DigituSync: A Dual-User Passive Exoskeleton Glove That
Adaptively Shares Hand Gestures
Jun NishidaYudai Tanaka

University of Chicago
jun.nishida@acm.org

University of Chicago
yudaitanaka@uchicago.edu

Romain Nith

Pedro Lopes

University of Chicagornith@uchicago.edu

University of Chicago
pedrolopes@uchicago.edu

Figure 1: (a) We engineered DigituSync, a passive exoskeleton that allows two users to share the same hand pose. DigituSync
requires no electronics to transmit forces and has virtually no latency, making it ideal for fine-motor skill transmission. In
addition, (b) each finger mechanism features variable-length linkages that allow adjusting the amount of force to be conveyed.
(c) Taken together, DigituSync is useful and safe to deploy in a variety of settings, such as between students and teachers in amusic classroom.

ABSTRACTWe engineered DigituSync, a passive-exoskeleton that physicallylinks two hands together, enabling two users to adaptively transmit
finger movements in real-time. It uses multiple four-bar linkages
to transfer both motion and force, while still preserving congruent haptic feedback. Moreover, we implemented a variable-length
linkage that allows adjusting the force transmission ratio between
the two users and regulates the amount of intervention, whichenables users to customize their learning experience. DigituSync’s
benefits emerge from its passive design: unlike existing haptic devices (motor-based exoskeletons or electrical muscle stimulation),
DigituSync has virtually no latency and does not require batteries/electronics to transmit or adjust movements, making it usefuland safe to deploy in many settings, such as between students andteachers in a classroom. We validated DigituSync by means of technical evaluations and a user study, demonstrating that it instantly
transfers finger motions and forces with the ability of adaptive force
transmission, which allowed participants to feel more control overtheir own movements and to feel the teacher’s intervention was
more responsive. We also conducted two exploratory sessions witha music teacher and deaf-blind users, which allowed us to gather
experiential insights from the teacher’s side and explore DigituSync
in applications.

CCS CONCEPTS• Human-centered computing; • Haptic devices;

KEYWORDS
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
UIST ’22, October 29–November 02, 2022, Bend, OR, USA© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9320-1/22/10. . . $15.00
https://doi.org/10.1145/3526113.3545630

Exoskeletons, Mechanics, Haptics, Skill sharing

ACM Reference Format:
Jun Nishida, Yudai Tanaka, Romain Nith, and Pedro Lopes. 2022. DigituSync:
A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures. In The 35th Annual ACM Symposium on User Interface Software andTechnology (UIST ’22), October 29–November 02, 2022, Bend, OR, USA. ACM,
New York, NY, USA, 12 pages. https://doi.org/10.1145/3526113.3545630

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

1

Jun Nishida et al.

INTRODUCTION

Learning dexterous hand skills plays an important role in ourlives, from using highly dexterous interfaces (e.g., keyboards, touch
screens), playing musical instruments [32, 33, 35, 49, 76, 106], performing surgical procedures [11], and even assisting others with
their movements, such as in hand rehabilitation [1, 18, 82, 92]. These
interactions require precise and stable finger pose, force, speed, andtiming (rhythms) control over individual fingers and their coordination [33, 49, 53, 69, 81, 108]. Given the complexity of these activities,
learning them takes a substantial amount of time due to the needto acquire many fundamental skills (e.g., simultaneous control offinger pose, force, speed, rhythm) that are hard for a novice user tounderstand by just passively watching a skilled user.
As such, a significant amount of research in interactive systems
has been dedicated to creating devices that can support transferringskills. Popular examples of such devices include those that sharevisual perspectives using either head-mounted displays (HMD)[4, 44, 45, 47, 52, 101] or projection mapping systems [50, 76, 106],both of which allow a student to view the teacher’s hand movements from the teacher’s perspective. While these approaches scale
up well, they do not transmit all the rich information that underlies skilled movement, such as force control. As a response, many
researchers have focused on designing wearable devices that provide the missing haptics by leveraging exoskeletons with active
motors [6, 25, 31, 56, 61, 62, 78, 94], electrical muscle stimulation[29, 55, 66, 68, 89], or even pneumatics [22, 87]. Providing haptic
feedback directly to a novice user’s body has often proven to beeffective in motor learning scenarios such as learning rhythmic
activities [22, 24] or recovering grasping capability after injuries[6, 13, 30, 99].
However, active haptic devices (e.g., motor-based exoskeletons)
are not without limitations. This includes limitations such as (1)end-to-end latency, which is problematic in skill-sharing [71]—in
fact, even assuming a collocated system (no network; teacher andlearner see each other), just 200ms of haptic latency (very fast for
motor-based exoskeletons) will be detrimental to the learning experience [28, 71], the sense of agency [5, 17, 79, 98], and the learning[46, 98]; (2) prohibitive cost, a pair of exoskeletons, such as Dexmo[25], might sell anywhere between 2400-45000 USD (actual quotes
from vendors), which prevents exoskeletons and other active haptic
devices from being widely used in school or home settings; (3)adaptive feedback: the challenges of the manufacturing cost andthe setup effort in active haptic devices made researchers explore
simpler and direct techniques such as “hand-over-hand guidance”[8, 82]—a technique in which the therapist assists a user in performing a task by physically grabbing the user’s hands or wrists,
or even using simple knitted joined-suits or gloves to synchronize
two bodies’ movements [100]; while these simple techniques canprovide instant haptic feedback, these are not designed to transmit
complex gestures (e.g., knitted joined-gloves have a lot of mechanical slack) nor these provide adjustable force feedback, which playsa critical role in skill acquisition and learning experience including
the feeling of agency and the motivation [2, 23, 32, 42, 80, 96, 111].
In this paper, we take a different approach to realizing fingerskill transfer, one that is inspired by the precision and dexterityof exoskeletons but that circumvents their adjustability, latency,

Figure 2: Sharing real-time finger motion and force throughlinkages between two users

and prohibitive cost. To achieve this, we engineered DigituSync, acompletely passive dual-user hand-exoskeleton. DigituSync allows
two users to share the pose, force, speed, and timing of their finger
movements on all fingers via mechanical linkages. Because thecore of DigituSync is entirely 3D printed and has no electronics, its
cost is as low as 40 USD, which we hope will enable such devices
to be adopted in large classrooms. Moreover, as DigituSync is designed for providing adaptive feedback to a learner, we engineered
it with end-user customization in mind, i.e., the teacher or the student can change the force transmission ratio to adjust the level ofintervention in proportion to their learning progress.

2

OUR APPROACH: SHARING DEXTEROUS
HAND GESTURES THROUGH PHYSICAL
LINKAGES

Our key contribution is the design, engineering, and evaluationof a dual-user passive exoskeleton that allows users to share theirhand postures in dexterous activities, as depicted in Figure 2. Our
approach has the following benefits: (1) adaptive, real-time, andcongruent haptics: unlike active exoskeletons which require sensors and actuators, our passive exoskeleton users can experience
real-time haptics (e.g., pressure, rigidity) without any perceptual
delay and much loss of fidelity. This is a direct benefit of our physical link. More importantly, our custom mechanics allow users tochange the amount of force feedback, while preserving the natureof real-time and congruent feedback, enabling customization of
their learning experience; (2) safe to use: our exoskeleton is entirely controlled by the users, without forces from motors, making
it extremely safe and easy to deploy; (3) wearable, walk-up-use,and low-cost: our device literally fits the user’s hands like a glove,
it is easy to understand and use without any explanations, which
we confirmed in our user studies. It also features magnetic linkages
that allow to attach and detach two users together, enabling students to receive haptic assistance just when needed. Lastly, thanks
to the passive form factor the cost is drastically reduced (40 USD),
making it easy to equip a classroom with dozens of DigituSync.
Naturally, our exoskeleton design is not without its limitations:(1) hand-over-hand guidance: the teacher can guide the learnerby positioning their hand over the learner’s hand, while this suits
applications such as piano, pegboard rehabilitation, occupational
therapy, etc.; it restricts the space of applications when comparedto motor-based decoupled exoskeletons; (2) finger motion: ourDigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures

current mechanism solely transmits vertical finger motion, while ateacher can rotate a learner’s hand through the wrist linkage. It also
places linkages on the finger’s dorsal side, which interfere with theuser’s palm, this prevents full grasp gestures; (3) the weight of theother: since both users’ hands are physically connected, users feelthe weight of the other user, which might impact the performance;
yet, in our studies, no participant mentioned this limitation. Lastly,note that we are not proposing to replace all existing haptic devices;
instead, we tend to think of DigituSync as a unique passive device
that is particularly useful for situations that require no latency, highaccuracy, and easily deployable in safe settings.

3 RELATED WORK
3.1 Hand Skills and Dexterity
Hand interactions are of one the central abilities of human beings[43]. A dexterous interaction, such as playing the piano or interacting with a computer keyboard, requires precise control of one’s individual fingers’ pose, force, speed, and timing [33, 49, 53, 69, 81, 108]—
usually in synchronization with one’s hands and eyes. Dexterity
played such a major role in evolution that much of our brain’s
capacity is dedicated to understanding the state of our hands. As
such, there is a lot of research in interactive systems that can allow
the learner to see from the teacher’s perspective.

3.2

Seeing from the teacher’s perspective

One popular and effective way to convey a person’s physical interactions to another is by visually depicting what the other seesfrom their perspective. This has been realized by visually projecting
their gestures [50, 76, 106] or by switching/blending both user’sviews using head-mounted displays (HMD) [4, 44, 45, 101]—theseapproaches allow, for instance, a teacher to transfer rhythmic hand
movements [47, 52]. These techniques have been further extended
by using virtual reality (VR) which also allows two persons to sharea virtual body [19, 26, 88]. While these visual-based approaches
allow for tele-learning, they do not transmit all the rich haptic cuesthat underlie skilled movements, such as pose, force, speed, andtiming control. While visual-based methods might apply for a subset of these skills, other cues are not being conveyed, such as force
control.

3.3

Feeling from the teacher’s perspective

As a response, researchers turned to engineer wearables that provide the missing haptics. For instance, even just sharing vibrotactile
cues allows us to teach rhythms [18, 34], melodies [35], and gait[63]. While vibration is exceptional in providing temporal and tactile information, it leaves out kinesthetic cues, such as forces, etc.
To this end, researchers often rely on force-feedback devices with
sufficient force to move the user’s body (typically with motors),
including large surgical telerobots that provide enhanced dexterity
to doctors or wearable robotic arms, such as Fusion [78] or Naviarm[56], which allow two users to transfer upper-limb motions, andelectrical muscle stimulation (EMS—a technique that directly stimulates the muscles with electrical pulses, causing them to feel force
feedback [14, 51, 70, 89–91, 109]. While EMS has been used to share
simple movements across multiple users [27, 29, 66, 68] and to teachdrumming [15], it still remains a key challenge to precisely actuate

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

multiple fingers simultaneously [86]. Aside from active actuators
or EMS, researchers have been also exploring the other side of thespectrum: passive mechanisms. These mechanisms, such as linkages
or hydrostatic transmissions, are increasingly popular since they
achieve safer, accurate, and direct bi-directional interaction. Examples include: a humanoid robot controlled by hydrostatic tubes froma remote location [103], sharing remote users’ presence throughmechanical rollers [12], changing the dimensions of the user’s hand
[59, 64, 65, 67] and body [3] via mechanical linkages, or even computation using microstructure [36–38]. While passive devices excel
in safety, they dispense some advantages from active devices, suchas dynamic adjustments, etc. As such, researchers have been exploring designs that supplement the passive exoskeletons withadditional mechanisms, such as active brakes [16, 25, 31, 54], adjustable dampers [95], or variable-length linkages [110]. We takeinspiration from these semi-passive mechanisms to engineer our
exoskeleton that allows bidirectional and adaptive transmission ofindividual finger motions.

3.4

Adaptive and interpersonal haptic learning

There is substantial evidence that supports the role of adaptive
and interpersonal haptic learning, coming from HCI and neuroscience. Adaptive training is a training paradigm in which the task
or the feedback is varied as a function of how well the learner performs [48], leading to better performance as well as more motivation[2, 23, 32, 42, 80, 96, 111]. One way to provide adaptive feedback to alearner is to establish an interpersonal haptic communication—theteacher responds to the learner’s action directly. Researchers haveshown that interpersonal motor learning is effective as it can provide more responsive feedback to a learner’s action [7, 21, 41, 58, 72–
74, 102]. For instance, connecting two users’ wrists via a physicallinkage, allows them to achieve higher performance in a target
tracking task when compared with doing the task alone [20, 83–85].
Therefore, these suggest that feeling the physical movements of
the other user (e.g., teacher) play a positive role in skill transmission. This is precisely the inspiration for our work, in which we
explore how even a passive exoskeleton can achieve adaptive andinterpersonal transmission of new finger skills.

4

IMPLEMENTATION

Figure 3: DigituSync consists of finger sockets, variablelength linkages that transmit motion, palm bases, and a wrist
linkage.

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

Figure 4: (a) Linkage structure of the transmission mechanism. (b) Overview of the finger socket and the pin-slot
behavior.

Jun Nishida et al.

Figure 6: (a) The linkage has two pairs of variable-length
mechanisms to dynamically change the transmission. (b) It
allows an input force 𝐹 1 to be scaled up/down as 𝐹 3 . (c) It also
allows an input angle 𝜃𝑖 to be scaled up/down as 𝜃𝑜accommodates various finger poses, it restricts the transmissionof fully-clenched grasping gestures (the maximum range of angle of the first and second joints are 40 degrees and 47 degrees,
respectively).

4.2
Figure 5: The pin slots successfully absorb the distance displacement of the inner joints and transmit finger motion.
As depicted in Figure 3, DigituSync consists of: finger sockets, afinger motion transmission mechanism with variable-length linkages, palm bases, and a wrist linkage. To accelerate replication, we
provide all 3D files and bill of materials of our implementation1 .

4.1

Finger motion transmission mechanism

To transmit the movement of individual fingers between two users,
we designed a transmission mechanism, depicted in Figure 4 (a). The
core of the mechanism is comprised of multiple four-bar linkages,
allowing bi-directional interaction without motors and with no delay.
The motion of our mechanism is illustrated in Figure 4 (b). Whenthe teacher bends the first joint of their index finger (distal interphalangeal joint), their fingertip pushes down the bridge link 𝐴3 ,which in turn pushes down the connection link 𝐶 3 and the bridge
link 𝐴3 of the learner’s side. Thus, the teacher’s finger motion is
replicated on the learner’s finger, and vice versa, moving the users
together, preserving all haptic cues. The bridge links 𝑍𝑌 and 𝑌𝑋of the learner’s side are angled preventing interreference with thelearner’s finger joints when bent.
Since the distance between each socket 𝑆𝑋 , 𝑆𝑌 , 𝑆𝑍 and between
socket 𝑆𝑋 and Joint 𝐽0 will change when fingers are bent or extended, a pin slot was attached to the sockets 𝑆𝑖 to permit translating movement from each joint to the bridge links 𝑍𝑌 , 𝑌𝑋 , and𝑋 𝐽 . These pin slots absorb the distance displacement of the inner joints while transmitting finger motion continuously (Figure5). To calculate the length of this pin slot, we first measured theangular displacement of each finger joint using a motion capturesystem (OptiTrack, V100 R2) for a grasping motion. Then, using asimulation in Autodesk Fusion 360, we obtained the minimum pin
length required to achieve this motion range. While this mechanism
1 http://lab.plopes.org/#digitusync-files

Variable transmission mechanism

Furthermore, we added a variable-length linkage, as shown in Figure 6 (a), that allows for changing the lengths of the four-bar linkage(not for generating haptic feedback to a user) to optimize the learning experience.
Force Transmission. Figure 6 (b) shows the model of the moment of force. 𝐷 1 and 𝑑 1 represent the linkage length of the connection link 𝑍𝑌 . 𝐷 2 and 𝑑 2 represent lengths of length-adjustablelinkage. 𝐹 1 and 𝐹 3 represents an input and output force respectively. These can be described using 𝐹 2 , 𝑑 1,2 and 𝐷 1,2 as follows:
𝐹 𝐷 = 𝐹 2 (𝐷 1 + 𝐷 2 ) [𝑁 · 𝑚]
{ 1 1
. Therefore, the output force 𝐹 3 can
𝐹 3𝑑 1 = 𝐹 2 (𝑑 1 + 𝑑 2 ) [𝑁 · 𝑚]
be described as: 𝐹 3 = 𝑡𝐹 1 [𝑁 · 𝑚] , in which (𝑡 = 𝐷 1𝐷+ 1𝐷 2 · 𝑑1𝑑+𝑑2 ).
1
We defined these linkage distances as 𝐷 1 = 𝑑 1 = 23𝑚𝑚. Thus,
2
resulting in: t = 23+23𝐷 2 · 23+𝑑
23 . Since we allow the lengths 𝐷 2 and𝑑 2 to be changed from 5 to 35mm, we obtain: 0.44 ≤ 𝑡 ≤ 2.29. Thisvalue 𝑡 indicates that the teacher’s finger force is reduced by halfwhen the variable-length linkage is at its minimum (when 𝑑 2 < 𝐷 2 )and is doubled when the variable-length linkage is at the maximum(when 𝑑 2 > 𝐷 2 ). As we will see later, we confirmed the relationshipbetween this value 𝑡 and the force transmission ratio 𝐹 3 / 𝐹 1 .
Angle Transmission. Figure 6 (c) shows the model of thejoint angles 𝜃 i and 𝜃 o , which represent the input angle of theteacher’s second finger joint (proximal interphalangeal joint) andthe output angle of the learner’s, respectively. An additionalline 𝐿, angle 𝜃 1 , and 𝜃 2 can be represented as follows: 𝐿 =𝑖𝑠𝑞𝑟𝑡 (𝐴2 + 𝐵 2 − 2𝐴𝐵𝑐𝑜𝑠𝜃𝑖 ) in which 𝜃 1 = 𝑐𝑜𝑠 −1 ( 𝐴−𝐵𝑐𝑜𝑠𝜃
) and
𝐿
2

2

2

2

−2𝐴𝐵𝑐𝑜𝑠𝜃𝑖𝜃 2 = 𝑐𝑜𝑠 −1 ( 𝐴 +𝐵 −𝐶 +𝐷). Therefore, the output angle 𝜃𝑜
2𝐷𝐿
can be calculated as: 𝜃𝑜 = 𝜋 −𝜃 1 −𝜃 2 . In our implementation, we defined linkage lengths as 𝐴 = 𝐶 = 109𝑚𝑚, 𝐷 = 𝑑 1 + 𝑑 2 , 𝐵 = 𝐷 1 + 𝐷 2 .
We used this model to calculate the angular transmission ratioΔ𝜃𝑜 /Δ𝜃𝑖 , which we evaluated in the later section.
Variable-length linkage. Our default method is to provide
users of DigituSync with a manual configuration for the transmission ratio. Users simply adjust the intended level of transmission
via a slide-joint, as depicted in Figure 7. This mechanism, consistingDigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures

Figure 7: Users can manually adjust the transmission ratio.
Positions can be fixed by the rubber O-rings in the slide.

Figure 8: Magnets embedded in the linkages enable quickattachment and detachment of (a) two hands and (b) wrists.
(c) They also fix floating linkages when detached.

of using metal shafts (2mm diameter, 45mm length) and O-rings(4mm diameter), was designed with sufficient friction that it does
not un-adjust itself but will still be easy to move at the push of afinger.
Detachable linkage mechanism. To transition between the
coupled and decoupled state of the dual-exoskeleton, we designed
a magnetic attachment mechanism. Small magnet slates are embedded as shown in Figure 8 (a). Those can be detached easily yet
can keep them connected while transferring finger poses. Larger
magnets, depicted in Figure 8 (b), were used for the wrist connection linkage since this is subject to more force. We also added small
magnets near the linkages of the first joint to avoid having moving joints when detached, as shown in Figure 8 (c). Attaching anddetaching action require 20s and ∼4s to complete, respectively.
Fabrication. DigituSync is manufactured from methacrylate
resin on a Formlabs Form3 printer. The total weight of the exoskeleton is 240g (learner’s side: 118g, teacher’s side: 122g). The rest of
the linkages are manufactured using a laser cutter with a 1.5mm
thick acrylic plate. Each of those has Velcro tapes to fit each finger’s
depth.

5

TECHNICAL EVALUATION: LINKAGE
TRANSMISSION

To validate the force and angular transmission ratio of DigituSync,
we measured the changes in output force and the angle at arbitrary combinations of 𝐷 2 and 𝑑 2 , and then compared them with theUIST ’22, October 29–November 02, 2022, Bend, OR, USA

Figure 9: Comparison between measured and simulated results of (a) the force and (b) angle transmission.

previously derived theoretical model. The linkage length 𝐷 2 was determined according to 𝑑 2 by using the minimum and the maximum
length of the variable-length link 𝐿𝑚𝑖𝑛 and 𝐿𝑀𝐴𝑋 , which were 5 mm
and 35mm respectively, as follows: 𝐷 2 = 𝐿𝑀𝐴𝑋 − 𝑑 2 + 𝐿𝑚𝑖𝑛 [𝑚𝑚].
Force transmission evaluation. A dummy weight (AAA battery, 𝑤𝑖 = 11g) was used instead of an actual finger to provide
a more repeatable load. A load-cell sensor (TAL220) was placed
underneath the learner’s finger socket 𝑆𝑍 . The output weight (𝑤𝑜 )was measured three times for thirteen different combinations of
𝐷 2 and 𝑑 2 . Figure 9 (a) shows the measured ratio of the input andoutput force (in orange), plotted alongside the simulated function(in blue). With a known constant weight when 𝑡 = 1.0 (ratio between 𝐷 2 and 𝑑 2 ), by increasing the linkage’s ratio 𝑡, the force
transmission ratio also increases linearly, thus it allows variable
force transmission from half up to double the input force (fitting
function: 𝑦 = 1.15𝑥 − 0.13, 𝑅 2 = 0.98 ).
Angular transmission evaluation. We also evaluated the relationship between the input and output angles of the finger motion
transmission. We used the motion capture system (mean calibration error=0.27mm) and placed reflective markers on the teacher’s
and the learner’s index fingers to measure the second joint (middle
interphalangeal joint) angles. The angles were measured five times
with five different combinations of 𝐷 2 and 𝑑 2 . Then, we calculated
the angular ratio (Δ𝜃𝑜 /Δ𝜃𝑖 ). Figure 9 (b) shows the result of the 𝑡value, measured ratio (fitting function: 𝑦 = 1.17𝑥 −1.12 ), and simulated ratio (fitting function: 𝑦 = 1.03𝑥 −1.19 ). We observed that themeasured angular ratio follows the simulated ratio, suggesting that
our linkage allows variable angular transmission from half up to
double the input angle.

6

USER STUDIES

Our studies were approved by our Institutional Review Board(IRB19-1431).

6.1

Initial study: piano learning

Goal. We first confirmed that the DigituSync exoskeleton does notdegrade the learner’s performance when compared to a traditional
learning setting. There are many piano learning settings one cancompare against, such as using a screen-based [114], projection
mapping [76, 106], vibration [34, 35, 60], force-feedback [22, 24]
including EMS [15], and so forth; however, as a preliminary evaluation, we first compared with this visual demonstration setting as it

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

is the more traditional and widely used method in piano classes toconfirm the DigituSync can be applied to this scenario.
Task & apparatus. Participants were asked to replay a melody
with correct timing and volume after it was performed by an experimenter. This task design was modeled after prior studies on
drumming skill acquisition [15,22]. We used a force-sensitive MIDI
keyboard (MiDiplus, Classic 25). Apple Logic Pro X and Apple
Script were used for recording and playing with consistent timings
between the demonstration and the replay.
Metrics. We calculated the percentage of correct notes and thepercentage of correct intensities using MATLAB and MIDI Toolbox
1.1 [93]. A note was considered correct if it was played within 150ms
of the experimenter’s timing, which is also typically used in thestudies of drumming [22] or piano [76]. We calculated the intensity
score by averaging the intensity error of all notes from those of theexperimenter.
Melodies. We created two different melodies (see scores in Appendix) with comparable level of difficulty by flipping the orderof the note sequence, which generates a melody that untrained
participants cannot identify as a flipped-melody.
Procedure. The study consisted of 10 sets of demonstration andreplay phases. There were two demonstration conditions: visual(Figure 10a) and DigituSync (Figure 10b). In a demonstration phase,participants were instructed to relax their hands and acquire themelody (using one of the two conditions). Immediately after thedemonstration, the participant was asked to replay the melody
without the exoskeleton attached to that of the teacher. Our two
melodies and two interface conditions were presented using a balanced Latin-square across participants.
Participants. We recruited twelve right-handed participantsfrom our organization (six identified as female; six as male; mean
age=22.9 years old, SD=2.7), who were compensated with 10 USD.
No participant had tried an exoskeleton and had no dexterity impairments. Eleven out of twelve participants had some experiencein learning any musical.
Results. Figure 10 (c) shows the error in notes with DigituSync(mean=5.04, SD=1.59) and the visual demonstration (mean=6.63,
SD=1.25). Figure 10 (c) also shows the error in intensity with
DigituSync (mean=18.84, SD=1.51) and the visual demonstration(mean=23.84, SD=6.86). A paired t-test revealed a significant difference in both notes and intensity error between the two demonstration conditions (notes; t(11) = -2.68, p = 0.02 < 0.05, intensity; t(11)= -2.47, p = 0.03 < 0.05).
Participants’ feedback. A participant stated “The [DigituSync]
feedback actually helped a lot more than after experiencing thecondition with just the teacher [visual] (. . .) [in the visual condition]
I had to utilize much more memorization to get the volume rightcompared to when I had the [DigituSync] feedback” (P12). No
participant reported negative feedback regarding our device’s form
factor or weight thanks to the detachable mechanism.
Study conclusion. These results suggest that using the DigituSync exoskeleton in a piano learning setting does not degrade
their learning performance. Based on these results, we conducted
an exploratory session in a piano class, which we describe later.

Jun Nishida et al.

Figure 10: Conditions: (a) visual and (b) DigituSync. (c) We
found that using DigituSync does not degrade their learning.

6.2

User study: learning with variable-length
linkages

In this study, we investigate whether DigituSync’s variable transmission linkages can be used to optimize the learning experience.
As such, we compare the users’ feeling of control over their own
movements and how a teacher was responsive to users’ actions thatare critical for preserving their learning motivation, in acquiring arhythm taught by the experimenter in two conditions: (1) variabletransmission, in which our variable linkages provide a transmission
directly correlated to the participants’ accuracy while playing therhythm (i.e., the better their score, the lower the transmission force
will be—this is just one of the many possible strategies one could
implement, we chose it as it is the simplest in order to explore
the role of the variable-length linkages); and (2) static transmission(baseline) where the same level of force feedback is provided in alltrials, which emulates conventional hand-over-hand guidance orusing a knitted joint-glove. We collected subjective feedback on
their learning experience as well as measured music performance.
Task. Participants learned a rhythm (in one of two conditions)from an experimenter then replayed it with correct timing andvolume. To allow participants to compare the two conditions easily,
we simplified the rhythms to use just the index finger. This study
focuses on rhythmic patterns with a shorter range for evaluating
timing (100ms). The same apparatus from preliminary study was
used.
Procedure. The study consisted of a block of 15 trials. Eachtrial was comprised of two phases: (1) demonstration, in which theexperimenter leads and plays the rhythm, and (2) replay. Note that,in this study, we asked participants to try to play together with theexperimenter during the demonstration phases (except the very
first one, in which they were asked to relax). At the end of eachcondition, they were asked to answer a 7-point Likert questionnaire and comment at the end of the study regarding their learning
experience. Our two interface conditions and two rhythms were
presented using a balanced Latin-square.
Variable transmission. While in the baseline condition we kept
the linkage transmission at a fixed ratio, as shown in Figure 11 (a),in the variable transmission condition, the experimenter manually
adjusted the linkage ratio 𝑡 between 0.44 to 2.29 in five increments
based on the accuracy difference of the participants’ current andlast replay trial. The idea behind this simple strategy is that theparticipants receive less haptic feedback from the teacher as theiraccuracy improved, or vice-versa. In other words, in the case where
the score improves by 1-50%, we decreased by one increment of 𝑡value (=0.46). In the case where the score improves by more than

DigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures

Figure 11: Experimental setup used to explore learning experience with: (a) static linkage or with (b) variable linkage.

Figure 12: (a) Subjective reporting on the intervention (b)feeling of control. (average(△), median( | ), IQR, outlier(⃝))
50%, we decreased by two increments of 𝑡 value (=0.93), as shown in
Figure 11 (b). The same logic was applied when the score decreased.
Rhythms. We created two rhythms with comparable difficultyby shuffling the order of note segments; see scores in Appendix).
Questionnaire. Participants were asked to answer the following
after each condition: (Q1 ) Did you feel that the experimenter’sintervention was static or changing as a response to your mistakes?
(static or variable); and (Q2 ) Please rate how much you felt in controlof your movements in this condition (1: not in control, 7: in control).
Finally, at the end of the study, we asked participants “in which
condition you felt that the teacher was able to react better to yourmistakes”. It is noted that we always referred to conditions as “inthis condition” rather than using “static” or “variable” words to
avoid bias.
Participants. We recruited eight right-handed participants fromour local organization (four self-identified as female; four as male;mean age=22.9 years old, SD=2.7), who were compensated with 10
USD. Five out of eight participants had an experience of learning
any musical instruments (mean duration=8.5 years, SD=3.2). The
experimenter has 10 years of experience with piano and practiced
rhythms beforehand.
Results. While we found there is no difference in the participants’ performance between both conditions (timing; t(7) = -0.83,
p = 0.43, intensity; t(7) = 0.517, p = 0.62), we did find differences intheir learning experiences across both conditions. First, as depicted
in Figure 12 (a), the participants more often reported feeling the experimenter’s intervention as responsive in the variable transmission
condition than in the static transmission. Secondly, participants
reported a higher sense of finger control over their movements(mean=5.1, SD=1.4) in the variable transmission than that of thestatic transmission (mean=4.3, SD=1.7), which was confirmed using a paired t-test (t(7) = 2.97, p = 0.02 < 0.05). At the end of our

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

study, Six out of eight participants chose the variable condition for
a condition in which “the teacher was able to react better to yourmistakes”.
Participants’ feedback. When asked to justify their preference
for the variable transmission, comments included “weaker linkage[trials] gives me the freedom to practice more on my own” (P2),“While I just start learning, the teacher can lead more; while I nearlylearn the rhythms, the teacher can provide less force.” (P4), and,“I had a different experience. I also felt much more comfortable(. . .) since the sound (. . .) was being impacted by me and provided
variability and leeway when I repeat the sound” (P8). Some participants also stated limitations of the variable condition, including“[it] changed my perception of the general dynamic range” (P7),and “sometimes it provided better feedback, but other times it felt
like the inconsistency in intervention pressure caused confusion”(P6). When asked to justify their preference for the static condition,
two of the participants commented “I liked (. . .) since the intervention felt more consistent” (P6) and “it consistently stimulates mytouching sense” (P5). When interviewed regarding their learning
experience with the variable transmission, comments included “thefirst linkage length [high force ratio] have a stronger power to lead(. . .) the second one [low force ratio] is relatively weaker” (P5). With
the static transmission, comments included “I let the teacher play(. . .) I found (. . .) it would interfere with the rhythm and pressure(. . .) also my perception” (P3), “I tried to follow the teacher (. . .) andI felt like the teacher and I clashed” (P5), and, interestingly, “I dobelieve I got better (. . .), as time went on, I got closer to the melody
yet I kept doubting myself” (P8).
Study conclusion. These results suggest that our variable transmission impacted positively, while not compromising performance.
It is also worth noting that learning a new skill has many challenges
beyond improving one’s performance, such as keeping oneself motivated [96, 97], which, again, the variable transmission seemed tosupport better. Yet some participants preferred a static transmission, the real benefit to note regarding the variable transmissionis that it can also be static and they can choose. There are moreadvanced learning strategies that can be coupled with our variable
linkage mechanism, such as [20, 23, 77, 104] as well as additional
sensors, such as electromyography, eye tracking devices [39, 40],or brain-computer interfaces [111, 112], enabling more accurate
estimation on the user’s state.
Discussion on usability. We also received feedback on theexoskeleton design such as absorbing personal differences in hand
dimensions and applying this to other parts of the body. These
could be achieved by incorporating computational techniques formechanical design [57, 64, 107, 113].

6.3

Exploratory session: feedback from musicteacherIn this session, we explored teaching experience with a professional
pianist in an actual piano class. We asked a tenured music professor (45 years old, +20 years of teaching) at music college to use
DigituSync anytime during their one-on-one piano class. In thisclass, the student was an advanced student (20 years old and morethan 10 years of experience) and had no prior experience in using
the exoskeleton. We provided no instructions whatsoever to the

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

Figure 13: (a) A deaf-blind user (middle) is testing the usability of DigituSync while receiving instruction from a protactile sign interpreter and (b) playing a rhythm together.
teacher as we wanted to understand if DigituSync affords “walkupuse”.
Teacher’s feedback: The teacher stated "glove was interestingfor my piano classes (. . .) I chose to use it with a more advanced
student, who plays well but does not articulate well yet [articulate
refers to the length of its sound and the shape of its attack anddecay] (. . .) while we were playing a contemporary piece, I put on
the glove and demonstrated how to articulate legato [playing notes
smoothly and connected] and portato [playing notes smoothly
pulsing] to sound distinct [these are two different articulations], as
my student tends to slur these in the same way, [this] was visibly
exciting.” The teacher also elaborated on limitations, “[it] did notwork for any interval, as it was difficult for me to extend their hands
via the glove to play a voice doubling [informal language to playing
the same note an octave above], perhaps a future glove could permit
this and have some more flexibility on the curl position too”.
Participants’ feedback on walkup use: The teacher stated“surprisingly, I found it easy to use. When I first saw it, I thought
I would never be able to get inside the glove alone, but I was able
to do it and the student too (. . .) after we used this for articulation,
the student played the remainder of the piece with the glove andI was surprised by it, [student] said it feels very different, but hequickly was getting used to it”.
Reversing roles: Surprising to us, teacher and student decided
to reverse roles (again, we gave no prompts or even instructions).
Teacher explained “this time I also let my student control my hand,which was a fun exercise, but also when we broke it – perhaps
having too much fun.".

6.4

Exploratory session: feedback fromdeaf-blind users

We also explored using DigituSync with deaf-blind users in a workshop, depicted in Figure 13. While these sessions did not carry the
rigor of our controlled experiments, they allowed us to gather insights into how deaf-blind users reacted to DigituSync in a looser
setting. We gave a brief introduction to the protactile sign language interpreters. After the interpreters communicated with thedeaf-blind participants, the deaf-blind participants either touched,
grabbed, or moved the exoskeleton without attaching it to theirhands. In this session, three deaf-blind participants (P1, P2, P3) tried
the DigituSync for 15 minutes each.
Participants’ feedback. P1, who was born deaf-blind, attached
the exoskeleton, then received rhythms from the experimenter on
the table, as shown in Figure 13 (a). P1 stated that “[when] movethose different knuckles [as they felt all the fingers move] (..) they

Jun Nishida et al.

could be stronger so that I can feel it much better”. After I asked
whether this can be used for, P1 stated that “I think this kind of
device would be good for (. . .) supporting a person to change a
specific habit [of fingers, referring to erroneous finger poses]”. We
also received opposite reactions regarding the feedback strength.
P2 tried the exoskeleton with a MIDI keyboard as shown in Figure
13 (b). P2 stated they would prefer to receive feedback on a targetedpart of a finger (alluding to the movements being too strong, conversely to P1). Lastly, a P3 used the exoskeleton in midair, similar
to signing, and stated “I think it has a great potential (. . .) suchas teaching cooking for children (. . .) because it would support
memorizing the position of cooking utensils”. P2 and P3 were born
Deaf and lost vision over time.

7

FURTHER OPPORTUNITIES FORDIGITUSYNC

We believe there is a range of interesting motor learning scenarios
where users can leverage the benefits of DigituSync: adaptive, realtime, and congruent haptic communication with walk-up-use andlow-cost form factor.

Figure 14: Application scenarios: (a) Finger braille training;(b) Hand-over-Hand Guidance; visually impaired persons
receive manual manipulation during therapy or performing
massage.
One such example is in teaching languages based on complex
and fast finger gesture combinations, such as finger braille, an important tactual communication language used by deaf-blind people,
as depicted in Figure 14 (a). Highly skilled interlocutors and interpreters, not only just tap with their fingers to spell letters, but canalso express emotion and attitude with the tapping pressure andrate [10, 105]. Another interactive space is in physical therapy forpatients who lost some degree of motor function. Physicians makeoften use of direct “hand-over-hand guidance” [82], which is extremely similar to how DigituSync operates. Given the adjustabilityof feedback, it might improve patients’ learning experience including their motivation as we found in our user study. These could
also include, for instance, teaching how to operate an abacus andlearning acupressure techniques for blind users [9, 75], as depicted
in Figure 14 (b).

8

CONCLUSION

We engineered DigituSync, a passive-exoskeleton that physicallylinks two hands together, enabling two users to adaptively transmit
finger movements in real-time. We conducted a user study and two
exploratory sessions to evaluate the benefits and challenges of theDigituSync in potential scenarios: In our user study on adaptive

DigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures

learning experience, we examined the role of the variable-length
linkages on the learning experience; we found that while it did
not compromise performance, participants felt more control overtheir own movements and felt the teacher’s intervention was more
responsive. Then in our exploratory sessions, we gathered insightsfrom participants (a piano teacher and deaf-blind people), which
provided inspiration for future development. Finally, we discussed
further opportunities to use DigituSync in haptic communication,such as assisting the learning of finger braille or how to use tools
including an abacus that would benefit from the passive form factor
of DigituSync.

9

APPENDIX: SCORES OF THE MELODIES &RHYTHMS

Figure 15: Our two 7-second melodies with comparable difficulty (blue notes in pianissimo, red notes in fortissimo).

Figure 16: Our two 7-second rhythmic patterns with comparable difficulty (with a crescendo section in the middle).

ACKNOWLEDGMENTS
This work was supported by NSF grant 2024923. Any opinions,findings, and conclusions, or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect theviews of any funding agencies. We are grateful for the feedback
from our reviewers.

REFERENCES[1] Dorit H. Aaron and Caroline W. Stegink Jansen. 2003. Development of the
Functional Dexterity Test (FDT): Construction, validity, reliability, and normative data. Journal of Hand Therapy 16, 1: 12–21. https://doi.org/10.1016/S08941130(03)80019-4[2] Z.A. Abbas and J.S. North. 2018. Good-vs. poor-trial feedback in motor learning:
The role of self-efficacy and intrinsic motivation across levels of task difficulty.
Learning and Instruction 55: 105–112. https://doi.org/10.1016/j.learninstruc.2017.
09.009[3] Tomohiro Aka, Shiroku Tatsuru, and Koki Tamashiro. 2011. SKELETONICS
SUIT. Retrieved September 19, 2019 from https://ars.electronica.art/nextidea/
skeletonics-suit/[4] Judith Amores, Xavier Benavides, and Pattie Maes. 2015. ShowMe: A Remote
Collaboration System that Supports Immersive Gestural Communication. In
Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human
Factors in Computing Systems - CHI EA ’15, 1343–1348. https://doi.org/10.1145/
2702613.2732927

UIST ’22, October 29–November 02, 2022, Bend, OR, USA[5] J. Arata, M. Hattori, S. Ichikawa, and M. Sakaguchi. 2014. Robotically Enhanced
Rubber Hand Illusion. IEEE Transactions on Haptics 7, 4: 526–532. https://doi.
org/10.1109/TOH.2014.2304722[6] J. Arata, K. Ohmoto, R. Gassert, O. Lambercy, H. Fujimoto, and I. Wada. 2013.
A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism. In 2013 IEEE International Conference on Robotics andAutomation, 3902–3907. https://doi.org/10.1109/ICRA.2013.6631126[7] Edwin Johnatan Avila Mireles, Jacopo Zenzeri, Valentina Squeri, Pietro Morasso,
and Dalia De Santis. 2017. Skill Learning and Skill Transfer Mediated by Cooperative Haptic Interaction. IEEE Transactions on Neural Systems and Rehabilitation
Engineering 25, 7: 832–843. https://doi.org/10.1109/TNSRE.2017.2700839[8] Alison Batchelder, T. F. McLaughlin, K. P. Weber, K. Mark Derby, and Tootie Gow.
2009. The Effects of Hand-Over-Hand and a Dot-to-Dot Tracing Procedure on
Teaching an Autistic Student to Write his Name. Journal of Developmental andPhysical Disabilities 21, 2: 131–138. https://doi.org/10.1007/s10882-009-9131-2[9] Carola Beresford-Cooke, Lynn Williams, and Nicholas Pole. 2003. Shiatsu theory
and practice: a comprehensive text for the student and professional. Churchill
Livingstone, Edinburgh.
[10] Mayumi Bono, Rui Sakaida, Ryosaku Makkino, Tomohiro Okada, Kouhei
Kikuchi, Mio Cibulka, Louisa Willoughby, Shimako Iwasaki, and Satoshi
Fukushima. 2018. Tactile Japanese sign language and finger Braille: An example of data collection for minority languages in Japan. In The 11th editionof the Language Resources and Evaluation Conference (LREC). Retrieved fromhttp://lrec-conf.org/workshops/lrec2018/W1/pdf/18027_W1.pdf[11] Dominic A. Brandy. 1995. The O’Connor Tweezer Dexterity Test as a Screening
Tool for Hiring Surgical Hair Restoration Assistants. The American Journal of
Cosmetic Surgery 12, 4: 313–316. https://doi.org/10.1177/074880689501200406[12] Scott Brave and Andrew Dahley. 1997. inTouch: A Medium for Haptic Interpersonal Communication. In CHI ’97 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’97), 363–364. https://doi.org/10.1145/1120212.1120435[13] A. Chiri, N. Vitiello, F. Giovacchini, S. Roccella, F. Vecchi, and M. C. Carrozza.
2012. Mechatronic Design and Characterization of the Index Finger Module of a
Hand Exoskeleton for Post-Stroke Rehabilitation. IEEE/ASME Transactions on
Mechatronics 17, 5: 884–894. https://doi.org/10.1109/TMECH.2011.2144614[14] Tim Duente, Max Pfeiffer, and Michael Rohs. 2017. Zap++: a 20-channel electrical muscle stimulation system for fine-grained wearable force feedback. In
Proceedings of the 19th International Conference on Human-Computer Interaction
with Mobile Devices and Services, 1–13. https://doi.org/10.1145/3098279.3098546[15] Ayaka Ebisu, Satoshi Hashizume, Kenta Suzuki, Akira Ishii, Mose Sakashita,
and Yoichi Ochiai. 2017. Stimulated percussions: method to control humanfor learning music by using electrical muscle stimulation. In Proceedings of
the 8th Augmented Human International Conference on - AH ’17, 1–5. https://doi.org/10.1145/3041164.3041202[16] Cathy Fang, Yang Zhang, Matthew Dworman, and Chris Harrison. 2020. Wireality: Enabling Complex Tangible Geometries in Virtual Reality with Worn MultiString Haptics. In Proceedings of the 2020 CHI Conference on Human Factors in
Computing Systems (CHI ’20), 1–10. https://doi.org/10.1145/3313831.3376470[17] C. Farrer, G. Valentin, and J. M. Hupé. 2013. The time windows of the sense ofagency. Consciousness and Cognition 22, 4: 1431–1441. https://doi.org/10.1016/j.
concog.2013.09.010[18] Feng Feng and Tony Stockman. 2019. Augmented Visuotactile Feedback Support
Sensorimotor Synchronization Skill for Rehabilitation. In Extended Abstracts ofthe 2019 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3290607.3312812[19] Rebecca Fribourg, Nami Ogawa, Ludovic Hoyet, Ferran Argelaguet, Takuji
Narumi, Michitaka Hirose, and Anatole Lecuyer. 2020. Virtual Co-Embodiment:
Evaluation of the Sense of Agency while Sharing the Control of a Virtual
Body among Two Individuals. IEEE Transactions on Visualization and Computer
Graphics: 1–1. https://doi.org/10.1109/TVCG.2020.2999197[20] G. Ganesh, A. Takagi, R. Osu, T. Yoshioka, M. Kawato, and E. Burdet. 2014. Twois better than one: Physical interactions improve motor performance in humans.
Scientific Reports 4: 3824 EP-. Retrieved from https://doi.org/10.1038/srep03824[21] S. Gentry, E. Feron, and R. Murray-Smith. 2005. Human-human haptic collaboration in cyclical Fitts’ tasks. In 2005 IEEE/RSJ International Conference on Intelligent
Robots and Systems, 3402–3407. https://doi.org/10.1109/IROS.2005.1545064[22] Takashi Goto, Swagata Das, Katrin Wolf, Pedro Lopes, Yuichi Kurita, and Kai
Kunze. 2020. Accelerating Skill Acquisition of Two-Handed Drumming using
Pneumatic Artificial Muscles. In Proceedings of the Augmented Humans International Conference, 1–9. https://doi.org/10.1145/3384657.3384780[23] Rob Gray. 2017. Transfer of Training from Virtual to Real Baseball Batting.
Frontiers in Psychology 8: 2183. https://doi.org/10.3389/fpsyg.2017.02183[24] Graham Grindlay. 2008. Haptic Guidance Benefits Musical Motor Learning. In
2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator
Systems, 397–404. https://doi.org/10.1109/HAPTICS.2008.4479984[25] Xiaochi Gu, Yifei Zhang, Weize Sun, Yuanzhe Bian, Dao Zhou, and Per Ola
Kristensson. 2016. Dexmo: An Inexpensive and Lightweight Mechanical Exoskeleton for Motion Capture and Force Feedback in VR. In Proceedings ofthe 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16),
1991–1995. https://doi.org/10.1145/2858036.2858487

UIST ’22, October 29–November 02, 2022, Bend, OR, USA[26] Takayoshi Hagiwara, Gowrishankar Ganesh, Maki Sugimoto, Masahiko Inami,
and Michiteru Kitazaki. 2020. Individuals Prioritize the Reach Straightness
and Hand Jerk of a Shared Avatar over Their Own. iScience 23, 12: 101732.
https://doi.org/10.1016/j.isci.2020.101732[27] Shin Hanagata and Yasuaki Kakehi. 2018. Paralogue: A Remote Conversation
System Using a Hand Avatar which Postures are Controlled with Electrical
Muscle Stimulation. In Proceedings of the 9th Augmented Human International
Conference on - AH ’18, 1–3. https://doi.org/10.1145/3174910.3174951[28] B. Hannaford, L. Wood, D. A. McAffee, and H. Zak. 1991. Performance evaluationof a six-axis generalized force-reflecting teleoperator. IEEE Transactions on
Systems, Man, and Cybernetics 21, 3: 620–633. https://doi.org/10.1109/21.97455[29] Mariam Hassib, Max Pfeiffer, Stefan Schneegass, Michael Rohs, and FlorianAlt. 2017. Emotion Actuator: Embodied Emotional Feedback through Electroencephalography and Electrical Muscle Stimulation. In Proceedings of the2017 CHI Conference on Human Factors in Computing Systems, 6133–6146.
https://doi.org/10.1145/3025453.3025953[30] Pilwon Heo, Gwang Min Gu, Soo-jin Lee, Kyehan Rhee, and Jung Kim. 2012.
Current hand exoskeleton technologies for rehabilitation and assistive engineering. International Journal of Precision Engineering and Manufacturing 13, 5:
807–824. https://doi.org/10.1007/s12541-012-0107-2[31] Ronan Hinchet, Velko Vechev, Herbert Shea, and Otmar Hilliges. 2018. DextrES:
Wearable Haptic Feedback for Grasping in VR via a Thin Form-Factor Electrostatic Brake. In The 31st Annual ACM Symposium on User Interface Software andTechnology - UIST ’18, 901–912. https://doi.org/10.1145/3242587.3242657[32] M. Hirano, M. Sakurada, and S. Furuya. 2020. Overcoming the ceiling effects ofexperts’ motor expertise through active haptic training. Science Advances 6, 47:
eabd2558. https://doi.org/10.1126/sciadv.abd2558[33] Masato Hirano, Yudai Kimoto, and Shinichi Furuya. 2020. Specialized
Somatosensory–Motor Integration Functions in Musicians. Cerebral Cortex
30, 3: 1148–1158. https://doi.org/10.1093/cercor/bhz154[34] Simon Holland, Anders J. Bouwer, Mathew Dalgelish, and Topi M. Hurtig. 2010.
Feeling the beat where it counts: fostering multi-limb rhythm skills with thehaptic drum kit. In Proceedings of the fourth international conference on Tangible,
embedded, and embodied interaction - TEI ’10, 21. https://doi.org/10.1145/1709886.
1709892[35] Kevin Huang, Thad Starner, Ellen Do, Gil Weiberg, Daniel Kohlsdorf, Claas
Ahlrichs, and Ruediger Leibrandt. 2010. Mobile music touch: mobile tactile stimulation for passive learning. In Proceedings of the 28th international conferenceon Human factors in computing systems - CHI ’10, 791. https://doi.org/10.1145/
1753326.1753443[36] Alexandra Ion, Johannes Frohnhofen, Ludwig Wall, Robert Kovacs, Mirela Alistar, Jack Lindsay, Pedro Lopes, Hsiang-Ting Chen, and Patrick Baudisch. 2016.
Metamaterial Mechanisms. In Proceedings of the 29th Annual Symposium on
User Interface Software and Technology, 529–539. https://doi.org/10.1145/2984511.
2984540[37] Alexandra Ion, David Lindlbauer, Philipp Herholz, Marc Alexa, and Patrick
Baudisch. 2019. Understanding Metamaterial Mechanisms. In Proceedings ofthe 2019 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3290605.3300877[38] Alexandra Ion, Ludwig Wall, Robert Kovacs, and Patrick Baudisch. 2017. Digital
Mechanical Metamaterials. In Proceedings of the 2017 CHI Conference on Human
Factors in Computing Systems, 977–988. https://doi.org/10.1145/3025453.3025624[39] Shoya Ishimaru, Syed Saqib Bukhari, Carina Heisel, Nicolas Großmann, Pascal Klein, Jochen Kuhn, and Andreas Dengel. 2018. Augmented Learningon Anticipating Textbooks with Eye Tracking. In Positive Learning in theAge of Information, Olga Zlatkin-Troitschanskaia, Gabriel Wittum and Andreas Dengel (eds.). Springer Fachmedien Wiesbaden, Wiesbaden, 387–398.
https://doi.org/10.1007/978-3-658-19567-0_23[40] Shoya Ishimaru, Soumy Jacob, Apurba Roy, Syed Saqib Bukhari, Carina Heisel,
Nicolas GroBmann, Michael Thees, Jochen Kuhn, and Andreas Dengel. 2017.
Cognitive State Measurement on Learning Materials by Utilizing Eye Tracker
and Thermal Camera. In 2017 14th IAPR International Conference on Document
Analysis and Recognition (ICDAR), 32–36. https://doi.org/10.1109/ICDAR.2017.
378[41] Ekaterina Ivanova, Axel Krause, Marie Schalicke, Franziska Schellhardt, Natalie
Jankowski, Josy Achner, Henning Schmidt, Michael Joebges, and Jorg Kruger.
2017. Let’s do this together: Bi-Manu-Interact, a novel device for studying humanhaptic interactive behavior. In 2017 International Conference on Rehabilitation
Robotics (ICORR), 708–713. https://doi.org/10.1109/ICORR.2017.8009331[42] Shixin Jiang and Jun Rekimoto. 2020. Mediated-Timescale Learning: Manipulating Timescales in Virtual Reality to Improve Real-World Tennis Forehand
Volley. In 26th ACM Symposium on Virtual Reality Software and Technology, 1–2.
https://doi.org/10.1145/3385956.3422128[43] Lynette A. Jones and Susan J. Lederman. 2006. Human hand function. Oxford University Press, New York, NY, US. https://doi.org/10.1093/acprof:oso/
9780195173154.001.0001[44] Shunichi Kasahara, Mitsuhito Ando, Kiyoshi Suganuma, and Jun Rekimoto.
2016. Parallel Eyes: Exploring Human Capability and Behaviors with Paralleled

Jun Nishida et al.

First Person View Sharing. In Proceedings of the 2016 CHI Conference on Human
Factors in Computing Systems (CHI ’16), 1561–1572. https://doi.org/10.1145/
2858036.2858495[45] Shunichi Kasahara and Jun Rekimoto. 2014. JackIn: Integrating First-person
View with Out-of-body Vision Generation for Human-human Augmentation.
In Proceedings of the 5th Augmented Human International Conference (AH ’14),
46:1-46:8. https://doi.org/10.1145/2582051.2582097[46] Shunichi Kasahara, Kazuma Takada, Jun Nishida, Kazuhisa Shibata, Shinsuke
Shimojo, and Pedro Lopes. 2021. Preserving Agency During Electrical Muscle
Stimulation Training Speeds up Reaction Time Directly After Removing EMS. In
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems,
1–9. https://doi.org/10.1145/3411764.3445147[47] Hiroki Kawasaki, Hiroyuki Iizuka, Shin Okamoto, Hideyuki Ando, and Taro
Maeda. 2010. Collaboration and skill transmission by first-person perspective
view sharing system. In 19th International Symposium in Robot and Human Interactive Communication, 125–131. https://doi.org/10.1109/ROMAN.2010.5598668[48] Charles R. Kelley. 1969. What is Adaptive Training? Human Factors: The Journalof the Human Factors and Ergonomics Society 11, 6: 547–556. https://doi.org/10.
1177/001872086901100602[49] Yudai Kimoto, Takanori Oku, and Shinichi Furuya. 2019. Neuromuscular andbiomechanical functions subserving finger dexterity in musicians. Scientific
Reports 9, 1: 12224. https://doi.org/10.1038/s41598-019-48718-9[50] David Kirk, Tom Rodden, and Danaë Stanton Fraser. 2007. Turn it this way:
grounding collaborative action with remote gestures. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems - CHI ’07, 1039–1048.
https://doi.org/10.1145/1240624.1240782[51] Michinari Kono, Yoshio Ishiguro, Takashi Miyaki, and Jun Rekimoto. 2018.
Design and Study of a Multi-Channel Electrical Muscle Stimulation Toolkit for
Human Augmentation. In Proceedings of the 9th Augmented Human International
Conference on - AH ’18, 1–8. https://doi.org/10.1145/3174910.3174913[52] Keitaro Kurosaki, Hiroki Kawasaki, Daisuke Kondo, Hiroyuki Iizuka, Hideyuki
Ando, and Taro Maeda. 2011. Skill transmission for hand positioning task
through view-sharing system. In Proceedings of the 2nd Augmented Human International Conference on - AH ’11, 1–4. https://doi.org/10.1145/1959826.1959846[53] Catherine E. Lang and Marc H. Schieber. 2003. Differential Impairment of Individuated Finger Movements in Humans After Damage to the Motor Cortex or the Corticospinal Tract. Journal of Neurophysiology 90, 2: 1160–1170.
https://doi.org/10.1152/jn.00130.2003[54] Nianlong Li, Han-Jong Kim, LuYao Shen, Feng Tian, Teng Han, Xing-Dong
Yang, and Tek-Jin Nam. 2020. HapLinkage: Prototyping Haptic Proxies for
Virtual Hand Tools Using Linkage Mechanism. In Proceedings of the 33rd Annual
ACM Symposium on User Interface Software and Technology, 1261–1274. https://doi.org/10.1145/3379337.3415812[55] Pedro Lopes, Patrik Jonell, and Patrick Baudisch. 2015. Affordance++: Allowing
Objects to Communicate Dynamic Use. In Proceedings of the 33rd Annual ACM
Conference on Human Factors in Computing Systems - CHI ’15, 2515–2524. https://doi.org/10.1145/2702123.2702128[56] Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata, and Masahiko Inami. 2019. Naviarm: Augmenting the Learningof Motor Skills using a Backpack-type Robotic Arm System. In Proceedings ofthe 10th Augmented Human International Conference 2019 on - AH2019, 1–8.
https://doi.org/10.1145/3311823.3311849[57] Vittorio Megaro, Jonas Zehnder, Moritz Bächer, Stelian Coros, Markus Gross,and Bernhard Thomaszewski. 2017. A computational design tool for compliant
mechanisms. ACM Transactions on Graphics 36, 4: 1–12. https://doi.org/10.1145/
3072959.3073636[58] A. Melendez-Calderon, L. Bagutti, B. Pedrono, and E. Burdet. 2011. Hi5: A
versatile dual-wrist device to study human-human interaction and bimanualcontrol. In 2011 IEEE/RSJ International Conference on Intelligent Robots andSystems, 2578–2583. https://doi.org/10.1109/IROS.2011.6094422[59] Luke E. Miller, Matthew R. Longo, and Ayse P. Saygin. 2014. Tool morphology constrains the effects of tool use on body representations. Journal of Experimental Psychology: Human Perception and Performance 40, 6: 2143–2153.
https://doi.org/10.1037/a0037777[60] Sosuke Miura and Masanori Sugimoto. 2006. Supporting children’s rhythm
learning using vibration devices. In CHI ’06 extended abstracts on Human factors
in computing systems - CHI EA ’06, 1127. https://doi.org/10.1145/1125451.1125664[61] Ken Nakagaki, Chikara Inamura, Pasquale Totaro, Thariq Shihipar, Chantine
Akikyama, Yin Shuang, and Hiroshi Ishii. 2015. Linked-Stick: Conveying a
Physical Experience using a Shape-Shifting Stick. In Proceedings of the 33rd
Annual ACM Conference Extended Abstracts on Human Factors in Computing
Systems - CHI EA ’15, 1609–1614. https://doi.org/10.1145/2702613.2732712[62] S. Nakagawara, H. Kajimoto, N. Kawakami, S. Tachi, and I. Kawabuchi. 2005.
An Encounter-Type Multi-Fingered Master Hand Using Circuitous Joints. In
Proceedings of the 2005 IEEE International Conference on Robotics and Automation,
2667–2672. https://doi.org/10.1109/ROBOT.2005.1570516[63] Marla Narazani. 2018. Designing Foot-Based Interaction for Wearable Skill
Transfer Systems. In Proceedings of the 2018 ACM International Joint ConferenceDigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gesturesand 2018 International Symposium on Pervasive and Ubiquitous Computing andWearable Computers - UbiComp ’18, 1787–1788. https://doi.org/10.1145/3267305.
3277818[64] Jun Nishida, Soichiro Matsuda, Hiroshi Matsui, Shan-Yuan Teng, Ziwei Liu,
Kenji Suzuki, and Pedro Lopes. 2020. HandMorph: a Passive Exoskeleton that
Miniaturizes Grasp. In Proceedings of the 33rd Annual ACM Symposium on User
Interface Software and Technology, 565–578. https://doi.org/10.1145/3379337.
3415875[65] Jun Nishida, Kei Nakai, Akira Matsushita, and Kenji Suzuki. 2015. Haptic
Augmentation of Surgical Operation Using a Passive Hand Exoskeleton. In
Haptic Interaction: Perception, Devices and Applications, Hiroyuki Kajimoto,
Hideyuki Ando and Ki-Uk Kyung (eds.). Springer Japan, Tokyo, 237–243.
https://doi.org/10.1007/978-4-431-55690-9_45[66] Jun Nishida and Kenji Suzuki. 2017. bioSync: A Paired Wearable Device for
Blending Kinesthetic Experience. In Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems (CHI ’17), 3316–3327. https://doi.org/10.
1145/3025453.3025829[67] Jun Nishida, Hikaru Takatori, Kosuke Sato, and Kenji Suzuki. 2015. CHILDHOOD: Wearable Suit for Augmented Child Experience. In Proceedings ofthe 2015 Virtual Reality International Conference (VRIC ’15), 22:1-22:4. https://doi.org/10.1145/2806173.2806190[68] Jun Nishida, Keisuke Yagi, Modar Hassan, and Kenji Suzuki. 2019. Wearable
Kinesthetic I/O Device for Sharing Wrist Joint Stiffness. In 2019 41st Annual
International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), 3306–3310. https://doi.org/10.1109/EMBC.2019.8857538[69] Takanori Oku and Shinichi Furuya. 2017. Skilful force control in expert pianists.
Experimental Brain Research 235, 5: 1603–1615. https://doi.org/10.1007/s00221017-4926-3[70] Max Pfeiffer, Tim Dünte, Stefan Schneegass, Florian Alt, and Michael Rohs.
2015. Cruise Control for Pedestrians: Controlling Walking Direction using
Electrical Muscle Stimulation. In Proceedings of the 33rd Annual ACM Conferenceon Human Factors in Computing Systems - CHI ’15, 2505–2514. https://doi.org/
10.1145/2702123.2702190[71] Ilia G. Polushin, Amir Takhmar, and Rajni V. Patel. 2015. Projection-Based ForceReflection Algorithms With Frequency Separation for Bilateral Teleoperation.
IEEE/ASME Transactions on Mechatronics 20, 1: 143–154. https://doi.org/10.1109/
TMECH.2014.2307334[72] K. Reed, M. Peshkin, J.E. Colgate, and J. Patton. 2004. Initial studies in humanrobot-human interaction: Fitts’ law for two people. In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004, 2333-2338
Vol.3. https://doi.org/10.1109/ROBOT.2004.1307410[73] Kyle Reed, Michael Peshkin, Mitra J. Hartmann, Marcia Grabowecky, James
Patton, and Peter M. Vishton. 2006. Haptically Linked Dyads: Are Two MotorControl Systems Better Than One? Psychological Science 17, 5: 365–366. https://doi.org/10.1111/j.1467-9280.2006.01712.x[74] Kyle Reed, Michael Peshkin, Mitra Hartmann, James Patton, Peter Vishton, andMarcia Grabowecky. 2006. Haptic cooperation between people, and between
people and machines. In 2006 IEEE/RSJ International Conference on Intelligent
Robots and Systems, 2109–2114. https://doi.org/10.1109/IROS.2006.282489[75] W. S. Rehm. 1998. “The man who made Peekskill famous”: Dr. C.R. Johnston–first blind chiropractor. Chiropractic History: The Archives and Journal of the
Association for the History of Chiropractic 18, 2: 81–92.
[76] Katja Rogers, Michael Weber, Amrei Röhlig, Matthias Weing, Jan Gugenheimer,
Bastian Könings, Melina Klepsch, Florian Schaub, Enrico Rukzio, and Tina
Seufert. 2014. P.I.A.N.O.: Faster Piano Learning with Interactive Projection. In
Proceedings of the Ninth ACM International Conference on Interactive Tabletops
and Surfaces - ITS ’14, 149–158. https://doi.org/10.1145/2669485.2669514[77] Ron J.C.M. Salden, Fred Paas, Nick J. Broers, and Jeroen J.G. van Merriënboer.
2004. Mental Effort and Performance as Determinants for the Dynamic Selectionof Learning Tasks in Air Traffic Control Training. Instructional Science 32, 1/2:
153–172. https://doi.org/10.1023/B:TRUC.0000021814.03996.ff[78] MHD Yamen Saraiji, Tomoya Sasaki, Reo Matsumura, Kouta Minamizawa, andMasahiko Inami. 2018. Fusion: Full Body Surrogacy for Collaborative Communication. In ACM SIGGRAPH 2018 Emerging Technologies (SIGGRAPH ’18), 7:1-7:2.
https://doi.org/10.1145/3214907.3214912[79] Sotaro Shimada, Kensuke Fukuda, and Kazuo Hiraki. 2009. Rubber Hand Illusion
under Delayed Visual Feedback. PLOS ONE 4, 7: 1–5. https://doi.org/10.1371/
journal.pone.0006185[80] Isabelle M. Shuggi, Hyuk Oh, Patricia A. Shewokis, and Rodolphe J. Gentili.
2017. Mental workload and motor performance dynamics during practice ofreaching movements under various levels of task difficulty. Neuroscience 360:
166–179. https://doi.org/10.1016/j.neuroscience.2017.07.048[81] C. Stein and E. J. Yerxa. 1990. A Test of Fine Finger Dexterity. American Journalof Occupational Therapy 44, 6: 499–504. https://doi.org/10.5014/ajot.44.6.499[82] Andrea Story. 1997. Hand-Over-Hand Guidance: What Lesson Do We Teach?
The National Federation of the Blind Magazine for Parents and Teachers of BlindChildren Vol. 23, No. 2. Retrieved September 2, 2021 from https://nfb.org/images/
nfb/publications/fr/fr14/fr04setc.htm

UIST ’22, October 29–November 02, 2022, Bend, OR, USA[83] Atsushi Takagi, Gowrishankar Ganesh, Toshinori Yoshioka, Mitsuo Kawato,
and Etienne Burdet. 2017. Physically interacting individuals estimate the partner’s goal to enhance their movements. Nature Human Behaviour 1: 0054 EP-.
Retrieved from https://doi.org/10.1038/s41562-017-0054[84] Atsushi Takagi, Masaya Hirashima, Daichi Nozaki, and Etienne Burdet. 2019.
Individuals physically interacting in a group rapidly coordinate their movement
by estimating the collective goal. eLife 8: e41328. https://doi.org/10.7554/eLife.
41328[85] Atsushi Takagi, Francesco Usai, Gowrishankar Ganesh, Vittorio Sanguineti,
and Etienne Burdet. 2018. Haptic communication between humans is tuned by
the hard or soft mechanics of interaction. PLOS Computational Biology 14, 3:
e1005971. https://doi.org/10.1371/journal.pcbi.1005971[86] Akifumi Takahashi, Jas Brooks, Hiroyuki Kajimoto, and Pedro Lopes. 2021.
Increasing Electrical Muscle Stimulation’s Dexterity by means of Back of theHand Actuation. In Proceedings of the 2021 CHI Conference on Human Factors in
Computing Systems, 1–12. https://doi.org/10.1145/3411764.3445761[87] Nobuhiro Takahashi, Hayato Takahashi, and Hideki Koike. 2019. Soft Exoskeleton Glove Enabling Force Feedback for Human-Like Finger Posture Control
with 20 Degrees of Freedom. In 2019 IEEE World Haptics Conference (WHC),
217–222. https://doi.org/10.1109/WHC.2019.8816142[88] Ryo Takizawa, Takayoshi Hagiwara, Adrien Verhulst, Masaaki Fukuoka, Michiteru Kitazaki, and Maki Sugimoto. 2021. Dynamic Shared Limbs: An Adaptive
Shared Body Control Method Using EMG Sensors. In Augmented Humans Conference 2021, 10–18. https://doi.org/10.1145/3458709.3458932[89] Emi Tamaki, Takashi Miyaki, and Jun Rekimoto. 2011. PossessedHand: techniques for controlling human hands using electrical muscles stimuli. In Proceedings of the 2011 annual conference on Human factors in computing systems - CHI’11, 543. https://doi.org/10.1145/1978942.1979018[90] Sho Tatsuno, Tomohiko Hayakawa, and Masatoshi Ishikawa. 2017. Trajectoryadjustment system for learning based on electrical stimulation. In Proceedingsof the 8th Augmented Human International Conference on - AH ’17, 1–4. https://doi.org/10.1145/3041164.3041197[91] Sho Tatsuno, Tomohiko Hayakawa, and Masatoshi Ishikawa. 2017. Supportive
training system for sports skill acquisition based on electrical stimulation. In
2017 IEEE World Haptics Conference (WHC), 466–471. https://doi.org/10.1109/
WHC.2017.7989946[92] Joseph Tiffin and E. J. Asher. 1948. The Purdue Pegboard: norms and studies
of reliability and validity. Journal of Applied Psychology 32, 3: 234–247. https://doi.org/10.1037/h0061266[93] P. Toiviainen and T. Eerola. 2016. MIDI toolbox 1.1. GitHub repository. Retrieved
from https://github.com/miditoolbox/1.1[94] Genki Toyama and Tomoko Hashida. 2018. A Detachable Exoskeleton Interface
that Duplicates the User’s Hand Posture and Motions. In Proceedings of the
9th Augmented Human International Conference, 1–5. https://doi.org/10.1145/
3174910.3174923[95] Hsin-Ruey Tsai, Jun Rekimoto, and Bing-Yu Chen. 2019. ElasticVR: Providing
Multilevel Continuously-Changing Resistive Force and Instant Impact Using
Elasticity for VR. In Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems - CHI ’19, 1–10. https://doi.org/10.1145/3290605.3300450[96] Dishita G Turakhia, Yini Qi, Lotta-Gili Blumberg, Andrew Wong, and Stefanie
Mueller. 2021. Can Physical Tools that Adapt their Shape based on a Learner’s
Performance Help in Motor Skill Training? In Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction, 1–12.
https://doi.org/10.1145/3430524.3440636[97] Dishita G Turakhia, Andrew Wong, Yini Qi, Lotta-Gili Blumberg, Yoonji Kim,
and Stefanie Mueller. 2021. Adapt2Learn: A Toolkit for Configuring the Learning
Algorithm for Adaptive Physical Tools for Motor-Skill Learning. In Designing
Interactive Systems Conference 2021, 1301–1312. https://doi.org/10.1145/3461778.
3462128[98] Thomas Waltemate, Irene Senna, Felix Hülsmann, Marieke Rohde, Stefan Kopp,
Marc Ernst, and Mario Botsch. 2016. The Impact of Latency on Perceptual
Judgments and Motor Performance in Closed-loop Interaction in Virtual Reality.
In Proceedings of the 22Nd ACM Conference on Virtual Reality Software andTechnology (VRST ’16), 27–35. https://doi.org/10.1145/2993369.2993381[99] A. Wege and G. Hommel. 2005. Development and control of a hand exoskeleton
for rehabilitation of hand injuries. In 2005 IEEE/RSJ International Conferenceon Intelligent Robots and Systems, 3046–3051. https://doi.org/10.1109/IROS.2005.
1545506[100] Daniel M. Wegner, Betsy Sparrow, and Lea Winerman. 2004. Vicarious Agency:
Experiencing Control Over the Movements of Others. Journal of Personality andSocial Psychology 86, 6: 838–848. https://doi.org/10.1037/0022-3514.86.6.838[101] Nadir Weibel, Danilo Gasques, Janet Johnson, Thomas Sharkey, Zhuoqun Robin
Xu, Xinming Zhang, Enrique Zavala, Michael Yip, and Konrad Davis. 2020.
ARTEMIS: Mixed-Reality Environment for Immersive Surgical Telementoring.
In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing
Systems, 1–4. https://doi.org/10.1145/3334480.3383169[102] Robrecht P.R.D. van der Wel, Natalie Sebanz, and Guenther Knoblich. 2012. The
sense of agency during skill learning in individuals and dyads. Consciousness

UIST ’22, October 29–November 02, 2022, Bend, OR, USA

and Cognition 21, 3: 1267–1279. https://doi.org/10.1016/j.concog.2012.04.001[103] J. P. Whitney, Tianyao Chen, J. Mars, and J. K. Hodgins. 2016. A hybrid hydrostatic transmission and human-safe haptic telepresence robot. In 2016 IEEE
International Conference on Robotics and Automation (ICRA), 690–695.
[104] Christopher D. Wickens, Shaun Hutchins, Thomas Carolan, and John Cumming.
2013. Effectiveness of Part-Task Training and Increasing-Difficulty Training
Strategies: A Meta-Analysis Approach. Human Factors: The Journal of the Human Factors and Ergonomics Society 55, 2: 461–470. https://doi.org/10.1177/
0018720812451994[105] Louisa Willoughby, Howard Manns, Shimako Iwasaki, and Meredith Bartlett.
2020. From Seeing to Feeling: How Do Deafblind People Adapt Visual Sign
Languages? In Dynamics of Language Changes: Looking Within and AcrossLanguages, Keith Allan (ed.). Springer Singapore, Singapore, 235–252. https://doi.org/10.1007/978-981-15-6430-7_15[106] Xiao Xiao and Hiroshi Ishii. 2011. MirrorFugue: communicating hand gesturein remote piano collaboration. In Proceedings of the fifth international conferenceon Tangible, embedded, and embodied interaction - TEI ’11, 13. https://doi.org/10.
1145/1935701.1935705[107] Hongyi Xu, Espen Knoop, Stelian Coros, and Moritz Bächer. 2018. Bend-it:
design and fabrication of kinetic wire characters. ACM Transactions on Graphics
37, 6: 1–15. https://doi.org/10.1145/3272127.3275089[108] Jing Xu, Naveed Ejaz, Benjamin Hertler, Meret Branscheidt, Mario Widmer,
Andreia V. Faria, Michelle D. Harran, Juan C. Cortes, Nathan Kim, Pablo A.
Celnik, Tomoko Kitago, Andreas R. Luft, John W. Krakauer, and Jörn Diedrichsen.
2017. Separable systems for recovery of finger strength and control after stroke.

Jun Nishida et al.

Journal of Neurophysiology 118, 2: 1151–1163. https://doi.org/10.1152/jn.00123.
2017[109] Vibol Yem, Kevin Vu, Yuki Kon, and Hiroyuki Kajimoto. 2018. Effect of Electrical
Stimulation Haptic Feedback on Perceptions of Softness-Hardness and Stickiness
While Touching a Virtual Object. In 2018 IEEE Conference on Virtual Reality and
3D User Interfaces (VR), 89–96. https://doi.org/10.1109/VR.2018.8446403[110] Toshihiro Yukawa and Tetsuya Kumada. 2010. Continuously variable transmission using quadric crank chains. In 2010 8th IEEE International Conference on
Industrial Informatics, 1043–1048. https://doi.org/10.1109/INDIN.2010.5549598[111] Beste F. Yuksel, Kurt B. Oleson, Lane Harrison, Evan M. Peck, Daniel Afergan,
Remco Chang, and Robert JK Jacob. 2016. Learn Piano with BACh: An Adaptive
Learning Interface that Adjusts Task Difficulty Based on Brain State. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems,
5372–5384. https://doi.org/10.1145/2858036.2858388[112] Beste Filiz Yuksel, Daniel Afergan, Evan Peck, Garth Griffin, Lane Harrison,
Nick Chen, Remco Chang, and Robert Jacob. 2015. Braahms: A Novel AdaptiveMusical Interface Based On Users’ Cognitive State. https://doi.org/10.5281/
ZENODO.1181418[113] Ran Zhang, Thomas Auzinger, Duygu Ceylan, Wilmot Li, and Bernd Bickel. 2017.
Functionality-aware retargeting of mechanisms to 3D shapes. ACM Transactions
on Graphics 36, 4: 1–13. https://doi.org/10.1145/3072959.3073710[114] Roger Zimmermann, Elaine Chew, Sakire Arslan Ay, and Moses Pawar. 2008.
Distributed musical performances: Architecture and stream management. ACM
Transactions on Multimedia Computing, Communications, and Applications 4, 2:
1–23. https://doi.org/10.1145/1352012.1352018