Increasing Electrical Muscle Stimulation’s Dexterity by means of
Back of the Hand Actuation
Akifumi Takahashi

Jas Brooks

The University of Chicago, USA; The University ofElectro-Communications, Japana.takahashi@kaji-lab.jp

The University of Chicago, USAjasbrooks@uchicago.edu

Hiroyuki Kajimoto

Pedro Lopes

The University of Electro-Communications, Japan
kajimoto@kaji-lab.jp

The University of Chicago, USA
pedrolopes@uchicago.edu

Figure 1: We improved the dexterity of the finger flexion produced by interactive devices based on electrical muscle stimulation(EMS). The key to achieve it is that we discovered a new electrode layout in the back of the hand. Instead of the existing EMS
electrode placement, which flexes the fingers via the flexor muscles in the forearm, we stimulate the interossei/lumbricals
muscles in the palm. Our technique allows EMS to achieve greater dexterity around the metacarpophalangeal joints (MCP),
which we demonstrate in a series of applications, such as (c) a two-stroke drum roll or (d) a barred guitar fret. These examples,as with all others in our paper, were previously impossible with existing EMS electrode layouts.

ABSTRACT
We propose a technique that allows an unprecedented level ofdexterity in electrical muscle stimulation (EMS), i.e., it allows interactive EMS-based devices to flex the user’s fingers independentlyof each other. EMS is a promising technique for force feedback
because of its small form factor when compared to mechanicalactuators. However, the current EMS approach to flexing the user’s
fingers (i.e., attaching electrodes to the base of the forearm, where
finger muscles anchor) is limited by its inability to flex a target
finger’s metacarpophalangeal (MCP) joint independently of theother fingers. In other words, current EMS devices cannot flex one
finger alone, they always induce unwanted actuation to adjacentfingers. To tackle the lack of dexterity, we propose and validate a
new electrode layout that places the electrodes on the back of thehand, where they stimulate the interossei/lumbricals muscles in thepalm, which have never received attention with regards to EMS. In
our user study, we found that our technique offers four key benefits

when compared to existing EMS electrode layouts: our technique(1) flexes all four fingers around the MCP joint more independently;(2) has less unwanted flexion of other joints (such as the proximal
interphalangeal joint); (3) is more robust to wrist rotations; and (4)reduces calibration time. Therefore, our EMS technique enables applications for interactive EMS systems that require a level of flexion
dexterity not available until now. We demonstrate the improved
dexterity with four example applications: three musical instrumental tutorials (piano, drum, and guitar) and a VR application that
renders force feedback in individual fingers while manipulating ayo-yo.

CCS CONCEPTS• CSS CONCEPTS; • Human-centered computing; • Humancomputer interaction (HCI); • Interaction devices; • Haptic
devices;KEYWORDS
Electrical muscle stimulation, EMS, dexterity, haptic actuation
This work is licensed under a Creative Commons Attribution-Share Alike
International 4.0 License.
CHI ’21, May 08–13, 2021, Yokohama, Japan© 2021 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-8096-6/21/05.
https://doi.org/10.1145/3411764.3445761

ACM Reference Format:
Akifumi Takahashi, Jas Brooks, Hiroyuki Kajimoto, and Pedro Lopes. 2021.
Increasing Electrical Muscle Stimulation’s Dexterity by means of Back of
the Hand Actuation. In CHI Conference on Human Factors in Computing
Systems (CHI ’21), May 08–13, 2021, Yokohama, Japan. ACM, New York, NY,
USA, 12 pages. https://doi.org/10.1145/3411764.3445761

CHI ’21, May 08–13, 2021, Yokohama, Japan1

Akifumi Takahashi et al.

INTRODUCTIONEMS is a promising technique for force-feedback & haptic-actuation
because of its small form factor when compared to mechanicalactuators (e.g., exoskeletons [1] and so forth). Therefore, EMS has
been recently used to enable a wide range of haptic applications,such as: moving a user’s wrist to tap to a beat [2, 3], teachingusers how to manipulate objects they have never seen before [4],communicating information eyes-free [5] or even controlling theuser’s hand, which is holding a pen, to act as a plotter device [6].
However, all these aforementioned EMS systems, and all othersin the literature, are limited in that they either do not actuate theuser’s fingers (mostly only the wrist) or actuate the user’s fingers
very coarsely and not independently of each other, i.e., in most ofthese systems a particular target finger is flexed by means of EMS
and other fingers start flexing as well, causing unwanted actuationand lack of dexterity—we denote this as the dexterity of the movements/poses induced by EMS on the user, not the user’s own dexterity. In fact, the lack of dexterity in EMS-induced finger flexions waswell documented in the PossessedHand [7], a work that pioneered
the use of EMS to build interactive devices. In this system, the authors found that they could not flex any finger around the metacarpophalangeal (MCP) joint independently of the other fingers. Although 10 years have passed since the PossessedHand, it still stands
as the most dexterous finger flexions in all interactive EMS devices.
We identify the cause of EMS’s lack of dexterity when inducing
a finger flexion, pinning it down to the fact that virtually every
researcher and practitioner realizes it by placing electrodes at theforearm. While this location intuitively makes sense, since all thefinger flexor muscles are anchored there, it also is the cause for thelack of dexterity for three key reasons: (1) the flexor muscles in theforearm are densely packed, as they all meet at the elbow as a sharedanchor; as such, attempting to flex a finger by means of electrodes
attached to the skin causes currents to run also through an adjacentmuscle, making other muscles move unwantedly; (2) the flexor
muscles at the forearm are layered with finger flexors at one depth
and wrist flexors at another; as such, attempting to flex a finger bymeans of electrodes attached to the skin often causes currents to
run through the other layers as well, causing other muscles (e.g.,wrist) to move unwantedly; (3) when we turn our wrist, the skin at
the forearm rotates by a different amount compared to the muscles
at the forearm; thus, whenever the wrist moves, the electrodes end
up stimulating a new muscle, causing unwanted flexions.
To illustrate the importance of this problem, our literature review
found that, in just the last 17 years of EMS research in HCI/Haptics,
54 publications [2–55] have used, tested, described, or proposed
applications that use this exact electrode placement on the forearmto achieve finger flexions. All these systems, unfortunately, do notexhibit more dexterity in the finger flexions because they are limitedby this electrode layout, which by now has become so “standard” itgoes unquestioned by researchers.
To address the lack of dexterity in EMS, we discovered a novel
electrode arrangement, depicted in Figure 1 (a), that robustly enables individual finger flexion. Our approach differs from thestandard-EMS electrode arrangement in that we place the EMS
electrodes on the back of the hand and instead stimulate the interossei/lumbricals muscles in the palm.

Figure 2: For this participant, (a) the index finger was independently flexed at the MCP joint by our back-of-handstimulation, yet (b) using the standard-EMS stimulation, it
induces several unwanted effects, e.g., flexion of other fingers or of the PIP joint.
Surprisingly, we found through a user study, that this new electrode arrangement provides four key benefits when compared to thestandard-EMS arrangement: our technique (1) flexes all four fingers
around the metacarpophalangeal joint (MCP) more independently;(2) has less unwanted flexion of other joints (such as the proximal
interphalangeal joint); (3) is more robust to wrist rotations; and (4)reduces calibration time.
To visually illustrate the difference in dexterity achieved by ourapproach when compared to the standard-EMS approach, we depict
in Figure 2 one striking example of the resulting actuation of theindex finger of an exemplary participant from our User Study.

2

RELATED WORK

Our work builds primarily on the field of haptics, in particularelectrical muscle stimulation. Also, to familiarize the reader withthe kinematics of the hand, we present first an overview of its joint
mechanisms and musculature.

2.1

Human Hand Joints

Most humans have five digits per hand, four fingers (index, middle,
ring, and pinky), and a thumb. Each finger has three joints: distalinterphalangeal (DIP), proximal interphalangeal (PIP), and metacarpophalangeal (MCP) as ordered from the tip of the finger to thepalm. In our paper, we focus on flexing the fingers around the MCP
joint as well as minimizing concurrent unwanted flexions of the
PIP joint.

2.2

Motor-Based Finger Actuation (e.g.,
Exoskeleton Gloves)

One way that researchers have long explored actuating the user’s
fingers to provide force feedback or guidance is by means of motorbased haptic gloves. Exoskeleton gloves are one example that successfully achieved dexterous manipulation by essentially addingIncreasing Electrical Muscle Stimulation’s Dexterity by means of Back of the Hand Actuationan actuator (typically a motor with a pulley) to every finger’s MCP
and/or PIP joints. These motors connect the finger segments via
mechanical linkages to achieve maximum force transmission whenpulling against the palm (the base). While many exoskeleton designs exist, including soft or brake-based exoskeletons, the mostdexterous devices tend to be rigid, such as the haptic workstation[56], which is one of the earliest fully-dexterous mechanical haptic
devices for the hands. Other notable examples include cyber grasp[57], Sandoval-Gonzalez et al.’s exoskeleton [1], or dexmo [58], justto cite a few.

2.3

Electrical Muscle Stimulation

Electrical Muscle Stimulation (EMS) is a technique to induce muscle
contractions by means of electrical impulses, typically via electrodes
attached on the skin. EMS originated in the 1960s’ medical rehabilitation as a means to restore lost motor functions [59]. Only
more recently have researchers in HCI started to explore EMS withthe pioneering uses of interactive EMS by Kruijff et al. for flexing
the biceps as video-game feedback [60] and the aforementioned
PossessedHand [7], which induced finger flexions and extensions.
While the aforementioned motor-based haptics are more precise
than the resulting flexion quality induced by muscle stimulation,
EMS is not without its advantages over its mechanical-counterpart.
EMS systems are generally more wearable as their resulting formfactor is much smaller than motor-based devices [61]. As such,many researchers consider EMS as an ideal actuation platform formobile haptics, such as force-feedback in VR [33], AR [32], or haptic
instructions [11, 34, 43, 55].
However, when we zoom into each of these interactive devices
based on EMS, we see that their actuation quality is coarse andimprecise. Most systems target simple joints, like the wrist, andonly a smaller number, such as the PossessedHand, target moving
fingers. More importantly, while the PossessedHand attempted tohave their participants learn how to play a string-based instrument,
the researchers found in their study that its electrode placement,
which is now the de-facto “standard” in EMS, could only actuate
three fingers independently around the PIP joint: index, middle, andring. They found that EMS could not move any finger independently
around the MCP joint. This is a critical limitation of today’s EMS
since it prevents building haptic applications that require any formof finger dexterity. Unsurprisingly, this is why applications suchas “playing piano with EMS” are desirable, but, 10 years after the
introduction of EMS at CHI, we still cannot realize it.
Some promising advances have been made to this end, such as
Watanabe et al.’s [52] method to control the middle finger at the
MCP and PIP joints by stimulating both its flexor and extensor.
While they succeeded in controlling this finger, they did not create
reliable actuations around the MCP joint for all fingers.
The reason why the independent flexion of fingers under EMS
control has been so elusive is tied to the location that all theseresearchers place their electrodes at: the forearm.

2.4

Muscle Arrangement for Flexors of MCP
joint at the forearm (“standard-EMS”)

Figure 3 depicts the cross-section of the forearm. The key insightthat drives the standard-EMS arrangement is that the finger flexor

CHI ’21, May 08–13, 2021, Yokohama, Japan(MCP) muscles are packed densely adjacent to one another and alsolayered one on top of each other inside the forearm. Therefore,
researchers have been using this location in virtually any EMS
system that achieves finger flexion, including usages outside of HCI
such as in medicine [54, 55, 62].
To illustrate the wide-spread use of this electrode placement, wefound that in just the last decades of EMS research in HCI/Haptics,
54 publications [2–55] have used, tested, described, or proposed
applications that use this exact electrode placement on the forearmto achieve finger flexions. Following, this arrangement emerged asa de-facto “standard-EMS” placement. In fact, like all our community, we have used this arrangement extensively in our own EMS
research in both HCI and neuroscience.

3

OUR APPROACH FOR PRECISE FLEXORACTUATION: EMS ON THE BACK OF THE
HAND

We solve the lack of dexterity in EMS by discovering a new electrodearrangement. Instead of placing the electrodes at the base of thearm to target the finger flexors at that location, we stimulate theback of the hand, targeting the unexplored lumbricals/interossei.
As we found in our empirical study, this results in more dexterity
when flexing fingers around the MCP joint using EMS.
The key insight that enables our approach is depicted in Figure
4. Interestingly, the hand’s dorsal side has no extensor muscles andmost of the dorsal muscles are interossei and lumbricals muscles,
which happen to be MCP finger flexors. Importantly, these muscles
are close to the surface and spaced from each other, making them
ideal targets for EMS activation by placing electrodes on the back of
the hand. While this might appear as trivial in hindsight, it has been
completely overlooked and presents an immediate and significant
impact on the dexterity of any EMS-based interactive devices thatuse any type of finger flexions.

4

BENEFITS, CONTRIBUTION AND
LIMITATIONS

Our key contribution is that we discovered a new electrode arrangement that enables any interactive device based on EMS finger
flexion to actuate the user’s fingers with more dexterity. Insteadof placing the electrodes at the forearm, we stimulate the interossei/lumbricals at the back of the hand.
Our contribution was validated by a user study, providing a thorough characterization of the technique’s accuracy. We found that
our technique has four key benefits when compared to standardEMS placement: our technique (1) flexes all four fingers aroundthe metacarpophalangeal joint more independently; (2) has lessunwanted flexion of the PIP joints; (3) is more robust to wrist rotations, i.e., users can finally move their hands freely while EMS still
flexes their fingers accurately; and (4) reduced calibration time.
Our approach is not without its limitations. (1) Like any EMS
approach, it requires calibration prior to use, but we found thatit reduces this calibration time. (2) It targets only the MCP flexor
muscles, not the extensors or PIP flexor muscles. While the MCP
independence that we achieve is key for many haptic applications,
it is not the complete vocabulary for all hand-poses. We believe

CHI ’21, May 08–13, 2021, Yokohama, Japan

Akifumi Takahashi et al.

Figure 3: A model of cross-section muscle arrangement in the forearm and the hand, drawn from anatomy literature [63].
that the latter can be resolved by integrating both the standardEMS approach with our novel arrangement, thus stimulating both
flexor and extensor around the MCP. Note that our approach cannot
possibly target any extensor muscles since there are none in the
palm.

5

USER STUDY: INDEPENDENCE OF FINGERFLEXION DURING EMS ACTUATION (MCP
JOINT)

In our user study, we aimed to validate our key contribution, i.e.,
stimulating the back of the hand to achieve a higher level of dexterity than the standard EMS approach that stimulates the forearm
flexors. As such, we measured the angles of participants’ fingers(all four fingers, no thumb) when stimulated by means of either
our back-of-hand EMS approach or the standard-EMS approach,which stimulates the finger flexion around the MCP joint via theflexor muscles at the base of the arm. By observing the fingers as
they flex, one can extract the index-of-independence of each finger
when actuated by means of EMS, i.e., how much this finger moves
independently of the other. This is a physiological measure, usedalso in [64], to evaluate the independence of finger movements. This
study was approved by our local Ethics Review Board (ID#18044).

5.1(i.e., minimize unwanted movement of adjacent fingers) than thestandard-EMS approach. For our secondary hypothesis, (H2) we
expected that our approach would lead to less unwanted movement
on other joints, i.e., we expected that our approach would lead toless actuation of the PIP joints when targeting the MCP joints. For
our tertiary hypothesis, (H3) we expected our approach to be morerotation invariant than the standard-EMS, i.e., we expected thatback-of-hand EMS would actuate fingers more independently ofeach other even if the user’s palm was rotated by an extreme 180
degrees (palm down). Lastly, we postulated (H4) that calibration
time would be reduced for back-of-hand stimulation; this should
occur as back-of-hand actuation targets the interossei/lumbricalmuscles, which are not as heavily layered as the forearm flexor
muscles targeted in the standard-EMS approach.

5.2

Interface Conditions

Each participant experienced our study in two distinct interface
conditions: (1) back-of-hand EMS stimulation, in which the electrodes were applied using our novel layout by targeting the dorsalside of their hand; and, (2) standard-EMS, in which the electrodes
are placed along the flexor muscles of the forearm (as often used in
EMS research [2–55], but most canonically in Possessed Hand [7]).
For either condition, we utilized the same EMS stimulator with thesame stimulation pattern.

Four Hypotheses

We postulated four hypotheses for our experiment. For our main
hypothesis (H1), we expected that our back-of-hand EMS approach would actuate each finger more independently of each other

5.3

Electrode Placement

For our back-of-hand electrode placement, we utilized five electrodes: one 1 x 3 cm electrode for each finger as the positive end,

Figure 4: The key insight that enables our approach to achieve dexterity is that the lumbrical and interossei muscles, at theback of the user’s hand, are more easily accessible to EMS than the standard forearm flexor muscles.

Increasing Electrical Muscle Stimulation’s Dexterity by means of Back of the Hand Actuation

which was placed alongside the respective finger’s metacarpus (fourtotal for four fingers), and one 3x3 cm electrode as common ground,which was placed at the wrist.
For the standard-EMS electrode placement, we followed theplacement described by Possessed Hand [7], selecting only the relevant electrodes for our comparison, which are the flexor electrodes.
As such, we also place five electrodes: one 1 x 3 cm electrode as
the positive end, placed over the respective flexor muscle at theforearm (as in [7]), and one 5 x 5 cm electrode as common ground,which was placed at the wrist. We follow a calibration procedure as
described in Possessed Hand [7] and calibrate each finger movement
one by one to achieve maximum independent motion by optimizing
the placement for each user’s arm anatomy.

5.4

Apparatus

We used a keyboard wrist rest so that participants rested theirforearm comfortably during the trials. This minimized the effect of
fatigue from unrelated muscles such as biceps, triceps, etc.
To capture the angles of both MCP and PIP joints accurately, we
instrumented participants’ hands with wearable inertial measurement units (with 6DOF sampled at 100 Hz). We added one 9DOF
IMU per finger joint and an additional one at the dorsal part of thehand for calibration and reference, totaling nine IMUs. We initiallyconfirmed the accuracy of our IMU tracking system by comparing
its accuracy when recording movements against a ground truth protractor (angle measurement). We recorded an accuracy of 5 degrees
with a drift of 2°/min. Since our single trials last only for 4 seconds,
this drift had virtually no impact, as our apparatus re-calibrated
the IMU’s drift prior to every trial.
Lastly, both our condition and the baseline used the same EMS
stimulator (the same used in [65]) and the same stimulation waveform (biphasic waveform, with a pulse width of 200 µs for the
positive phase, 100 µs rest period, and 200 µs for negative phase)at 50 Hz. As common in EMS research, the intensity (current) was
calibrated per-participant to ensure pain-free operation. Lastly, wefilmed participants’ fingers via a side-camera.

5.5

Calibration

Prior to the experiment, we calibrated the EMS for each participant.
First, to determine the stimulation intensity (i.e., current, in mA)
we actuated the back-of-hand flexor of the participant’s index finger. We started with an intensity of 0mA and a pulse-width of 200µs (which we call zero point) and slowly increased the intensity
in 1 mA steps until the participant’s finger was fully flexed; this
newly found intensity value was then fixed for all conditions andfingers and all further adjustments were made only by varying thepulse-width—this process effectively normalizes intensity values
across multiple users, allowing us to compare intensity values bycomparing the pulse-width required to actuate each finger (Figure5). Then, after fixing the intensity for this participant, we adjusted
the pulse-width (in µs) to actuate all fingers in both conditions. In
this step, we calibrated following a manual version of PossessedHand [7]: (1) attached electrodes to the target muscles following
anatomical guide; (2) per electrode, started with low pulse-widthand increased it step-by-step, confirming at each step with the participant that the stimulation was is pain-free; (3) repeated until no

CHI ’21, May 08–13, 2021, Yokohama, Japan

more flexion happens at the target finger or pain has been reached(the latter never occurred). Before starting the trials, we validated
the calibration and recalibrated if needed, removing any confoundin the order of each condition’s calibration.
After calibrating all participants, we found an average currentof 9.23mA (SD=2.3), and the average pulse-width, per finger andcondition, is depicted in Figure 5. As observed, both conditions
require very similar pulse-widths to flex the fingers.

5.6

Task and Procedure

Each participant performed a total of 48 trials: 2 conditions (backof-hand or standard-EMS) × 4 fingers × 3 repetitions × 2 palm
directions (up or down). Per trial, a 2s stimulation was applied ina randomized order and the resulting pose of the four fingers wasmeasured via video (from 1s after the stimulation to 1s after the endof a trial). We instructed participants to relax their hands before
every single trial started.

5.7

Participants

We recruited nine participants (all self-identifying as male; all righthanded) from our local institution. Participants received a 1000 JPY
compensation. Only four participants had previously experienced
EMS.

5.8

Metric: Independence Index

Our study is based on the independence index (also known as
I.I. in literature), a standard metric used to measure the amount of
independent movement of a finger. The independence index denotes
the ratio of movement between a finger’s joint (measured in theangle of the joint’s movement) to how much the other joints moved[64]. Thus, when considering only the MCP joint, the independentindex of a target finger i (IIi ) can be calculated as follows:1Í
k ∈Γi |Ak |IIi = 1 − 3, where, Ak = ∫ θ k dt,|Ai |T
Here, i denotes a stimulated (target) finger and Γi is a set of notstimulated fingers. An independence index of 0 or lower signifies
that moving the target finger caused movement in the other fingers instead of independent movement of the target finger itself.
Conversely, an index closer to 1 signifies more independence, witha perfect “1” depicting a complete independent movement of thetarget finger (with no unwanted movements from other fingers).
However, it is critical to note that, the human hand’s biomechanics
do not exhibit fully independent fingers. As experienced in daily life,
many fingers bend when other fingers move. Lang et al. measured
an independence index of 0.84 for passive finger flexions aroundthe MCP joint when a participant’s finger was bent passively by a
motor to extract its natural limits [64]. For the sake of visual clarity,
we depicted this value of 0.84 as a dashed line in all our charts(annotated as “maximum voluntary independence”).

5.9

Results

We analyzed the effects of interface condition, palm direction, and target finger on the independence index scores, and their possible interactions, by a model of multivariate analysis of variance (MANOVA),
followed by four separated univariate three-way ANOVA analyses.

CHI ’21, May 08–13, 2021, Yokohama, Japan

Akifumi Takahashi et al.

Figure 5: Resulting pulse-width after calibration for all fingers in both conditions (index finger as zero point; see calibration).
The MANOVA model included the aforementioned three factors(interface condition, palm direction, target finger) as independent
variables and the MCP independence index and the ratio of PIP-toMCP movement as dependent variables. This MANOVA analysis
showed a significant main effect of interface condition [Pillai’s trace
0.181, F(2,127)=14.1, p<0.001]. Further, a significant effect emerged
for palm direction [Pillai’s trace 0.0649, F(2,127)=4.41, p<0.05]. The
three-way ANOVA analyses showed similar significant effects of theinterface condition on the MCP independence index [F(1,128)=20.5,
p<0.001] and the ratio of PIP-to-MCP movement [F(1,128)=26.1,
p<0.001].
Figure 6 depicts our results with respect to the differences between the dexterity of stimulating the fingers around the MCP joint
with back-of-hand compared to standard-EMS, as measured by
the independence index. Taken together, our previously reported
main effect of interface conditions [Pillai’s trace 0.181, F(2,127)=14.1,
p<0.001] and the significant effect of the conditions on the MCP
independence index [F(1,128)=20.5, p<0.001] allow us to reject thenull hypothesis (that there is no difference between conditions
on MCP independence index scores) and support our H1. In fact,
as depicted in Figure 6, we found that the index of independence
averaged for all fingers was higher when actuated via back-ofhand EMS (M= 0.62; SD= 0.21); compared to standard-EMS (M=0.18; SD= 0.81); this suggests that overall actuation by means ofback-of-hand EMS is more dexterous than the standard-EMS
practice.

In particular, we measured an increased independence index inall four fingers: (1) the index finger was actuated with an average independence index of 0.62 (SD=0.33) with back-of-hand andan average of -0.08 (SD=1.00) with standard-EMS; (2) the middle
finger was actuated with an average independence index of 0.563(SD=0.14) with back-of-hand and an average of 0.34 (SD=0.37)
with standard-EMS; (3) the ring finger was actuated with an average independence index of 0.59 (SD=0.16) with back-of-handand an average of 0.33 (SD=0.34) with standard-EMS; and, lastly,(4) the pinky finger was actuated with an average independence
index of 0.72 (SD=0.14) with back-of-hand and an average of 0.14
(SD=1.16) with standard-EMS.
Next, we analyze our second hypothesis, in which we postulated
that our back-of-hand would exhibit less unwanted actuation of
the PIP joint than standard-EMS. Our previously reported significant effect of the ratio of PIP-to-MCP movement [F(1,128)=26.1,
p<0.001] allows us to reject the null hypothesis and support our
H2. In fact, as depicted in Figure 7, we found that there were significantly more unwanted movements of the PIP joints when theparticipants’ fingers were flexed using standard-EMS (M=0.83,
SD=0.48) than with back-of-hand EMS (M=0.41, SD=0.19). In particular: (1) when actuating the index finger we observed a ratio of
PIP movements with an average of 0.65 (SD=0.37) with standardEMS compared to a lower average of 0.37 (SD=0.14) with back-ofhand; similarly, we observed this trend on remainder fingers as
depicted in Figure 7

Figure 6: Measured index of independence when actuating each of the participants’ fingers (index, middle, ring, pinky) using
either back-of-hand EMS or standard-EMS; last box-plot depicts averages for all fingers. There are four outliers trimmed outof this plot for visualization clarity (for standard-EMS. -1.64, -2.70, 1.95 in the index, -4.23 in the pinky).

Increasing Electrical Muscle Stimulation’s Dexterity by means of Back of the Hand Actuation

CHI ’21, May 08–13, 2021, Yokohama, Japan

Figure 7: Measured ratio of unwanted PIP movements when flexing each of the participants’ fingers around the MCP joint(index, middle, ring, pinky) using either back-of-hand EMS or standard-EMS.
Next, we analyze our third hypothesis, in which we postulated
that our back-of-hand would allow for more independent finger
flexions under extreme arm rotations than standard-EMS. Our previously reported significant effect of palm direction [Pillai’s trace
0.0649, F(2,127)=4.41, p<0.05] allow us to reject the null hypothesis (that there is no difference between the interface conditions
regarding palm direction); thus, there is a difference in these conditions, which we analyze next. In fact, as depicted in Figure 8, wefound that the independence of standard-EMS decreased significantly as the participants’ palms were simply turned down (drastic drop from M=0.31, SD=0.45 to M=0.055, SD=1.04) when compared to back-of-hand (a slight decrease from M=0.65, SD=0.16
to M=0.60, SD=0.25). This supports our H3. In particular, the middle finger illustrates the negative impact that the palm rotation
had on the performance of the standard-EMS (decreasing from
M=0.40, SD=0.29 to M=0.34, SD=0.35), but barely any impact inback-of-hand (M=0.57, SD=0.13 and similarly M=0.54, SD=0.16).
Moreover, as depicted in Figure 8 this trend is similar for the index
finger.
As expected, the ring was the outlier, relatively stable in bothconditions with minor improvement in independence in back-ofhand. This was expected because the ring is anatomically an easily
accessible muscle. In fact, this is why a number of interactive EMS
devices actuate precisely this finger, for instance in [24], authors

state “we could robustly actuate it without any parasitical motionof neighboring muscles”.
Finally, for the pinky finger, we observed an improvement forback-of-hand as the palm turned.
Last, we analyze our fourth hypothesis, in which we postulated
that our back-of-hand would require less calibration time thanstandard-EMS. We found, by means of an independent t-test, asignificant difference between the time to calibrate a participant
per conditions (p< 0.01). This confirms our H4. In fact, depicted in
Figure 9, we found that back-of-hand calibration is significantly
quicker (M=6.02, SD=2.59) than standard-EMS (M=15.6, SD=7.9);
note that we used the same calibration procedure for both andthat we have far more years of experience with the standard-EMS
calibration, having it performed thousands of times.

5.10

Discussion

Our results demonstrate that our method provides interactivesystems based on EMS with more independent actuation for all
fingers around the MCP joint, when compared to the standard-EMS
method. We now discuss our findings in detail, organized in five
topics: (1) finger independence on MCP flexion; (2) minimizing unwanted actuation of other joints (e.g., PIP); (3) robustness to wrist
rotations; and, (4) reduction of calibration time. Finally, we note that

Figure 8: Measured index of independence when flexing each of the participants’ fingers (index, middle, ring, pinky) in two
different arm direction (palm-up or palm down) using either back-of-hand EMS or standard-EMS. There are four outliers
trimmed out of this plot for visualization sake (for standard-EMS, one point (-1.64) in Index-Palm-Up, two points (-2.70 and1.95) in Index-Palm-Down, one point (-4.23) in Pinky-Palm-Down).

CHI ’21, May 08–13, 2021, Yokohama, Japan

Akifumi Takahashi et al.

Figure 9: Calibration time using either back-of-hand EMS or
standard-EMS.
with our sample size (N=9) our findings should not be generalized
to variations in anatomy that might occur in different subjects.
5.10.1 Finger Independence During EMS Flexion Around the MCP
Joint. Our main finding was that stimulating the muscles at theback of the hand results in more dexterous actuation of the flexor
muscles, i.e., fingers flex independently of each other. As we demonstrate in our applications, this is critical to enable scenarios that
depict realistic grasp poses. None of these are currently possible
with the standard-EMS approach of stimulating the flexor muscles
via the forearm. Our results for the finger independence during
EMS actuation, depicted in Figure 6, demonstrated two key improvements: (1) flexion for all four fingers (index, middle, ring, and pinky)around the MCP joint improved in independence on average by
79.9% and, moreover, (2) the amount of standard deviation was reduced, suggesting our back-of-hand actuation is also more robust
over multiple repetitions. To give the reader a visual illustration of
the improved quality of the achieved dexterity, we selected a few
examples from the study, which we depict in Figure 10

Figure 11: Examples from our study of EMS finger flexions
targeted at the MCP joint: (a) middle finger and (b) ring finger. “✓” denotes a correct finger flexion by back-of-hand,
while “%” denotes an incorrect finger flexion by standardEMS.
5.10.2 Minimizing Unwanted Actuation of the PIP joint. One main
limitation that Tamaki et al. discussed in PossessedHand [7] is thatwhen actuating the MCP joint via the standard-EMS electrode position (base of the forearm) it induces unwanted flexions of the PIP
joints. This is a showstopper for haptic applications that require
finger dexterity, e.g., playing musical instruments, etc. Fortunately,
we found that our technique significantly minimized the amountof unwanted flexion at the PIP joint, with an average reduction of-51.2%. To give the reader a visual illustration of the minimizationof the unwanted movements of the PIP joint, we selected a few
examples from the study, which we depict in Figure 11
5.10.3 Robustness to Wrist Rotations. One critical limitation of
EMS systems that actuate the fingers is that, when the user turns
their palm, the actuation becomes imprecise for any finger flexions;this was well-documented and discussed in Affordance++ [4] andPossessedHand [7]. This happens because when the human arm
rotates to turn the palm upside down, our skin (where the electrodes
are attached) moves independently from the muscle structure. As
such, when the hand rotates, the electrodes are displaced and thenactuate the “wrong” portion of the flexor muscles. This is a major
obstacle for applications where users move their hands, including
virtually any practical application of EMS. Our finding was that
our technique significantly improved the robustness of the flexionof the MCP joint during palm rotations, i.e., the actuation had asimilar performance when the palm was up or down. To give thereader a visual illustration of the improvements when the palm
is rotated, we selected a few examples from the study, which we
depict in Figure 12.

Figure 10: Examples from our study of EMS finger flexions
targeted at the MCP joint: (a) middle finger and (b) pinky
finger. “✓” denotes a correct finger flexion by back-of-hand,
while “%” denotes an incorrect finger flexion by standardEMS.

5.10.4 Reduction in Calibration Time. One key limitation of any
EMS system (ours included) is that they require calibration prior
to use. This is well documented in the early works that pioneered
the use of EMS in interactive systems, such as Kruiff et al. [60] andPossessedHand [7]. Our finding was that our technique significantly
minimized the calibration time to one third of the time neededIncreasing Electrical Muscle Stimulation’s Dexterity by means of Back of the Hand Actuation

CHI ’21, May 08–13, 2021, Yokohama, Japan

Figure 12: Examples from our study of EMS ring finger flexions targeted at the MCP joint while rotating the palm (up or
down). “✓” denotes a correct finger flexion by back-of-handin both palm up or down, while “%” denotes an incorrect
finger flexion by standard-EMS that dramatically decreasedin quality when the palm was rotated down.
for standard-EMS. This is most likely caused by the fact that themuscles that our technique purposely targets are not as densely
packed nor layered, which is the case for the finger flexors in theforearm. It is important to note that, in fact, the experimenters
had more years of experience with the standard-EMS electrodelayout, having performed thousands of standard-EMS calibrations
for studies in HCI and neuroscience as well as demos at conferences.
Unsurprisingly, even though we were very unfamiliar with the
back-of-the-hand layout, it was significantly easier to calibrate,
which explains the reduced calibration time. We believe this result
is important not only for engineering interactive systems withsignificantly less calibration effort, but especially for anyone using
EMS in both HCI, neuroscience, and rehabilitation.

6

EXAMPLES OF DEXTROUS HAPTICAPPLICATIONS ENABLED BY OURTECHNIQUE

Now that we validated that our back-of-hand EMS approach affords
more dexterity by allowing us to flex fingers more independently,
we explore some of the applications that our contribution enables.
Note that all these applications are novel in that they are simply
not possible with the standard EMS approach because all of theserequire individual finger actuation with significant precision. For instance, while researchers have used EMS for drumming by coarsely
moving the user’s wrist or biceps up/down, we demonstrate how todrum using a professional drumming technique, the double strokeroll, which requires individual control of the ring finger.

6.1

Actuating Double-Stroke Drum Rolls withEMS

After drummers spend some time acquainting themselves withthe basic stroke (hitting the drum pad with one swift motion of
their wrist) they start exploring how to drum faster. This is typicallyFigure 13: This drummer is being actuated by means of EMS
to perform a double-stroke roll, a movement that requires
significant finger independence (a quick finger flexion of thering finger around the MCP joint) to realize.

achieved with a technique called double-stroke drum roll (Figure 13).
Unlike the basic stroke, double-stroke rolls require a high level ofdexterity, in which the motion of the second stroke is entirely driven
by a very precise movement of the MCP joint alone of the ring
finger; this is used by drummers using the so-called “French grip”
to grab the drumstick. We implemented a simple haptic application,
depicted in Figure 13, which actuates users in performing the twostroke roll.
6.1.1 Actuating Fingers Independently via EMS to Play Piano Notes
And Chords. Much like all applications in our paper, this is a longsought application in EMS that was never realized due to the lack ofdexterity. However, we can now realize it via the back of the handstimulation. In Figure 14, we depict a user who is being actuated to(a) play a simple one-handed piano melody with EMS finger flexions
around the MCP joint for each finger; and, (b) play a perfect fifthinterval by having the EMS flex their fingers to correctly play twonotes at the same time. While our technique provides the first step
towards EMS for piano playing by flexing the user’s fingers aroundthe MCP joint, it does not actuate all finger joints required to play
piano in its full expression.
6.1.2 Actuating Fingers Independently To Play Barred Frets On The
Guitar. For playing the guitar, an important skill is to press all
strings at a fret with the same finger (usually the index finger),which is required to make a barre chord—we chose these types ofchords as they require only MCP flexion, unlike generic chords
that typically require flexion of other joints. Figure 15 depicts an
example of our simple haptic application that actuates users how

CHI ’21, May 08–13, 2021, Yokohama, Japan

Figure 14: (a) With this newly achieved EMS dexterity, we
can actuate a user to play simple piano melodies by having
their fingers flexed, independently, by means of EMS. Also(b) we can assist a user in playing multiple notes and chords,
such as this perfect fifth interval played, by means of EMS
flexion of their index and pinky fingers at the MCP joint.

Akifumi Takahashi et al.

Figure 15: (a) Back-of-hand stimulation assists this user inperforming a barred fret by flexing (a) their index finger
and (b) their middle finger so that all strings at a fret are
be pressed down completely.

to make barred frets by independently flexing their fingers aroundthe MCP joint.
6.1.3 Force Feedback for Individual Fingers in VR. While the previous applications used EMS to create motion guidance, our last
application depicts an entirely different class: EMS to create force
feedback. While EMS has been an increasingly popular techniqueto achieve wearable force feedback (e.g., [22, 32–34], there has notbeen any system that can stimulate each finger independently while
retaining performance during wrist rotations, which is requiredby virtual reality (VR) applications where the user’s hands movefreely in space. In Figure 16, we exemplify this by means of a simple
VR yo-yo application: (a) the user throws down a virtual yo-yo;
then, (b) as the yo-yo’s string hits its limit it pulls down on theuser’s middle finger, where the virtual yo-yo’s string is knotted.
Here, our back of the hand stimulation renders the force feedbackof the yo-yo’s pull by independently actuating a single finger, evenunder different wrist rotations caused by the user as they movetheir hands around to play with the yo-yo.

7

CONCLUSIONSIn this paper, we proposed, implemented, and tested a novel electrode layout that allows an unprecedented level of dexterity in electrical muscle stimulation (EMS), allowing interactive EMS-based
devices to flex the user’s fingers independently of each other. Ourelectrode layout places the electrodes on the back of the hand, where
they stimulate the interossei/lumbricals muscles in the palm—a set

Figure 16: (a) The user has a virtual yo-yo in their hand with
its string tied to the user’s middle finger. (b) As the yo-yo isreleased, the yo-yo descends and pulls on the middle finger
independently. The EMS from the back of the hand renders
the yo-yo’s force.

Increasing Electrical Muscle Stimulation’s Dexterity by means of Back of the Hand Actuationof muscles that have been unexplored in EMS and that offer the following key improvements to the current EMS practice: (1) because
these muscles are not as densely packed as the forearm flexors,
our technique flexes all four fingers at the MCP joint more independently; (2) because these muscles are less densely layered than
the forearm muscles, there is less unwanted flexion of other joints(such as from the proximal interphalangeal joint); (3) because thesemuscles are not subjected to forearm rotations, our technique ismore robust to wrist rotations; and (4) because of all of the above,
our technique reduced calibration time. We validated all these improvements by means of a user study.
To sum up, our EMS technique enables applications for interactive EMS systems that required a level of flexion dexterity unachievable until now. We demonstrate its dexterity with four example
applications: three musical instrumental tutorials (piano, drum, andguitar), which require individual actuation of only a finger’s MCP
joint, and a VR application rendering force feedback for individual
fingers when manipulating a yo-yo.
As for future work, we believe there are synergies to be foundby combining the standard-EMS approach, which stimulates using
the forearm muscles, with our back-of-hand approach, especiallyfor the extensor muscles. Taken in combination, our technique andthe previous approach might unlock the next level of dexterity andprecision for EMS interfaces.

ACKNOWLEDGMENTS
Our research was supported by JSPS KAKENHI Grants (JP20J14306,
JP18H04110). This work is supported in part by NSF grant 2047189.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agencies.

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