simplicity of its tracking algorithm, RotoWrist does not
require user training to work. We also showed that with minimal additional calibration, pitch and yaw accuracy improves and achieves
an RMSE of 3.5° and 3.6° respectively.

8.1

Complementing Vision-based Wristband
Tracking

We envision RotoWrist’s use either with or without a head-mounted
display. However, even though head-mounted cameras are getting
better at tracking hands and fingers, they still require line-of-sight.
In contrast, tracking a wristband with infrared fiducial markers is amuch simpler task. The combination of RotoWrist with a wristband
tracker that results in a complete 6-DoF tracking system, may be
preferable over a handheld controller in some scenarios. For example, a user could comfortably interact with their hands at their side
where computer vision might fail, then seamlessly move into thefield-of-view of the cameras, which would begin tracking the armor wristband pose, and start direct manipulations. As a standalone
device, RotoWrist offers a rich input source for smartwatches. With
RotoWrist, a user can provide input using their wrist while keeping
their arm motionless at their side or resting on a table. With somemodifications, we envision that RotoWrist can capture information
about the pose of the fingers as well, e.g., detecting a fist vs a full
extended hand for target selection. This could be useful in mid-air
drag & drop interactions.

8.2

Limitations and Future WorkIn this paper, we focused on how RotoWrist can be generalized
across sessions and users. We will now discuss how RotoWrist

VRST ’21, December 8–10, 2021, Osaka, Japan

can be generalized across different palm posture, location on theforearm and rigidity. Although in our evaluations, we asked theparticipants to make a fist, we observed that the tracking holds
even as the user opens their fist and starts moving their fingers.
It’s worth mentioning that the tracking error increases as the user
moves their thumb or pinky finger outwards, making a motion that,to RotoWrist, appears similar to a radial/ulnar deviation motion.
Future work should consider how additional modeling or the useof additional sensors can distinguish between finger motion andwrist motion.
Although RotoWrist achieves a cross-user RMSE of 5.9° and 6.8°
for pitch and yaw, respectively, our system can be vulnerable tomajor changes in the location of the band. We expect this can beaddressed by using a few seconds of online learning. Furthermore,a challenge in the design of any wristband is sizing it for an appropriate fit across users. We also wanted the wristband to be rigid inorder for the ToF modules to be roughly at the same pose for allusers. Incorporating these modules in a flexible band requires more
attention as shifts in the position of the modules would degrade
performance. Furthermore, the relative angle of each module withrespect to the band is an important factor to consider. While incorporating RotoWrist in a flexible band, care should be take so thatthe modules do not change orientation.
RotoWrist consists of the sensing wristband with eight ToF IR
modules and a controller arm band that supplies powering the sensing wristband and handles data communicating back to a host PC.
Here, we have focused on the design of the sensing wristband andoptimizing the placement of the ToF IR modules. Incorporating thecontroller components into the wristband is a fairly straightforward task—most smart watches do have an MCU and Bluetooth. In
designing the sensing wristband, we placed the ToF modules evenly
around the wristband. RotoWrist’s performance could be improved
further by additional optimization of the number and placement of
ToF modules on the wristband. Future work could also investigate
tracking accuracy with fewer sensors.
If RotoWrist is combined with other tracking systems, e.g., an
HMD-based hand tracker, the additional calibration step discussed
in Sections 5.2 and 5.3 could be done automatically during the firstfew seconds of use. After this, the hand tracking from the headset
can stop and RotoWrist will be used for tracking.

9

CONCLUSION

In this work, we demonstrated RotoWrist, a wearable wrist-worndevice that performs continuous 2-degree-of-freedom (DoF) wrist
tracking using ToF IR sensing modules. RotoWrist works acrossusers without requiring user calibrations. The RotoWrist system
consists of a wristband that uses eight ToF IR modules to continuously measures the absolute distance from the hand in real-time.
As the user moves their wrist, the relative distance between thehand and each sensor changes and collectively RotoWrist uses thesemeasurements to track 2-DoF wrist orientation—flexion/extension
and radial/ulnar deviation. RotoWrist offers a rich input source fora variety of wearable devices, including smartwatches and HMDs.

VRST ’21, December 8–10, 2021, Osaka, JapanACKNOWLEDGMENTS
We wish to thank Slava Karulin, Richard Cardin, and Jeff Holter
for their help in designing and building RotoWrist’s hardware andphysical prototype. We also like to thank Aria Fereydouni for his
help in conducting the user studies.

REFERENCES[1] [n.d.]. ISO, 9421-9 Ergonomic requirements for office work with visual display terminals (VDTs) - Part 9: Requirements for non-keyboard input devices.
International Organization for Standardization. 2000.
[2] Javid Abbasov, Tom Horak, and Raimund Dachselt. 2018. Smartwatch-based
Pointing Interaction. In Mensch und Computer 2018 - Tagungsband, Raimund
Dachselt and Gerhard Weber (Eds.). Gesellschaft für Informatik e.V., Bonn. https://doi.org/10.18420/muc2018-mci-0373[3] Ke-Yu Chen, Kent Lyons, Sean White, and Shwetak Patel. 2013. UTrack: 3DInput Using Two Magnetic Sensors. In Proceedings of the 26th Annual ACM
Symposium on User Interface Software and Technology (St. Andrews, Scotland,
United Kingdom) (UIST ’13). Association for Computing Machinery, New York,NY, USA, 237–244. https://doi.org/10.1145/2501988.2502035[4] Ke-Yu Chen, Shwetak N. Patel, and Sean Keller. 2016. Finexus: Tracking Precise
Motions of Multiple Fingertips Using Magnetic Sensing. In Proceedings of the 2016
CHI Conference on Human Factors in Computing Systems (San Jose, California,
USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA,
1504–1514. https://doi.org/10.1145/2858036.2858125[5] F. Cordella, F. Di Corato, G. Loianno, B. Siciliano, and L. Zollo. 2013. Robust pose
estimation algorithm for wrist motion tracking. In 2013 IEEE/RSJ International
Conference on Intelligent Robots and Systems. 3746–3751. https://doi.org/10.1109/
IROS.2013.6696891[6] Artem Dementyev and Joseph A. Paradiso. 2014. WristFlex: Low-power Gesture
Input with Wrist-worn Pressure Sensors. In Proceedings of the 27th Annual ACM
Symposium on User Interface Software and Technology (Honolulu, Hawaii, USA)(UIST ’14). ACM, New York, NY, USA, 161–166. https://doi.org/10.1145/2642918.
2647396[7] T. Deyle, S. Palinko, E. S. Poole, and T. Starner. 2007. Hambone: A Bio-Acoustic
Gesture Interface. In 2007 11th IEEE International Symposium on Wearable Computers. 3–10. https://doi.org/10.1109/ISWC.2007.4373768[8] Jun Gong, Zheer Xu, Qifan Guo, Teddy Seyed, Xiang “Anthony” Chen, Xiaojun
Bi, and Xing-Dong Yang. 2018. WrisText: One-Handed Text Entry on Smartwatch Using Wrist Gestures. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, Article 181, 14 pages.
https://doi.org/10.1145/3173574.3173755[9] Jun Gong, Xing-Dong Yang, and Pourang Irani. 2016. WristWhirl: One-handed
Continuous Smartwatch Input Using Wrist Gestures. In Proceedings of the 29th
Annual Symposium on User Interface Software and Technology (Tokyo, Japan) (UIST’16). ACM, New York, NY, USA, 861–872. https://doi.org/10.1145/2984511.2984563[10] Aakar Gupta, Cheng Ji, Hui-Shyong Yeo, Aaron Quigley, and Daniel Vogel. 2019.
RotoSwype: Word-Gesture Typing Using a Ring. In Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI
’19). Association for Computing Machinery, New York, NY, USA, Article 14,
12 pages. https://doi.org/10.1145/3290605.3300244[11] Chris Harrison, Desney Tan, and Dan Morris. 2010. Skinput: Appropriating the
Body as an Input Surface. In Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for
Computing Machinery, New York, NY, USA, 453–462. https://doi.org/10.1145/
1753326.1753394[12] Fang Hu, Peng He, Songlin Xu, Yin Li, and Cheng Zhang. 2020. FingerTrak:
Continuous 3D Hand Pose Tracking by Deep Learning Hand Silhouettes Captured
by Miniature Thermal Cameras on Wrist. Proc. ACM Interact. Mob. Wearable
Ubiquitous Technol. 4, 2, Article 71 (June 2020), 24 pages. https://doi.org/10.1145/
3397306[13] Yasha Iravantchi, Mayank Goel, and Chris Harrison. 2019. BeamBand: Hand
Gesture Sensing with Ultrasonic Beamforming. In Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI
’19). ACM, New York, NY, USA, Article 15, 10 pages. https://doi.org/10.1145/
3290605.3300245[14] Yasha Iravantchi, Yang Zhang, Evi Bernitsas, Mayank Goel, and Chris Harrison.
2019. Interferi: Gesture Sensing Using On-Body Acoustic Interferometry. In
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk) (CHI ’19). ACM, New York, NY, USA, Article 276, 13 pages.
https://doi.org/10.1145/3290605.3300506[15] P. Jung, G. Lim, S. Kim, and K. Kong. 2015. A Wearable Gesture Recognition
Device for Detecting Muscular Activities Based on Air-Pressure Sensors. IEEE
Transactions on Industrial Informatics 11, 2 (April 2015), 485–494. https://doi.
org/10.1109/TII.2015.2405413

Salemi Parizi, et al.

[16] Frederic Kerber, Pascal Lessel, and Antonio Krüger. 2015. Same-Side Hand
Interactions with Arm-Placed Devices Using EMG. In Proceedings of the 33rd
Annual ACM Conference Extended Abstracts on Human Factors in Computing
Systems (Seoul, Republic of Korea) (CHI EA ’15). Association for Computing
Machinery, New York, NY, USA, 1367–1372. https://doi.org/10.1145/2702613.
2732895[17] Wolf Kienzle and Ken Hinckley. 2014. LightRing: Always-Available 2D Input on
Any Surface. In Proceedings of the 27th Annual ACM Symposium on User Interface
Software and Technology (Honolulu, Hawaii, USA) (UIST ’14). Association for
Computing Machinery, New York, NY, USA, 157–160. https://doi.org/10.1145/
2642918.2647376[18] David Kim, Otmar Hilliges, Shahram Izadi, Alex D. Butler, Jiawen Chen, Iason
Oikonomidis, and Patrick Olivier. 2012. Digits: Freehand 3D Interactions Anywhere Using a Wrist-Worn Gloveless Sensor. In Proceedings of the 25th Annual
ACM Symposium on User Interface Software and Technology (Cambridge, Massachusetts, USA) (UIST ’12). Association for Computing Machinery, New York,NY, USA, 167–176. https://doi.org/10.1145/2380116.2380139[19] Gierad Laput, Robert Xiao, and Chris Harrison. 2016. ViBand: High-Fidelity BioAcoustic Sensing Using Commodity Smartwatch Accelerometers. In Proceedingsof the 29th Annual Symposium on User Interface Software and Technology (Tokyo,
Japan) (UIST ’16). Association for Computing Machinery, New York, NY, USA,
321–333. https://doi.org/10.1145/2984511.2984582[20] Claus Falck Larsen, Finn K. Mathiesen, and Steen Lindequist. 1991. Measurementsof carpal bone angles on lateral wrist radiographs. The Journal of Hand Surgery
16, 5 (1991), 888 – 893. https://doi.org/10.1016/S0363-5023(10)80156-X[21] Jaime Lien, Nicholas Gillian, M. Emre Karagozler, Patrick Amihood, Carsten
Schwesig, Erik Olson, Hakim Raja, and Ivan Poupyrev. 2016. Soli: Ubiquitous
Gesture Sensing with Millimeter Wave Radar. ACM Trans. Graph. 35, 4, Article
142 (July 2016), 19 pages. https://doi.org/10.1145/2897824.2925953[22] Jess McIntosh, Asier Marzo, and Mike Fraser. 2017. SensIR: Detecting Hand
Gestures with a Wearable Bracelet using Infrared Transmission and Reflection.
593–597. https://doi.org/10.1145/3126594.3126604[23] Scott D. McPhee. 1987. Functional Hand Evaluations: A Review. American Journalof Occupational Therapy 41, 3 (03 1987), 158–163. https://doi.org/10.5014/ajot.41.
3.158 arXiv:https://ajot.aota.org/aota/content_public/journal/ajot/930394/158.pdf[24] Franziska Mueller, Florian Bernard, Oleksandr Sotnychenko, Dushyant Mehta,
Srinath Sridhar, Dan Casas, and Christian Theobalt. 2018. GANerated Hands for
Real-Time 3D Hand Tracking From Monocular RGB. In The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR).
[25] Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan, and Shyamnath Gollakota.
2016. FingerIO: Using Active Sonar for Fine-Grained Finger Tracking. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose,
California, USA) (CHI ’16). Association for Computing Machinery, New York, NY,
USA, 1515–1525. https://doi.org/10.1145/2858036.2858580[26] Farshid Salemi Parizi, Eric Whitmire, and Shwetak Patel. 2019. AuraRing: Precise
Electromagnetic Finger Tracking. Proc. ACM Interact. Mob. Wearable UbiquitousTechnol. 3, 4, Article 150 (Dec. 2019), 28 pages. https://doi.org/10.1145/3369831[27] Siddharth S. Rautaray and Anupam Agrawal. 2015. Vision Based Hand Gesture
Recognition for Human Computer Interaction: A Survey. Artif. Intell. Rev. 43, 1(Jan. 2015), 1–54. https://doi.org/10.1007/s10462-012-9356-9[28] J. Rekimoto. 2001. GestureWrist and GesturePad: unobtrusive wearable interaction devices. In Proceedings Fifth International Symposium on Wearable Computers.
21–27. https://doi.org/10.1109/ISWC.2001.962092[29] Zhou Ren, Junsong Yuan, and Zhengyou Zhang. 2011. Robust Hand Gesture
Recognition Based on Finger-Earth Mover’s Distance with a Commodity Depth
Camera. In Proceedings of the 19th ACM International Conference on Multimedia(Scottsdale, Arizona, USA) (MM ’11). Association for Computing Machinery, New
York, NY, USA, 1093–1096. https://doi.org/10.1145/2072298.2071946[30] Jaiyoung Ryu, William P. Cooney, Linda J. Askew, Kai-Nan An, and Edmund Y.S.
Chao. 1991. Functional ranges of motion of the wrist joint. The Journal of Hand
Surgery 16, 3 (1991), 409 – 419. https://doi.org/10.1016/0363-5023(91)90006-W[31] T. Scott Saponas, Desney S. Tan, Dan Morris, Ravin Balakrishnan, Jim Turner,
and James A. Landay. 2009. Enabling Always-Available Input with MuscleComputer Interfaces. In Proceedings of the 22nd Annual ACM Symposium on User
Interface Software and Technology (Victoria, BC, Canada) (UIST ’09). Association
for Computing Machinery, New York, NY, USA, 167–176. https://doi.org/10.
1145/1622176.1622208[32] Arpita Ray Sarkar, G. Sanyal, and S. Majumder. 2013. Article: Hand Gesture
Recognition Systems: A Survey. International Journal of Computer Applications
71, 15 (June 2013), 25–37. Full text available.
[33] Shaishav Siddhpuria, Sylvain Malacria, Mathieu Nancel, and Edward Lank. 2018.
Pointing at a Distance with Everyday Smart Devices. In Proceedings of the 2018
CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada)(CHI ’18). Association for Computing Machinery, New York, NY, USA, Article
173, 11 pages. https://doi.org/10.1145/3173574.3173747[34] Paul Strohmeier, Roel Vertegaal, and Audrey Girouard. 2012. With a Flick of theWrist: Stretch Sensors as Lightweight Input for Mobile Devices. In Proceedings of
the Sixth International Conference on Tangible, Embedded and Embodied Interaction

RotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband(Kingston, Ontario, Canada) (TEI ’12). Association for Computing Machinery,
New York, NY, USA, 307–308. https://doi.org/10.1145/2148131.2148195[35] Jonathan Taylor, Lucas Bordeaux, Thomas Cashman, Bob Corish, Cem Keskin,
Toby Sharp, Eduardo Soto, David Sweeney, Julien Valentin, Benjamin Luff, andet al. 2016. Efficient and Precise Interactive Hand Tracking through Joint, Continuous Optimization of Pose and Correspondences. ACM Trans. Graph. 35, 4,
Article 143 (July 2016), 12 pages. https://doi.org/10.1145/2897824.2925965[36] Hoang Truong, Shuo Zhang, Ufuk Muncuk, Phuc Nguyen, Nam Bui, Anh Nguyen,
Qin Lv, Kaushik Chowdhury, Thang Dinh, and Tam Vu. 2018. CapBand: Batteryfree Successive Capacitance Sensing Wristband for Hand Gesture Recognition. In
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems(Shenzhen, China) (SenSys ’18). ACM, New York, NY, USA, 54–67. https://doi.
org/10.1145/3274783.3274854[37] D. M. Vogt and R. J. Wood. 2014. Wrist angle measurements using soft sensors. In
SENSORS, 2014 IEEE. 1631–1634. https://doi.org/10.1109/ICSENS.2014.6985332[38] L. Wang, K. Sun, H. Dai, W. Wang, K. Huang, A. Liu, X. Wang, and Q. Gu. 2019.
WiTrace: Centimeter-Level Passive Gesture Tracking Using OFDM signals. IEEE
Transactions on Mobile Computing (2019), 1–1.
[39] Robert Wang, Sylvain Paris, and Jovan Popoviundefined. 2011. 6D Hands: Markerless Hand-Tracking for Computer Aided Design. In Proceedings of the 24th
Annual ACM Symposium on User Interface Software and Technology (Santa Barbara, California, USA) (UIST ’11). Association for Computing Machinery, New
York, NY, USA, 549–558. https://doi.org/10.1145/2047196.2047269[40] Eric Whitmire, Farshid Salemi Parizi, and Shwetak Patel. 2019. Aura: Inside-out
Electromagnetic Controller Tracking. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (Seoul, Republic of Korea) (MobiSys ’19). ACM, New York, NY, USA, 300–312. https://doi.org/10.1145/3307334.3326090

VRST ’21, December 8–10, 2021, Osaka, Japan[41] Erwin Wu, Ye Yuan, Hui-Shyong Yeo, Aaron Quigley, Hideki Koike, and Kris M.
Kitani. 2020. Back-Hand-Pose: 3D Hand Pose Estimation for a Wrist-Worn
Camera via Dorsum Deformation Network. In Proceedings of the 33rd Annual
ACM Symposium on User Interface Software and Technology (Virtual Event, USA)(UIST ’20). Association for Computing Machinery, New York, NY, USA, 1147–1160.
https://doi.org/10.1145/3379337.3415897[42] Xing-Dong Yang, Tovi Grossman, Daniel Wigdor, and George Fitzmaurice. 2012.
Magic Finger: Always-Available Input through Finger Instrumentation. In Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology(Cambridge, Massachusetts, USA) (UIST ’12). Association for Computing Machinery, New York, NY, USA, 147–156. https://doi.org/10.1145/2380116.2380137[43] Hui-Shyong Yeo, Juyoung Lee, Hyung-il Kim, Aakar Gupta, Andrea Bianchi,
Daniel Vogel, Hideki Koike, Woontack Woo, and Aaron Quigley. 2019. WRIST:
Watch-Ring Interaction and Sensing Technique for Wrist Gestures and MacroMicro Pointing. In Proceedings of the 21st International Conference on HumanComputer Interaction with Mobile Devices and Services (Taipei, Taiwan) (MobileHCI’19). Association for Computing Machinery, New York, NY, USA, Article 19,
15 pages. https://doi.org/10.1145/3338286.3340130[44] Hui-Shyong Yeo, Erwin Wu, Juyoung Lee, Aaron Quigley, and Hideki Koike. 2019.
Opisthenar: Hand Poses and Finger Tapping Recognition by Observing Back ofHand Using Embedded Wrist Camera. In Proceedings of the 32nd Annual ACM
Symposium on User Interface Software and Technology (New Orleans, LA, USA)(UIST ’19). Association for Computing Machinery, New York, NY, USA, 963–971.
https://doi.org/10.1145/3332165.3347867[45] Yang Zhang and Chris Harrison. 2015. Tomo: Wearable, Low-Cost Electrical
Impedance Tomography for Hand Gesture Recognition. In Proceedings of the 28th
Annual ACM Symposium on User Interface Software &#38; Technology (Charlotte,NC, USA) (UIST ’15). ACM, New York, NY, USA, 167–173. https://doi.org/10.
1145/2807442.2807480