SplitBody: Reducing Mental Workload while Multitasking via
Muscle Stimulation
Romain Nith

Yun HoPedro Lopes

University of Chicagornith@uchicago.edu

University of Chicago
cathorine.ho.photo@gmail.com

University of Chicago
pedrolopes@uchicago.edu

Figure 1: (a) This user finds themselves having to split their attention (dashed green arrow depicts their split attention)
between two sub-tasks, continuously stirring the pot to make caramel (a repetitive muscle movement) and writing an essay(a cognitively-demanding task)—multitasking is hard and even a simple repetitive muscle task detracts an untrained userfrom devoting more cognitive resources to the competing task. To explore this space, we (b) propose using electrical muscle
stimulation (EMS) to ”split” the user’s body and allow the EMS (lightning icon depicts the electrical stimulation) to automate
the simple & repetitive muscle movements while focusing on writing (depicted by the solid green arrow).

ABSTRACTCCS CONCEPTS

Techniques like electrical muscle stimulation (EMS) offer promise
in assisting physical tasks by automating movements, e.g., shaking a spray-can or tapping a button. However, existing actuation
systems improve the performance of a task that users are alreadyfocusing on (e.g., users are already focused on using the spraycan). Instead, we investigate whether these interactive-actuation
systems (e.g., EMS) offer any benefits if they automate a task that
happens in the background of the user’s focus. Thus, we exploredwhether automating a repetitive movement via EMS would reduce
mental workload while users perform parallel tasks (e.g., focusing
on writing an essay while EMS stirs a pot of soup). In our study,
participants performed a cognitively-demanding multitask aided
by EMS (SplitBody condition) or performed by themselves (baseline). We found that with SplitBody performance increased (35% on
both tasks, 18% on the non-EMS-automated task), physical-demanddecreased (31%), and mental-workload decreased (26%).

• Human-centered computing → Human computer interaction(HCI); Human-centered computing; User studies; Human-centeredcomputing; Haptic devices.

This work is licensed under a Creative Commons Attribution International 4.0 License.
CHI ’24, May 11–16, 2024, Honolulu, HI, USA© 2024 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0330-0/24/05
https://doi.org/10.1145/3613904.3642629

KEYWORDS
Haptics, Electrical Muscle Stimulation, Agency, Mental Workload,
Cognitive Load
ACM Reference Format:
Romain Nith, Yun Ho, and Pedro Lopes. 2024. SplitBody: Reducing Mental
Workload while Multitasking via Muscle Stimulation. In Proceedings of the
CHI Conference on Human Factors in Computing Systems (CHI ’24), May
11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3613904.3642629

1

INTRODUCTION

Techniques such as electrical muscle stimulation (EMS) offer
promise for assisting users with physical tasks. These interactive
systems do this by automating entire movements, e.g., shaking aspray-can [47], tapping a button on a touchscreen [29] or playing a
musical instrument [87]. However, it is key to note that the majority of these actuation systems improve the performance of a taskthat users are already focusing on (e.g., users are already focused onusing the spray-can in [47] or already attempting to press the button in [29]). In other words, body-actuation driven by interactive
systems (e.g., EMS) happens in the foreground of the user’s main

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

focus of attention, in which the interactive system assists the user
in completing the same task the user is also attending to.
Instead, we investigate whether these interactive actuation systems (e.g., EMS) offer any interactive benefits if they automate atask that the user is not attending to. In other words, we explore
a novel space in which the body actuation happens in the background, out of the user’s main focus of attention, enabling users
to potentially attend to another task in parallel—this would enable
new forms of physical multitasking.
However, much is unknown about body-actuating systems.
When it comes to EMS, only recently have researchers found that
actuating the user’s muscles can also decrease the user’s sense ofagency [29, 30, 85] or even distract users (e.g., EMS causes a tinglingthat can distract [39, 44, 65, 74, 89]). Thus, while actuation systems
can automate simple & repetitive gestures, it has not been studied
whether their limitations (e.g., tingling or loss of agency) mightprove detrimental to task performance by distracting users.
To shed light on this, we conducted a study where participants
performed a cognitively-demanding multitask, in which both theirhands performed parallel tasks: a repetitive movement task and acognitive task. They performed these tasks twice, once with the
movement task aided by EMS on one hand (a condition we call
SplitBody) and another entirely by themselves (baseline).
We found that with SplitBody, participants reported less physicaldemand (decrease of 31%) and less mental-demand (decrease of 26%)
than when performing the task by themselves. Moreover, the performance increased by 35% (averaged over both tasks), including thetask that was not automated by EMS, which increased by 18%. Thissuggested that, with SplitBody, participants were able to free-up
cognitive resources that they then allocated to this task. Moreover,accounts of their experience suggested they felt less overwhelmedsince they could just focus on one task—the non-automated cognitive task with SplitBody while not being distracted by the EMS
moving their arm involuntarily.

2

RELATED WORK

Our work builds on interactive systems based on EMS that assist
users by electrically actuating their limbs. While EMS is not the
only class of haptic actuators capable of displacing limbs involuntarily, we focus on it due to its inherent wearability when compared
to mechanical actuators [8–10, 18, 24, 57, 66]—EMS does not require heavy & cumbersome power supplies (e.g., batteries or air
compressors).

2.1

EMS as a means to move the body

Electrical muscle stimulation (EMS) has been used to actuatemuscles as a form of highly compact force-feedback. Many in
HCI have explored its ability to actuate limbs, including: fingers[2, 31, 60, 86, 87, 91], wrists [11, 19, 46, 77, 79, 92], arm [13, 47, 48],legs & ankles [13, 22, 45, 73], and even neck [88]—all in wearable
form factors. EMS has been used in a variety of tasks, from outdoor sports (e.g., golf [14] or running [22]), musical instruments(e.g., piano [61, 62, 86] or percussion [13]) to tool use (e.g., using aspray-can [47] or sketching [49]).

Romain Nith et al.

2.2

EMS is always in the foreground

EMS systems are typically used to aid users in tasks they are alreadyfocused on. For instance, in the seminal PossessedHand [87], EMS
plays the next musical note for a user who is already seated &playing this musical instrument. Similarly, in Affordance++ [47],
EMS shakes a spray-can that the user is already focused on using. As
such, designers & engineers tend to position EMS in the foregroundof the user’s attention by using the stimulation to tackle primarytasks. This approach proved successful in that it sparked new usagesof EMS for interactive contexts. Thus, it inspired us to propose a
new design position for EMS in the background, i.e., assisting theuser by automating repetitive tasks, and leaving the user’s attention
to focus more on more challenging concurrent tasks.
To the best of our knowledge, this has not been systematicallystudied. The idea that comes closest and from which we draw
inspiration is Pedestrian Cruise Control [73], an EMS system thatturns the user’s legs. While Pfeiffer et al. envisioned their systemgoing as far as to help users by steering them automatically, they
only tested it in a study where participants were not engaged in a
multitasking scenario; in fact, participants who were being guided
by the system to walk in a park were asked ”to pay attention to any
obstacles, (. . .) and to stop or circumvent these as necessary”; thus,
the EMS of [73], much like prior work, was acting in the foregroundof the user’s attention.

2.3

Mixed agency while interacting with”integrated” devices

There is an emerging body of literature exploring the concept of
mixed agency [55] between a user and their device [54, 55]. While
EMS provides one of the most extreme case-studies for this concept[11, 15, 29, 30, 33, 46, 51, 55, 81, 82, 85], others have also started to
discuss this for the case of force-feedback devices [12].
Central to the frameworks of thought in this area (e.g., humancomputer integration [55] or [12]) are two dimensions: sense ofbody-ownership (”this is my body”) and sense of agency (”this is
my action”) [12, 54]. In Mueller et al.’s taxonomy [54], most EMS
interfaces score low on agency since users do not initiate the actionthat the EMS carried out. Conversely, EMS interfaces score high inownership, since it is the user’s body that carries out the actions[54]. The authors also emphasize that if the EMS system does notcontrol the entire body, some agency remains [54]; this is also thecase in SplitBody, as our users were always in control of otherupper joints of the actuated extremity (e.g., they controlled theirshoulder while their hand was EMS-automated). Taken together,
this allows us to denote SplitBody as a type of EMS interface withhigh body-ownership but low agency.
Finally, it has been argued that these mixed agency devices introduce new design spaces that foster playful experiences [46, 54, 71],
creativity [11, 13, 94], and even new forms of productivity [69, 83],
to which we believe SplitBody contributes to with its multitasking
perspective (i.e., design spaces with concurrent physical tasks).

2.4

Evidence of lower performance in physicalmultitasking

It is well-known that multitasking comes at a performance costcompared to only focusing on a single task. Neuroscientists andSplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Figure 2: (a) In the baseline condition, the participants are performing a movement task and a n-back task by themselves. (b) In
the SplitBody condition, the participants are performing the same tasks, but the movement task is being automated by EMS(lightning icon). In both conditions, the movement task (c) involves repeating the following arm gesture sequence: up, down,
left, right, down, up, right, and left.
psychologists have correlated this cost to the limited cognitive
resources available [4, 43] and the operations the brain undertakes
when presented with a task: for each task, the brain has to ingest
the information, process it, make a decision, and respond withmovements. Therefore, when presented with concurrent tasks, thebrain uses different strategies [27, 70], such as processing one task
before moving to the next one (causing a bottleneck) [6] or reducing
the capacity to process tasks in parallel [59, 90]—both strategies
increase completion time [53, 76] and lower performance [67]. To
improve physical multitasking, researchers have looked at different
interfaces, such as adding haptics.

3

The goal of our study was to evaluate whether the use of aninteractive-actuation system (in this case, EMS) would reduce mental workload while users perform parallel tasks. To this end, we
designed a multitasking study in which participants were asked
to perform a multitask: aided by EMS (our SplitBody condition) or
performed by themselves (baseline). Our study was approved byour ethics review board (IRB21-1158).

3.1
2.5

Exploring haptics for mental workload
reduction

Adding haptics allows the conveying of additional information to
the user [1, 3, 26, 72]. Researchers have found that haptic cues canimprove the performance of single tasks [1, 58]. More recent studies
have also shown that similar results can be seen while multitasking,lowering mental workload. For example, Zhou et al. [93] showedthat surgeons using a haptic simulator performed better against
a no-haptics simulator while concurrently answering arithmetic
problems. Leung et al. [40] also found that adding haptic-feedback
to a touchscreen improved the response time when undergoing aconcurrent auditory-task, but no performance improvement was
found. Moreover, Haghighi et al. [20] investigated the ability to recognize haptic cues with vibration while performing a cognitive task
(1-back) and found that some parameters improved response time,
but none improved performance. These haptic systems provide
only haptic cues (e.g., vibrations or resistance) but do not move thebody. Thus, users still need to focus on executing the movements
required to perform all concurrent tasks.

USER STUDY: DOES EMS REDUCE MENTALWORKLOAD DURING PHYSICAL
MULTITASKING?

Hypotheses

Our main hypothesis (H1) was that the SplitBody’s ability to automate one of the tasks would result in a decreased mental workload,when compared to the baseline; as such, we utilized the NASA-TLX
questionnaire [21]. Our secondary hypothesis (H2) was that thisreduced mental workload (i.e., if H1 was true), we would observe
an increased task performance; as such, we measured task accuracy and response time. A corollary of the previous hypothesis
was that both performances of each sub-task should increase, i.e.,(H2.1) increased performance of the task automated by EMS (movement task); and (H2.2) increased performance of the voluntary task
(cognitive task).

3.2

Study procedure

Study design. Our multitask was based on two standardized designs: (1) a cognitive task—the n-back task [32], a popular cognitive load test [23, 25, 28, 68]; and (2) a movement task—a repetitive sequence of movement often used to analyze cognitive load
[41, 42, 64]; except participants were requested to perform these twotasks simultaneously—resulting in a challenging multitask, similar
to the one depicted in our Figure 1.

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Participants. We recruited 12 participants (six female and six
male; average age=27.4 years old; SD=8.0). No participant reported
any motor impairment. Participants were compensated with 20
USD for their time.
Conditions. Participants performed the multitask twice, once
per condition (condition order counterbalanced across participants),
as depicted in Figure 2. During baseline, participants performed
the multitask by themselves. During SplitBody, participants performed the cognitive task, while EMS performed the movementtask.
Cognitive task (dominant-hand). The objective of our cognitive task was to keep observing a sequence of letters on a screen,
shown one at a time, and respond if the current letter was previously
shown—n-back task [28]. We utilized an N=2, i.e., participants indicated if the current letter was shown two letters ago (2-back).
If it was a 2-back, participants were asked to press the left arrow.
Conversely, if it was not, press the right arrow. If the participants
failed to press a key or if two keys were pressed, their response
would be considered an error. A total of 32 letters were presented
for 500 ms each at 2500 ms intervals. Two predefined sequencesof letters were generated from the following eight visually distinct
letters: B, F, K, H, M, Q, R, X where each letter appeared exactly fourtimes in both sequences. Of the 30 responses (first two stimuli do
not have n-backs), 10 were n-backs, and 20 were not. Furthermore,
the difficulties of both sequences were equalized by featuring three
types of n-backs at equal numbers (here illustrated with A, B, C for
explanation purposes): ”A, B, A” was shown four times, ”A, B, A,
B” was shown twice and ”A, B, A, C, A” shown once per sequence.
Movement task (non-dominant hand). The goal during the
movement task was to perform a sequence of hand-gestures foras long as the trial lasted, as depicted in Figure 2 (c): up, down,
left, right, down, up, right, and left at a constant tempo of 50 BPM.
In the baseline condition, an audible metronome was heard (nometronome was used in the SplitBody condition, as EMS alreadyprovides a temporal cue). A complete sequence was consideredvalid if the participant’s hand performed all eight gestures in thecorrect order (i.e., each gesture was performed before the end of
the current beat, and two gestures were not performed within thesame beat).
Combined task design. Performing these two tasks simultaneously is challenging. Thus, we designed the combined task tostart incrementally, i.e., participants first started the movementtask (two complete rounds of the sequence), and only then did thecognitive task start. This was beneficial, especially for the baseline
condition, allowing participants to get ”a feel” for the movement
before adding the cognitive task.
Apparatus. Movements were tracked using a VIVE Tracker 3.0
attached to the hand. An additional RGB camera was used to record
trials and transcribe post-interviews. In the SplitBody condition, we
utilized a medical-grade muscle stimulator (HASOMED RehaMove3[75]). The stimulator interfaced with the n-back software, which
we implemented in Python via the RehaMove3’s library1 .
EMS parameters & calibration. We attached four pairs ofelectrodes to participants’ muscles at the: palmaris longus for wrist
1We provide all source-code needed to replicate our experiment in the supplementarymaterial and at https://lab.plopes.org/#SplitBody.

Romain Nith et al.

flexion (right gesture), carpi radialis longus for wrist extension (left),biceps (up), and triceps (down). Each participant was calibrated sothat the EMS parameters could robustly actuate each gesture. First,
we determined the stimulation intensity (i.e., current in mA) for
each muscle by starting at 0mA and a pulse-width of 300 𝜇s andincreasing by steps of 1 mA until a full & repeatable contraction
was observed while also being comfortable to the participant (nopain, cramps, etc.). The pulse frequency was fixed at 100Hz. This
process was repeated for all muscles following an anatomical guide.

3.3

Trial design & metrics

Warmup. Prior to the tasks, participants were introduced to EMS
by having their hands actuated at a constant speed of 30 BPM forthree minutes while, simultaneously, the experimenter explained
the n-back. After this explanation was completed, participants performed the multitask (once per condition, order counterbalanced).
Trial. Participants were asked to score as high as possible on
both tasks. A trial started with a visual countdown and ended when
the n-back letters finished. At the end of a trial, participants complete an unweighted NASA-TLX questionnaire. Then, participants
were invited to provide feedback on what they just experienced.
Performance metrics. (1) Movement task performance wasscored by the number of correctly performed sequences divided by
the maximum number possible during a trial, which was ten fullsequences. Moreover, we also recorded the response time of eachmovement according to the expected 50 BPM beats. (2) Cognitive
task performance (n-back) was scored by the correct number ofanswers over the total number of letters (30). Moreover, we also
recorded the response time. Finally, the NASA-TLX was scored byaveraging equally the six metrics (unweighted TLX)—the higher a
TLX score is, the higher the perceived workload was.

3.4

Results

Movement task performance. We analyze the movement task
performance. As our data did not follow a normal distribution using
the Shapiro-Wilk test, we conducted a Mann-Whitney U-test. We
found a significant difference (p<0.005) between movement task
performance of SplitBody (M=79%, SD=26%) and baseline (M=28%,
SD=21%). Results suggest that movement performance was

Figure 3: (a) Score with SplitBody and baseline, including a
breakdown for the (b) movement (gesture) and (c) cognitive(n-back) tasks.

SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Figure 4: The NASA Task Load Index score for both conditions.
increased 2.5x with SplitBody, as depicted in Figure 3 (b). Notethat this was expected since we calibrated the EMS to be robust
and it was automating the task. This result supports our H2.1 (i.e.,
increased performance of the EMS-automated task).
Cognitive task performance. Most relevant to our H1, we
analyze the cognitive task performance. As our data did not follow
a normal distribution using the Shapiro-Wilk test, we conducted
a Mann-Whitney U-test. We found significant difference (p<0.05)
between cognitive task performance of SplitBody (M=78%, SD=19%)
and baseline (M=60%, SD=23%). These results suggest that cognitive task performance was increased by 1.3x with SplitBody,
as depicted in Figure 3 (c). Average wrong-answers decreased by
1.6x with SplitBody (M=13%, SD=6%) compared to baseline (M=21%,
SD=9%). Similarly, average no-answers (failed to press either key
as a response, potentially caused by mental overload) decreased by
2x with SplitBody (M=9%, SD=19%) compared to baseline (M=19%,
SD=24%)—These results are highly supportive of our H1, this task
was performed by participants unassisted (no EMS), suggesting thatthe gain was due to the SplitBody’s assistance of the backgroundtask. This also further supports our H2.2 (i.e., increased performance of non-automated). Critically, this increase in performanceon the cognitive task shows that despite EMS’ shortcomings (e.g.,
EMS can distract with its tingling sensation [39, 44, 65, 74, 89]), it
provides a benefit when automating another demanding task.
Overall performance. We analyze the overall multitasking performance, i.e., the average of both tasks. As our data followed anormal distribution using the Shapiro-Wilk test, we conducted a
two-tailed paired t-test. We found a significant difference (t(11)=5.6,
p<0.0005) between the multitask performance of SplitBody (M=78%,
SD=13%) and baseline (M=44%, SD=17%). Results suggest that multitasking performance was almost doubled with the SplitBody, as depicted in Figure 3 (a).
Workload (NASA TLX). Figure 4 depicts our results fromthe NASA-TLX unweighted questionnaire (higher values indicate
higher perceived workload). All comparisons below are Bonferroni
corrected (𝛼 adjusted = 0.0083).
First, we analyzed workload data, which followed a normal distribution (Shapiro-Wilk test), with a two-tailed paired t-test. We
found a significant difference (t(11)=5.7, p<0.0005) between theworkload with SplitBody (M=52%, SD=11%) and baseline (M=78%,

SD=14%)—suggesting that workload was 1.5x lower with SplitBody. Second, we analyzed mental-demand data, which did not
follow a normal distribution (Shapiro-Wilk), with a Mann-Whitney
U-test. We found significant difference (p<0.005) between mentaldemand with SplitBody (M=63%, SD=4%) and baseline (M=89%,
SD=2%)—suggesting that mental-demand was 1.4x lower with
SplitBody. Third, we analyzed physical-demand data, which followed a normal distribution (Shapiro-Wilk), with a two-tailed paired
t-test. We found a significant difference (t(11)=3.2, p<0.008) between the physical-demand with SplitBody (M=23%, SD=4%) andbaseline (M=54%, SD=6%)—suggesting that physical-demand was
2.3x lower with SplitBody. Next, in a similar fashion, we conducted statistical analyses for the remaining NASA-TLX metrics,
all found to be significantly different: (1) temporal-demand using a Mann-Whitney U-test (p=0.0081, not normal distribution
via Shapiro-Wilk); (2) perceived performance via Mann-Whitney(p<0.005, not normal distribution via Shapiro-Wilk); (3) perceived
effort via two-tail paired t-test (t(11)=6.6, p<0.0005, normal distribution via Shapiro-Wilk); (4) perceived frustration via two-tail paired
t-test (t(11)=6.1, p<0.0005, normal distribution via Shapiro-Wilk).
Overall, these results indicate a decreased perceived workload
using SplitBody.
Response time. Figure 5 depicts participants’ response time,
which, as we will analyze, we found to be statistically faster with
SplitBody than with the baseline condition.
Both movement and n-back response time data followed a normal distribution via Shapiro-Wilk. Using a two-tail paired t-test, weFigure 5: Response time results: (a) movement task and (b)cognitive (n-back) task.

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

found both tasks to be statistically different: (1) movement responsetime (t(11)=5.2, p<0.0005) with SplitBody (M=131ms, SD=82.8) compared to baseline (M=312ms, SD=59.0), and (2) n-back response time(t(11)=3.3, p<0.01) with SplitBody (M=0.93s, SD=0.20) compared tobaseline (M=1.17s, SD=0.24). Results suggest that, as expected, participants were 2.3x faster at movements with SplitBody but, more
importantly, 1.2x faster at answering the n-back task with
SplitBody.

3.5

Participants’ experiences

Perceived improvement. Ten (out of 12) participants expressedthat they perceived performing better with SplitBody (P1, P2, P4,
P5, P6, P7, P8, P9, P10, P12). For instance, P12 stated, ”with [SplitBody] I did not have to think too much about the direction ofmoving the left hand, so I could focus on the [n-back] task”. Of
the two participants who did not mention a perceived improvement with SplitBody, P11 stated ”[I did] a little better with no EMS(. . .) but pretty close [on both conditions] for the computer task”,
and P3 stated that they ”performed better on the movement task
without EMS but did better in the [n-back] with EMS.” These areperceived scores, not their scores; in fact, P3 performed marginallybetter with SplitBody (2%) and P11 performed 40% better with
SplitBody.
EMS distractions. Overall, seven participants specifically statedthey did not find EMS distracting (P1, P2, P4, P5, P6, P7, P12),
while five still found it distracting (P3, P8, P9, P10, P11). Of theparticipants who stated not to find EMS distracting, P2 noted ”I
wasn’t even focused on it (. . .) since I was focused on the [n-back]”,
while P6 stated ”the EMS intensity was just enough that I could
put it in the background.” On the other hand, three participants(P8, P9, P10) mentioned that the EMS was, at times, too strong anddistracting. To this end, P10 stated ”I felt like the pulse was strong,and I would forget the letter I was on, but for the most part, it wasfine.”
Trust in EMS’ performance. All participants, except P6, statedthey trusted the EMS automation to perform the correct pattern.
They expressed either forgetting about it, such as P9 stating ”I did
not think about it at all. I did not even think about if it was doing
the wrong pattern”, or feeling that it was correct, as P10 stated,”I did not question it (. . .) they seemed correct”. P6 stated being
skeptical at times, saying, ”I was trying to optimize the letter task.
There were times I was doubting [the EMS movements]; it feels
like at times, I think it wasn’t going in the right direction of thesequence, but I was also not focused on it, so it was hard to keep
track”.

3.6

Discussion & study limitations

Summary of findings. Taken altogether, our results (i.e., reduced
overall workload and mental-demand) support our main hypothesis (H1, i.e., our SplitBody condition’s ability to automate one of
the tasks would result in a decreased mental workload). We also
observed increased performance & faster response time, supporting
our second hypothesis (H2, i.e., reduced mental workload improves
task performance).
Study limitations. Our study is not without its limitations.
First, we focused on creating a challenging multitask and thus

Romain Nith et al.

resorted to one that combines muscle movements and cognitive
operations (e.g., short-term memory). Yet, this is only one of many
possible physical multitasking situations that users encounter, so
we advise to be mindful in extrapolating our results to tasks thatare fundamentally different. Secondly, the ability of the EMS actuation to assist users with complex movements is limited by thecapability of EMS research (e.g., many explore how to make it more
precise [2, 7, 16, 31, 34, 37, 46, 63, 86, 91]. In fact, our observations lead us to believe that there are per-participant differencesin how well participants let the EMS move their hands (e.g., wenoticed that three participants tensed up their non-dominant handas they are not used to the feeling of having their limb move involuntarily, and hence decreasing the quality and precision of each
EMS stimulate). This points to an open challenge in EMS researchin optimizing how natural these actuated movements feel to the
user.

4

ENVISIONED APPLICATIONS

We illustrate the use of SplitBody in four envisioned applications, inwhich our system assists by performing repetitive background tasks:(1) writing while cooking; (2) drawing while coloring; (3) soloing
on the snare-drum while playing a backbeat; and, (4) playing an
instrument while being accompanied by another.
We designed these applications to highlight scenarios that are
not meant to be automated by a machine, but instead, whereusers seek to be physically engaged in the task, either for thesake of creativity (e.g., drawing), learning (e.g., playing music),or pleasure (e.g., cooking). These were chosen to convey how
SplitBody can open up a new design space for interactive EMS
systems.
Also, these examples were designed by taking into account the
precision of existing EMS systems. In fact, the interactions depictedwere designed conservatively with respect to the accuracy alreadypossible with EMS.

4.1 Split-chef: making caramel while writing an
essay
In our first envisioned application, a user multitasks by preparing
caramel while writing an essay. Making caramel demands constantstirring and monitoring to prevent burning. Our user taps on their
EMS device, activating a pre-programmed stirring motion with
SplitBody. This allows them to divert their focus to writing, asshown in Figure 6 (a). Once they feel the sugar is consistently
melted, as their sense of proprioception lets them feel the change inviscosity while stirring (even though this hand is EMS-controlled,
proprioception is never off [46]), they suspend writing to switch
their focus to add butter into the pot, finalizing the mixture, as
depicted in Figure 6 (b). Subsequently, they switch back mostof their focus to writing as the EMS continues stirring the added
butter. Upon achieving the desired caramel consistency, they tap thestimulator to stop SplitBody. While this application is envisioned
due to its simplicity (e.g., open-loop-EMS, no-tracking), the user inFigure 6 (c) was indeed successful at cooking/writing with SplitBody
while avoiding burning the caramel (this caramel was taken home
for their enjoyment).

SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Figure 6: (a) A user is multi-tasking by making caramel with SplitBody on their left arm (lightning icon) while they are writing
an essay with the other arm (solid green arrow depicts their main attention). By feeling the consistency of the melted sugar in
the pot, (b) they switch their attention back to the cooking caramel (solid green arrow depicts the switch of their main focus)
and butter to the mix while their left arm is still automated by EMS (lightning marker). Finally, (c) they stop SplitBody bytaping on the stimulator.

Figure 7: A user drawing (a) simple shadows and (b) coloring with SplitBody on their non-dominant hand (lightning icon) while
continuing to draw with their dominant hand.

Technical feasibility. The EMS movement used in this application was inspired by Kaul et al.’s [31], which demonstrated that
EMS can actuate a user’s arm in a circular motion with an average
error of 17.8mm—this system’s implementation & accuracy would
be sufficient to realize our proposed stirring gesture.

next shape, while the coloring process continues as a backgroundtask.
Technical feasibility. The EMS used is similar to Muscleplotter’s [49], which achieves a drawing accuracy of ±4.07mm—thissystem’s implementation & accuracy would be sufficient to realize
our proposed drawing gestures.

4.2

4.3

Split-draw: enabling synchronous shadow
drawing and coloring while sketching

In this envisioned application (inspired by the Split Body artworkof Stelarc [84], to which our system’s name is an homage), we
explored drawing with SplitBody: (a) shadow-drawing and (b) coloring. While these are envisioned explorations, it is possible to
track & actuate a user’s drawing with an EMS system similar to
Muscle-Plotter [49]. First, Figure 7 (a) depicts a user drawing ahouse under a sun. Because the user drew a sun, SplitBody actuates
the user’s non-dominant arm to simultaneously draw the shadows
cast by the house without needing to shift all of their attention.
Second, Figure 7 (b) shows SplitBody coloring inside a shape thatthe user just finished drawing, allowing the user to move to the

Split-drum: learning to drum one limb at atime

In this envisioned application, we depict a novice drummer playing
a full drum set without, yet, being able to multitask on each drum
kit’s part (a hard skill when learning drums, referred to as limb
independence [95]). Figure 8 depicts our user choosing to have
SplitBody automate the backbone of a funk drumbeat (i.e., EMS
plays the bass & hi-hat) while, voluntarily, the user focuses moreof their attention on playing the snare at the correct timings.
Technical feasibility. The EMS used for drumming is inspiredby Ebisu et al.’s [13], which demonstrated EMS’ ability to playrhythms with both hands (using more complex beats than ours).
Also, EMS lends itself well to timing-based applications, such as the

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Romain Nith et al.

of their attention to playing a more advanced rudiment pattern on
the drum.
Technical feasibility. The EMS used to move individual fingers in this application is based on Takahashi et al.’s [86], which
demonstrated that EMS can actuate all four fingers with an index
of independence of 0.62—their approach’s accuracy is sufficient toplay single keys on the synthesizer as we depict in this application.

5

Figure 8: A novice drummer only focusing on playing thesnare drum while SplitBody automates the hi-hat and bass
(lightning icon).

envisioned drumming, due to its fast actuation speed (e.g., 40ms
for [29]).

4.4

Split-musician: alternating foreground &background musical tasks

Finally, we explored the concept where users alternate between
which task is automated with SplitBody and which task is performed
voluntarily. Figure 9 (a) shows our user soloing on the synthesizer
while letting SplitBody play the drum. Then, as depicted in Figure 9(b), our user decides to swap these around by pressing a footswitch,which causes SplitBody to, in the background, play simple threenote arpeggios on the synthesizer, while the user redirects more

DISCUSSION

Safety & Ethics. We believe that any interactive system withthe capability of moving the body must be ethically designed bygrounding it in the principles of user-agency & safety. This is precisely the case for SplitBody. First, while our explorations wereentirely lab-based, in all these situations, our users were given fullcontrol of when to activate SplitBody’s EMS (e.g., pressing a button while cooking, pressing a footswitch while drawing, etc). In
other words, SplitBody does not include automatic triggers thatinvoke EMS assistance, only user-defined triggers. This mechanism
further implements a simple way for users to turn off the EMS
assistance. Importantly, SplitBody only actuates a subset of muscles(e.g., forearm & wrist muscles while cooking, wrist & calf muscles
when drumming, etc), always leaving most of the user’s limbs nonactuated and completely under the user’s voluntary control—thisallows the user to turn off the assistance when desired. Secondly, aswith other interactive systems based on EMS, we believe that fully
realizing any of SplitBody’s applications outside of a research environment must include features that provide agency to the user, suchas: automatically halting any EMS when the user moves against thestimulated movement (e.g., as used in [47]), providing user-defined
gestures that immediately suspend the stimulation (e.g., as used in[46]), or only enabling the stimulation in user-defined areas (e.g.,
as used in [47] or [49]). Moreover, all our experiments followed
the established EMS guidelines [36, 65, 80], were approved by our
ethics review board, and conducted with the informed consent of
all participants.

Figure 9: A user switching between which task is automated with SplitBody (lightning icon) and which task is performed
voluntarily.

SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation

Conceptual differences to external automation. Unlike devices that automate background tasks using machinery (e.g., robots)that are external to the user, interactive actuation systems (e.g., EMS)
act on the user’s body. While EMS-actuated movements feel lessagentic than one’s own voluntary movements [29, 30, 85], users
are still involved as they feel their body moving via their sense of
proprioception/touch [46, 89]. This distinction is key to our concept, which focuses on interactive contexts where utility is not
the user’s sole objective (e.g., unlike tasks viewed as chores suchas vacuuming by hand), and, instead, the user’s goals involve not
only utility but also body-ownership [12, 54]. As such, we focusedon situations where users want to automate repetitive gestures
but also want to be bodily-engaged with the tasks—for the sake of
their creativity, learning, pleasure, or even for a sense of ownershipover the outcome [12, 54, 55, 85]—rather than letting an external
machine perform the background task for them, which offers nosense of involvement due to the ”full automation” [5, 52]. Naturally, we acknowledge there are many scenarios where backgroundautomation is beneficial using external devices and where users
might feel no desire to be bodily engaged (e.g., vacuuming robots),which were not the focus of our investigation.

6

CONCLUSIONS & FUTURE WORK

We proposed and evaluated a novel concept (SplitBody) that uses
electrical muscle stimulation to assist users in movement tasks that
happen in the background while the user is focusing on another task
in the foreground. We found that participants assisted with SplitBody
were able to perform better on a physically demanding multitask.
Inspired by these findings, we envisioned a set of applications toillustrate the design space that SplitBody opens.
Future renditions. While EMS is a highly portable actuatorcapable of moving the body, other force-feedback actuators (e.g.,
exoskeletons [8, 18, 38, 50, 66], artificial-muscles [17]) may exhibit
more precision at the expense of their larger form-factor. As ourconcept hopes to one day integrate with everyday interactions, it
was important for us to choose a small & portable device. That being
said, we expect that similar benefits can be found using mechanicaldevices, and we hope that our work inspires future work in that
unexplored direction.
Future integration with supernumerary limbs or VR. We
believe that some of the advantages of SplitBody might be integrated with supernumerary-limb interfaces [56, 78], such as thedecreased mental workload or the ability to alternate between automated/voluntary tasks. Similarly, researchers started to explore
how users simultaneously control two VR avatars [35] (also a typeof SplitBody, but for input); we believe that our SplitBody mightprovide useful haptic feedback so that these VR users can synchronize their body pose with their virtual avatars prior to initiating
control.

ACKNOWLEDGMENTS
We would like to thank Prof. Peggy Mason for insightful discussions
on this topic. This work was supported by NSF grant 2047189 and2212352. Any opinions, findings, conclusions, or recommendations
expressed in this material are those of the authors and do notnecessarily reflect the views of any funding agencies.

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

SPECIAL ACKNOWLEDGMENTS
We would like to acknowledge that the ACM CHI 2024 conference
has decided to take place in Hawaii, offering extremely limited
options for remote participation. While some authors have chosen
to present their work in person or bring its physical prototype to
the CHI community, they have done so with compassion towards
Native Hawaiians and with a heavy heart. We urge that the ACM’s
process for conference selection should more carefully consider the
impact our conferences have on local communities. We especiallywant to acknowledge Prof. Josiah Hester, a Kanaka Maoli Professor
in Computing, who organized comprehensive resources that educated us about the negative impacts of over-tourism and climate
degradation in Hawaii2 . Finally, the decision of some authors toparticipate in this conference does not represent the views of othermembers of our lab, who have chosen not to engage with this edition of the CHI conference due to its impact on Native Hawaiians.

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