User Authentication via Electrical Muscle Stimulation
Yuxin Chen

Zhuolin Yang

Ruben Abbou

yxchen@cs.uchicago.edu

zhuoliny@cs.uchicago.edu

rabbou@cs.uchicago.edu

University of Chicago
Chicago, IL, USA

University of Chicago
Chicago, IL, USA

University of Chicago
Chicago, IL, USA

Pedro Lopes

Ben Y. Zhao

Haitao Zheng

pedrolopes@cs.uchicago.edu

ravenben@cs.uchicago.edu

htzheng@cs.uchicago.edu

University of Chicago
Chicago, IL, USA

University of Chicago
Chicago, IL, USA

University of Chicago
Chicago, IL, USA

a to verify whether
the user is Mary,
our system
stimulates her
fingers withan EMS pattern...
duration=1.2s

c the response isthen verified
as Mary

VR hand
6 b01#&$(2#tracking!,$*3)(4#
records how,'*",2-#&"5#
the fingers!&'#,$-.(+"
respond#5)
to+("35-4
this EMS#*$")012)
pattern
3!##(+-

4 d welcome to your
VR room, Mary!

MaryothersFigure 1: We propose a novel modality for authentication: electrical muscle stimulation (EMS). To explore it, we created aninteractive system that (a) stimulates the user’s forearm muscles with electrical impulses (i.e., using one of 68M possible
EMS challenges); (b) measures the user’s involuntary finger movements, which are unique because everybody’s physiology
is different; (c) verifies this response using an authentication model, and immediately eliminates this challenge, making oursystem secure against data breaches and replay attacks as it never reuses the same challenge. We demonstrate it here using
the example of (d) authenticating a VR user without passwords or PINs.

ABSTRACT
We propose a novel modality for active biometric authentication:
electrical muscle stimulation (EMS). To explore this, we engineered
an interactive system, which we call ElectricAuth, that stimulates
the user’s forearm muscles with a sequence of electrical impulses(i.e., EMS challenge) and measures the user’s involuntary finger
movements (i.e., response to the challenge). ElectricAuth leverages
EMS’s intersubject variability, where the same electrical stimulation
results in different movements in different users because everybody’s physiology is unique (e.g., differences in bone and muscular
structure, skin resistance and composition, etc.). As such, ElectricAuth allows users to login without memorizing passwords or PINs.
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CHI ’21, May 8ś13, 2021, Yokohama, Japan© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8096-6/21/05. . . $15.00

https://doi.org/10.1145/3411764.3445441

ElectricAuth’s challenge-response structure makes it secure against
data breaches and replay attacks, a major vulnerability facing today’s biometrics such as facial recognition and fingerprints. Furthermore, ElectricAuth never reuses the same challenge twice inauthentications ś in just one second of stimulation it encodes oneof 68M possible challenges. In our user studies, we found that ElectricAuth resists: (1) impersonation attacks (false acceptance rate:
0.17% at 5% false rejection rate); (2) replay attacks (false acceptance
rate: 0.00% at 5% false rejection rate); and, (3) synthesis attacks(false acceptance rates: 0.2-2.5%). Our longitudinal study also showsthat ElectricAuth produces consistent results over time and acrossdifferent humidity and muscle conditions.

CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Haptic devices; • Security and privacy → Authentication; Biometrics.

KEYWORDS
electrical muscle stimulation, biometric authentication, wearable

CHI ’21, May 8–13, 2021, Yokohama, Japan

1

INTRODUCTION

Biometric authentication is a technique that identifies an individual
by their unique biological characteristics, such as their iris [85],fingerprints [51], or even one’s voice [8]. To identify their users,
these interactive systems compare a previously stored biometric
key to incoming, typically real-time, biometric data of the user
wishing to authenticate. Compared to traditional password or PIN
based systems, biometric authentication offers significantly better
usability as it does not require users to memorize passwords or
PINs. As such, biometric authentication is getting widely adopted,
replacing passwords in many contexts [74].
However, the key feature of biometric authentication is typicallyalso its key flaw: once the biometric data is compromised (e.g.,stolen in database breaches or recorded by an external attacker),there is nothing the user can do to securely re-use their own data.
For example, if someone steals a user’s fingerprints, this user cannever trust a fingerprint-based interactive system. Unfortunately,
these threats are not theoretical and many biometric systems havebeen breached. For instance, the biggest known biometric data
breach involved a database of 27.8M records, including fingerprints
and faces [30].
To tackle this shortcoming, researchers turned to interactive systems that feature a challenge-response as a form of active biometricauthentication. One example is Velody [40], which challenges a user
by vibrating her palm and measuring the user’s unique vibrationresponse. The advantage of these systems is that, if the stored
challenge-response pairs are breached, the system can quickly recover by simply asking the user to submit responses to a new set ofchallenges. As such, researchers seek to find more modalities thatafford challenge-response biometric authentication.
In this paper, we propose and explore a novel modality for active
biometrics: electrical muscle stimulation (EMS). To understand andevaluate the potential of EMS as a biometrics system for interactive
applications, we engineered a prototype that performs user authentication via EMS. Our system, which we call ElectricAuth, stimulates
the wearer’s forearm muscles with an EMS-based challenge, i.e.,
a 1.2s long sequence of electrical impulses on four of the user’s
muscles. Then, it measures the user’s involuntary movements that
result from this EMS challenge. In Figure 1, we illustrate our systemwith the example of authenticating a user in VR. Here, ElectricAuth
uses the VR headset’s hand tracking to observe the response of
the user’s muscles to the EMS-challenge as their individual finger
muscles are actuated.
ElectricAuth makes three key contributions in the design of
EMS-based biometric authentication.
First, ElectricAuth authenticates users by leveraging what is
typically seen as the biggest disadvantage of EMS: intersubject
variability, i.e., the same electrical stimulation results in different
movements in different users because everybody’s physiology isdifferent [11, 14, 17, 36, 53]. This unique response to EMS acrossusers is well-known and well-documented in the early HCI works
that pioneered the use of EMS in interactive devices, for instance:"(..) stimulation level differed between users and was clearly dependent on the muscle and fat level and thickness of the arm" (fromKruijff et al. [39]) and, similarly, "(...) levels according to individual
variations" (from PossessedHand [79]). In fact, researchers in the

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng

field of muscle-biomechanics and physiology demonstrated how
this uniqueness arises from multiple factors, such as differencesin muscle contractility [23], muscle elasticity [82], muscle viscosity [13], the limb’s mass and shape [55], skin conductance [41],bioimpedance [12, 70] and even nerve conduction [1]. All these differences add up to create individual responses to the same stimulus,
which our system uses as the key feature to authenticate a user.
Second, ElectricAuth generates a very large pool of challenges
by exploring an underutilized property of EMS: muscles responddifferently depending on their current state of contraction, which
can be altered by varying the timing between two impulses. Using four muscles, six impulses and seven time gaps, ElectricAuth
encodes one of 68M possible challenges in 1.2s. As such, ElectricAuth is robust against data breaches and replay attacks because it
never reuses the same challenge twice in authentications ś ElectricAuth rejects replay of recorded responses to any previously
used challenges, and can quickly recover from leak/breach of either
authentication model or stored challenge-response pairs by asking the user to register responses to a new set of challenges (likeregistering new one-time passwords).
Finally, we evaluated our prototype of ElectricAuth by means offour different evaluations, each shining light on a different facet ofour research question: (1) in our user studies, we found that ElectricAuth offers accurate user verification and resists three common
biometric attacks: impersonation, replay and synthesis attacks; (2)in our exploratory longitudinal study, we found that ElectricAuth’s
pre-trained authentication model performed stably over 21 days
against various muscle conditions (fatigue, humidity, etc.) that wereabsent from the training data; (3) in our technical evaluation we
showed that ElectricAuth, after receiving a response, can verify theuser in 3ms on laptop’s CPU and 35ms on a small embedded device;
we also confirmed the use of depth camera as an alternative motion
tracking modality (since our prototype uses IMUs); and, (4) we
generated synthetic impersonator responses to test ElectricAuth’s
robustness against impersonation attacks at scales larger than ouruser studies.

2

RELATED WORKThe work presented in this paper builds on the fields of wearables,
electrical muscle stimulation, and biometrics.

2.1

Electrical muscle stimulation

Electrical muscle stimulation (EMS) is a technique from medical rehabilitation [76] that induces involuntary movements by delivering
electrical impulses to the user’s muscles. This is typically achieved
by non-invasive methods such as attaching pairs of electrodes tothe user’s skin (e.g., on top of the muscles that control finger movement, located in the forearm). Electrode pairs are typically driven
using safe and medical compliant muscle stimulators [37].
The range of motion of an induced muscle contraction depends
on several key factors. Even in the very first interactive use of
EMS in HCI, by Kruijff et al. [39] in 2006, the potential causes of
EMS’ intersubject-variability were discussed: "(..) stimulation level
differed between users and was clearly dependent on the muscle

User Authentication via Electrical Muscle Stimulationand fat level and thickness of the arm (...)". Similarly, in PossessedHand [79], Tamaki et al. also found "(...) stimulation levels according to individual variations". In fact, researchers in the fields ofmuscle-biomechanics and physiology have been investigating precisely which factors drive a muscle’s unique response to electricalimpulses, including: the location of the electrodes [68, 79]; the electrical waveform characteristics, such as frequency and amplitudeof the impulses [39, 68, 79]; the target muscle’s contractility [23],i.e., the ability of muscle fibers to shorten; muscle elasticity [26, 82],
i.e., the ability of the elastic tissue present in the muscle fibers toreturn to its original length when a tensile force is removed; muscle viscosity [13], i.e., the internal bio-lubrication of the muscle
inhibits the muscle from reacting too quickly to protect against
stretch injuries; the limb’s mass and shape [10, 11, 14, 17, 55]; skinconductance affects non-constant current EMS devices [39, 41],
bioimpedance [12, 70]; and, even nerve conduction [1, 75], i.e., thespeed of nerve signal transmission. However, it is not possible
to precisely determine how much each factor weighs in the final
variability, as these are tied together in complex non-linear ways,
and this is still an open research question in muscle physiology.
More importantly, all the aforementioned factors are relevant toour proposed technique since these vary-across users. Typically, acombination of these explains the intersubject variability seen in
EMS-based interactive systems, which is why researchers report
long periods of calibration [44, 47, 77, 79] and even specificallymention differences across users [39, 79].
Recently, researchers started to engineer interactive devices
based on EMS. These tend to fall into two broad categories: (1)haptic devices that increase immersion/realism of virtual environments, and (2) interactive devices that facilitate information accessvia proprioception. As far as interactive devices that increase immersion, EMS has been used to render forces in mobile devices [43],virtual reality [44, 47] or augmented reality [20, 48]. As a meansof general information access, EMS has been especially used for
haptic training (e.g., learning a musical instrument [79], operating atool the user is not familiar with [46]) or eyes-free communication(e.g., communicating walking directions via leg stimulation [77],communicating a state of a variable via wrist movements [45]).
Unlike these interactive systems that use EMS as a form of force
feedback or as an information channel, we explore EMS in a new
direction: leveraging user’s unique muscular responses to EMS as aform of active biometric authentication.

2.2

Biometric authentication

Biometric authentication verifies an individual by their uniquebiological characteristics. To verify a user’s identity, a biometricauthentication system compares a previously stored biometric key
from a particular user to incoming, typically real-time, biometricdata of the user wishing to authenticate. Compared to traditional
password or PIN based methods, biometric authentication offers
significantly better usability by not requiring the user to memorize
passwords or PINs.
Existing biometric systems can be categorized into two types:
passive and active biometrics.
Passive biometrics. Passive biometrics rely on physiologicalcharacteristics that naturally occur in users, which can be either

CHI ’21, May 8–13, 2021, Yokohama, Japanstatic or dynamic. Static data, e.g., fingerprints [27], handprints [22],facial and eye features [2, 51, 59, 85], is often used for authentication. Biometrics based on dynamic data recognize patterns that varyover time, e.g., heartbeats [31], gait [78], mouse movements [32],keystrokes [80], speech features [4], body movements [54, 62],
pulse-response [67] and bioimpedance [28, 70]. Compared to static
data, these display greater complexity and are harder to model.
Passive biometrics are vulnerable to data thefts and replay attacks
as reported by numerous incidents and studies [6, 18, 35, 57, 86ś88].
This is because the identity (also known as "key") associated witheach user is physically "hard-coded" and then used repeatedly for
all authentications. Thus after a key has been compromised (e.g.,stolen from a database), an adversary can bypass authentication
until the key is replaced. Finally, there is a small number of available
biological traits per user that act as suitable keys, e.g., once all ten
fingerprints are compromised, this user can never again rely on
fingerprint authentication.
Active biometrics via challenge-response. Active biometrics
leverage a user’s physiological response to a given stimulus (alsoknown as "challenge") injected by the interactive device. The assumption is that each user’s response to a given challenge is unique.
Thus, each challenge-response is effectively a biometric password.
Examples of challenge-response biometrics include leveraging: thepalm’s response to vibrations [40], reflexive eye behaviors in response to visual stimuli [73], or even EEG responses [42]. Thesesystems authenticate implicitly so the user does not need to consciously follow the challenge, e.g., the palm vibrates and the user isauthenticated [40].
Compared to passive biometrics, active systems are more robust
against data thefts and replay attacks. This is because each user canpotentially generate many challenges, each triggering a different
response. The system uses a new challenge in each authentication session, preventing attackers from using previously observed
responses to breach it.
Lastly, while many challenge-response authentication systems
leverage the user’s movement (e.g., gaze [66] or wrist shakes [60]),
these require explicit action from the user. Unlike these, our novel
exploration of EMS-based authentication provides the advantagesof movement-based challenge-response while automatically delivering the challenge and eliciting the user’s involuntary response.

3

IMPLEMENTATION

To help readers replicate our design, we provide the necessary
technical details. Furthermore, to accelerate replication, we provide
source code and training scripts1 . Here, we describe in detail the
prototype we implemented for our user studies, which is based onsensing the user’s movements using inertial measurement units(IMUs). However, this is just one possible configuration for ourconcept. As depicted in Figure 1, other tracking systems, such as
optical tracking [56, 84], are likely feasible alternatives.

3.1

System Overview

ElectricAuth consists of three components: (1) a medically-compliant
EMS device that delivers EMS challenges to the user, (2) a motion
sensor that captures the actuated limb’s movements, such as IMUs
1 http://sandlab.cs.uchicago.edu/electricauth

CHI ’21, May 8–13, 2021, Yokohama, JapanIMU sensors

electrodes

Figure 2: IMU-based version of our EMS authentication system, which we used for our user studies.

make all impulses uniform to shine light in the fact that intersubject
variability in EMS arises from factors external to EMS waveformcharacteristics.
Our EMS challenges are constructed by sequencing these standardized pulses to one of the four channels the user’s forearm isconnected to. For instance, one can construct a challenge with asequence of six impulses, each followed by a resting period. We
detail the engineering of our pulse sequences in Section 3.2.
2. Motion sensing.
We utilized a set of five 9-DOF inertial measurement units, attachedto the fingers via a 3D printed ring (NXP Precision 9DoF, comprisedof the FXOS8700 3-Axis accelerometer and magnetometer and the
FXAS21002 3-axis gyroscope). These sample the fingers’ acceleration and rotation at 50Hz (post-sample interpolated to 100Hz) witha precision of ±4g at 14-bit for acceleration and ±250°/s at 16-bit
for rotations; note that we do not use the magnetometer. These
IMUs are sampled by a ATSAMD21G18 ARM Cortex M0 48 MHz
processor, via a TCA9548A I2C Multiplexer. Finally, our sensing
board relays the IMU data via serial over USB to our software.
While attaching IMUs to each finger has been shown to be a reliable way to capture hand pose [15, 29], we believe many alternative
tracking systems are possible to realize EMS-based authentication,such as depth cameras [72, 84], RGB cameras [9, 71], and others [34].
We provide a short evaluation that confirmed the use of depth cameras as an alternative tracking system in Section 9.
a registrationb authentication
EMS challenges#1#2(i) send a randomchallenge anddelete it immediately...

(1) send all
challenges#n(2) capture
responses(ii) capture
response

motion responses
real time response

#1
#2

...

or depth cameras, and (3) a trained machine learning modelthat classifies the user’s movements and performs authentication.
Figure 2 depicts one concrete implementation of our system using
EMS and IMUs attached to a user’s forearm, which we used for ouruser studies.
1. EMS hardware.
EMS stimulator: For delivering EMS impulses we use the Hasomed Rehastim, a medical compliant device with eight individuallycontrollable channels. This device has often been used in interactive
systems based on EMS [47ś49]. To control the EMS stimulation,
our software sends serial commands via USB using the Hasomed’s
Science Protocol [24]. These impulses have a latency of <1ms.
Customized EMS sleeve: As with any device based on EMS,
we start by calibrating the electrode placement for each user at her
registration session. Our calibration aims at targeting four muscles
on the user’s forearm that actuate finger and wrist rotation. At theanterior forearm we stimulate two muscle groups: (1) primarily the
flexor carpi radialis and partially the flexor digitorum profundus;
and, (2) the flexor pollicus longus. At the posterior forearm westimulate two muscle groups: (1) primarily the extensor digitorum
and partially the extensor digiti minimi, extensor pollicis brevis &longus; and, (2) the extensor indicis. As is typical with EMS-based
systems, these electrode positions are adjusted for each user during
the registration session to ensure comfort. Because each user has adifferent muscular anatomy and body shape, the resulting electrodelocations are different across different users.
After calibration, the resulting electrode layout for a particular user is fixed by making an EMS-electrode sleeve (fabric withelectrodes stitched to it) that this user wears any time they use
ElectricAuth. Moreover, the sleeve becomes part of each user’s
own challenge definition, i.e., an attacker trying to impersonate aparticular user will require obtaining or copying the user’s sleeve,
which we later validate in our studies by actually providing theimpersonators with the EMS sleeves of the legitimate users.
EMS parameters: Our EMS stimuli on all electrode locations are
the same: single-shot square-impulses with an intensity of 10mA
and a pulse-width of 200µs. We chose this configuration for two
reasons. First, we configured EMS impulses to generate small andsubtle finger movements rather than large conspicuous movements
typical of most existing EMS research, because this enables morepractical authentication scenarios. While these smaller movements
are harder to recognize, our results suggest that our authentication
model can accurately track these (see Section 7). Second, we opted to

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng#n(3) builda model(iii) feed tothe model

authentication
model

decision(iv) inference

grant / deny

Figure 3: Interactive pipeline for the registration (registering a new user) and authentication phase (interactive use inruntime). User response can be caputured using a motion
capturing device, e.g., IMUs and cameras (not shown). In thissystem, the EMS device and electrodes are wearable; the motion capturing device is either wearable or placed near theuser; while the authentication model can be remote.

3. Authentication software and pipeline.
The software component of ElectricAuth, written in Python, handles all the interactions between EMS device, motion sensing, modeltraining and real-time authentication. The pipeline of ElectricAuth,which is depicted in Figure 3, is comprised of two phases: (a) registration and (b) authentication.
In the registration phase, marked by solid lines in Figure 3,
registering a new user (after calibration) is as follows: (1) a set of n
EMS challenges are sent one at a time; (2) the user’s movements
in response to each challenge are recorded; (3) these responses areused to train a machine learning-based authentication model forthis user. The number of challenge-response recorded per user isthe primary factor that dictates the total time the system needs forregistering a single user (we detail this in Section 4).
In the authentication phase, marked by dashed lines in Figure 3,
verifying a user’s identity in run-time is as follows: (i) one random
EMS challenge belong to the claimed identity is chosen, deleted
immediately from the database, and sent to the user via EMS; (ii)the user’s movements in response to the challenge are recorded;(iii) the motion responses are fed into the trained authentication
model of the claimed identity; (iv) the system determines whether
this user is legitimate (i.e., being the claimed identity) or not.

3.2

Engineering EMS-based Challenges

As our system is the first that explores EMS for authentication, we
dedicated a significant part of our exploration in understanding how
to increase the challenge pool using EMS; a large challenge poolis what makes a challenge-response based authentication systemrobust against data breaches and replay attacks. Naively, one canstimulate the user’s muscles with individually configurable pulses;
however, this (1) requires more calibration time and (2) does notreveal the mechanisms that explain these individual responses.
Therefore, we kept purposely all EMS impulses uniform for all usersof our system; this grants us more confidence in interpreting theunique responses as originating from the physiological differences
between users. Yet, this introduces a challenge when it comes todiversifying the challenge pool.
One straightforward solution (adopted by many existing works
on challenge-response biometrics [40, 42, 73]) is to sequence stimuli
but separate them by a fixed time gap. If we were to adopt this aswell, the maximum number of EMS challenges would be S L , where
S is the number of unique stimuli in the system and L is the numberof stimuli in each challenge. For example, a sequence of six EMS
impulses over four possible EMS channels, with a fixed rest period
between each impulse, results in 46 = 4, 096 challenges. We wereinterested in whether we could dramatically surpass this approach.
To significantly increase our challenge pool, we explored a rather
unused property of human muscles that causes them to responddifferently to EMS depending on their current state of contraction.
We call this temporal dependency.
Temporal dependency. We empirically found, in our preliminary pilots, that a subject’s response to an EMS stimulus is affectedby the previous stimulus in the same challenge, and the impact
depends on the time gap between them (represented as τ ).

CHI ’21, May 8–13, 2021, Yokohama, Japan

acceleration(m/s 2)User Authentication via Electrical Muscle Stimulation15

τ = 0.17
τ = 0.10

10
0.00s

0.20s

0.40s

0.60s

Figure 4: An example of how a response changes when thetime gap between two EMS stimuli varies: we vary the time
gap from 0.1s (blue curve) to 0.17s (orange curve).
Figure 4 shows two example traces of a finger’s acceleration
when we stimulate the user’s muscles with a sequence of two stimuli (A and B) but vary the time gap between A and B (i.e., τ = 0.1s
and τ = 0.17s). The measured acceleration displays different characteristics when we vary τ . The strongest candidate for a physiologicalexplanation is that muscle contractility and elasticity vary withmuscle length [21, 81], and the response to a stimulus depends onthe muscle lengths at the time of stimulation. Thus, depending on
the gap between A and B, the subject’s unique contractility [23]and elasticity [26, 82] will lead to different responses.
The use of temporal dependency affords a large EMS challengepool by varying the time gaps between consecutive stimuli. Assuming they all produce distinguishable responses, the numberof unique challenges (of length L) is upper bounded by S L · T L−1 ,where T is the number of distinct time gaps. For our ElectricAuth
prototype, we utilize S = 4 EMS channels and T = 7 different time
1 s, 2 s, ..., 7 s), which in early pilots we found to lead togaps (τ = 30
30
30
sufficiently different movement outcomes. The maximum numberof unique challenges is 112 (L=2), 87, 808 (L=4) or 68, 841, 472 (68M)(L=6), compared to 16 (L=2), 256 (L=4) or 4, 096 (L=6) when we donot vary the time gap.
Further increasing the challenge pool. Encoding longer challenges is another way to expand the challenge pool. With S = 4
stimulus locations and T = 7 time gaps, sending L = 8 pulses (<2s)
increases the pool size to 53, 971, 714, 048 (48 × 77 ). Also it is possible to add more electrodes or customize EMS impulses to further
diversify the pool.
Checking for uniqueness. Ideally, every challenge-response
authentication in the pool is unique. However, in practice this might
not be the case given the granularity and sensitivity of motion
sensors. To enforce uniqueness, ElectricAuth can apply a verification step during user registration. Specifically, after generating
new challenges for a user at the registration phase, it collects thecorresponding responses and checks the similarity across these
responses and previously registered responses (e.g., computing themean square error (MSE) between raw responses). If a new challenge is identified as a previously registered challenge, this newchallenge is removed.

4

USER AUTHENTICATION MODEL

We now present the design of ElectricAuth’s user authentication
model. ElectricAuth requires a trained authentication model per
legitimate user, which is used to verify whether a test subject is
indeed that user. To do so, the model takes as input the response
to a given challenge designed for the legitimate user, and outputs
whether the test subject is legitimate. Our authentication model

CHI ’21, May 8–13, 2021, Yokohama, Japanwas designed with two objectives in mind: (1) minimize the amountof samples collected from the user (i.e., reducing registration overhead) and (2) resist common attacks (e.g., impersonation and replay
attacks) and data breaches.

reconstructedtrained on P1's data
Z
Encoder
Decoder

input

encoderZ

reconstructed

MSE-

decoder

MSE

Overview

Initially, we explored implementing our model using specific features of the user’s IMU data in response to particular EMS challenges (so called feature-engineering). However, we quickly realizeda major downside of this approach: as the response data we capture in real-time from the IMUs is complex (thirty concurrent data
streams: 5 × 3 axes of acceleration and 5 × 3 axes of rotation), simple feature extraction might not capture the full expressivity of thedata. Therefore, after experimenting with this approach, we turned
to neural network based models.
We implemented a robust authentication model that, for eachregistered user, integrates two deep neural network (DNN) models
to resist both impersonation and replay attacks. Specifically, authentication starts with (1) an unsupervised anomaly detector,
which verifies whether a response was produced by the user themodel belongs to (i.e., the legitimate user); this step prevents impersonation attacks, in which a different user attempts to gain identityof the legitimate user. If a response passes the anomaly detector, it
then enters (2) a challenge classifier, which detects and rejects
replay attacks by verifying whether the response is the reaction to
the challenge used in the current authentication session.
Both models are trained using only the challenge-response pairsof this legitimate user collected during registration. When the user(re)registers a new set of challenges, we retrain both models fromscratch using the new data. This also enables ElectricAuth to recoverfrom data and model breaches.

4.2

a unsupervised anomaly detection based on reconstrction,

input trained on P1’s responses.

Detailed Model Design

1. Verifying user via unsupervised anomaly detection.
We implement user verification as unsupervised anomaly detection [7], where the detection model is trained on only the legitimate
user’s responses collected during registration. At run time, themodel verifies whether an input response was likely originated by
the legitimate user. This anomaly-based detector leverages the factthat responses from other users will display characteristics different
from those of the legitimate user. Thus the model is designed to
produce normal output when the input response comes from its
legitimate user, but abnormal output when the input comes fromany other user. This design prioritizes generality as the model istrained without requiring knowledge on other users.
For our prototype, we apply a reconstruction error based anomaly detection system [63]. Specifically, we use variational autoencoder (VAE) [16], a DNN architecture well-known for automatically
capturing complex patterns in target data. As shown in Figure 5,each VAE starts from an encoder to extract latent features from each
input response, followed by a decoder to reconstruct the response
from these features. It then computes the mean squared error (MSE)
between the input and reconstructed responses, and outputs it as
the anomaly score of the input. Ideally, the anomaly score will
be low when the input response comes from the legitimate userand high when the input comes from a different user. Thus, theb input and reconstructed

responses of P1, MSE=0.057

a cceleration (m/s2)

4.1

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng

c responses of anotherparticipant, MSE=0.604

11reconstructed
input

10
9

input
reconstructed

8
0.0s

0.1s

0.2s

0.3s

0.0s

0.1s

0.2s

0.3s

Figure 5: Authentication starts with an anomaly detection,
which verifies if a response came from the legitimate userthat the model belongs to (P1 in this example). (a) The anomaly score is the MSE of the input and model-reconstructed responses. We illustrate how our anomaly detector correctly:(b) identifies P1 (legitimate user) with a low MSE and (c) rejects P2 (impersonator) with a high MSE.

system can configure a threshold on the anomaly score, where avalue larger than the threshold indicates the test subject is not
the legitimate user (i.e., the user verification fails). In ElectricAuth,
we choose the threshold during model training to reach a desired
false rejection rate (i.e., the probability that the model rejects thelegitimate user’s input responses).
For our implementation, we train our VAE using each legitimate user’s responses to all the chosen challenges collected during
registration. The data aggregation (across challenges) creates a reasonable amount of data to train the VAE successfully. We consider
a common VAE architecture [19], where the encoder contains twodense layers of 400 and 200 neurons, respectively, and the decoder
contains two dense layers of 400 and 3600 neurons, respectively, tomatch the input size.
To illustrate the effectiveness of our model, we plot in Figure 5b-c
the input and reconstructed responses of a legitimate user (here,
P1 of our user study) and a different participant P2 (also fromour user study), respectively, using the model trained for P1. For
the sake of visual clarity, we only plot the responses from only
one accelerometer axis. Both responses are not used for modeltraining. We see that P1’s response is well-approximated by themodel-reconstructed response; in fact, with a very low MSE of 0.057.
On the other hand, P2’s response (when tested on P1’s model),produces a large MSE of 0.604, around 10-fold higher than the MSEof the legitimate user (P1).
Figure 6 shows the responses (collected by the IMUs) of fivesubjects (P1-5) to a challenge designed for P1 (the legitimate user
in this case). When tested on P1’s anomaly detection model, theanomaly scores for these responses are 0.70, 5.03, 9.44, 8.81 and 7.50,
respectively. Thus P1’s model easily rejects P2-5 as impersonators.

P1

P2

P3

P4

P5

30
0

8

m/s 2

15

CHI ’21, May 8–13, 2021, Yokohama, Japan

−30

1
15

5

8

0

1

−5

rad/s

gyroscope accelerometer

User Authentication via Electrical Muscle Stimulation

Figure 6: Sample responses of a P1’s challenge (with L=6 impulses) and impersonators’ responses (P2, P3, P4 and P5) to thesame challenge. Each row is a sensor channel and each column is one data sample. Here we show one second of responses.
When tested on P1’s anomaly detection model, the corresponding anomaly scores for P1-5 are 0.70, 5.03, 9.44, 8.81 and 7.50,
respectively. In this case, the model can easily detect impersonators.
2. Verifying challenge via challenge classifier.
Next in the authentication pipeline, ElectricAuth verifies whether
the input response matches the challenge used in the current session.
As mentioned earlier, this is designed to resist replay attacks, wherean attacker, after obtaining a copy of the legitimate user’s responses
to previously used challenges, replays one of these responses to
bypass authentication.
ElectricAuth implements challenge verification by training aclassifier: given an input response, it determines the corresponding
challenge. If the identified challenge matches the challenge usedin the current authentication session, authentication is granted;
otherwise, rejected. Moreover, the classifier also detects when the
input response comes from any challenge not used to train theclassifier, because the classifier will output a low confidence score.
Our implementation uses a Convolutional Neural Network (CNN)
for this classification task [58]. It contains four convolutional andtwo dense layers. Each convolutional layer employs 64 filters sized
5 to extract information from the input. The information is thenfed into the two dense layers containing 128 and 112 neurons, respectively. At the end, a softmax function is applied to the output
to produce a probability distribution over potential challenges. We
train our CNN using the same training data used in training theabove anomaly detector, except that we now label each responseby its corresponding challenge.

5

CONTRIBUTIONS, BENEFITS AND
LIMITATIONS

Our main contribution is that we explore EMS in a new direction,i.e., leveraging EMS’s intersubject variability as a novel modality
for active biometric authentication.
ElectricAuth inherits the advantages of both biometric and password authentication: (1) As with any biometric authentication device, ElectricAuth does not require memorization or cognitive effort
ś this makes our system suited for a wide range of users, including
those with cognitive impairments; (2) Unlike passive biometrics(such as fingerprints), ElectricAuth’s challenge-response structure
makes it secure against data breaches and replay attacks; Lastly,(3) ElectricAuth leverages temporal dependency to create a verylarge set of challenges ś in this way, ElectricAuth can dispose achallenge anytime like a one-time password.

On the flipside, ElectricAuth is subject to several limitations: (1)Like any solution based on electrical muscle stimulation, ElectricAuth requires some initial adjustments of the electrodes (during
registration) that ensure pain-free operation, and also periodic regelling of adhesive electrodes to prevent electrodes from fatigue
and eventually affecting the authentication accuracy; (2) ElectricAuth requires user’s hands to be free while authenticating, making
it more suitable for hands-free applications; (3) As with existing
biometric devices, ElectricAuth requires initial registration. Specifically, each challenge needs to be registered in advance; Lastly, (4)while a single EMS impulse can be very short (e.g., 200µs) to achieve
very high accuracy, we expanded our sequence to 1.2 seconds ofmuscle stimulation, as such ElectricAuth takes ∼1300ms to authenticate a user in runtime. While this is certainly fast enough for mostapplications, it is longer than some passive approaches, such as
fingerprint recognition.

6

OVERVIEW OF EVALUATIONS

We evaluated our concept of using EMS for authentication by meansof four different evaluations, each shining light on a different facetof our research question. All studies were approved by our Institutional Review Board (IRB no. omitted for anonymity). To aid thereader in understanding the different validations we performed,
we present an overview of our evaluations with a preview of theirrespective results:
I. User studies. We evaluated the feasibility of EMS as an active
biometric with three experiments and 13 participants. We found thatthat ElectricAuth resists three common attacks: (1) impersonations
attacks, in which participants played impersonators against eachlegitimate user (attack success rate or false acceptance rate: 0.17%);(2) replay attacks, in which participants mimic the movements of
the legitimate user from videos (success rate: 0.00%), or replay aperfect record of response to any used challenges directly into
the IMUs (success rate: 0.00%); and, (3) synthesis attacks, in which
we synthesized data from the participants’ data to attack theirauthentication models (success rate: 0.2-2.5%).
II. Exploratory longitudinal study. We conducted a longitudinal study over 24 days and for two participants, to examine ElectricAuth’s authentication model over time and against various muscle
conditions (fatigue, humidity, etc.). We found that an authentication

CHI ’21, May 8–13, 2021, Yokohama, Japanmodel, trained using the first three days and tested over the next
21 days, performed very stable over time and on muscle conditions
unseen during training (false rejection rate ≈2%, with a SD around3%).
III. Technical evaluation. A technical in which we measured
ElectricAuth’s latency, model training time, and the feasibility ofusing depth cameras as an alternative motion tracking modality.
IV. Testing model robustness at scale, using synthetic data.
We applied a data-driven approach to better understand how oursystem might scale to larger numbers of users that is simply impractical to test in the laboratory. To realize this, we employed the user
study data to train deep generative models that produce synthetic
impersonator responses, and used these data to further evaluateElectricAuth. We found that, across all the data-driven experiments
and for all legitimate users, no generated response was accepted byElectricAuth (attack success rate: 0).

7

USER STUDIES

To evaluate the feasibility of EMS as an active biometric we conducted a user study, with three sub-experiments, which allowed
us to understand: (1) authentication accuracy, in which we evaluated the accuracy of our system; (2) impersonation attack, inwhich we evaluated its robustness against attackers trying to impersonate legitimate users; and, (3) replay attack and synthesis
attack, in which we evaluated its robustness against three replay
attacks (human mimicry, record-replay, breach-replay) and one
online synthesis attack.
In total, we collected 70,000+ wrist and finger movements as
responses to EMS challenges (stimulation patterns). We analyzed
the performance of ElectricAuth using four standard metrics, typically employed to assess a system’s authentication performance:(1) False rejection rate (FRR), which measures how often a legitimate user is mistakenly denied, at a specific threshold; (2) False
acceptance rate (FAR), which measures how often an illegitimate
user is mistakenly authorized, at a specific threshold; (3) Equal
error rate (EER), the rate at which the measured FRR equals themeasured FAR for a certain threshold; and, (4) Receiver operator
characteristic curve (ROC curve), which describes the relationship between FRR and FAR as a curve, by varying its threshold.

7.1

Experiment#1: Authentication Accuracy

The goal of our first study was to understand the authentication
accuracy of our system. Furthermore, as we were interested in theimpact of the length of the EMS challenges on its performance,
we recorded participants’ movements to three sets of challenges,
based on their number of impulses L = 1, 2, 6 (referred to as length1, -2, and -6 challenges, respectively). For each challenge set we
stimulated participants’ forearms and recorded finger movements
using IMUs.
Participants. We recruited 13 participants from our institution(mean age= 24 years, SD= 3 years; mean weight= 66.3 kд, SD=
13.3 kд; mean height= 171.2 cm, SD= 8.2 cm; 7 females, 6 males).
Participants were compensated with 50 USD for their time.
Apparatus. Participants wore our system on their left forearm.
This included the EMS and IMU components, which were fitted
by an experimenter. To ensure participants’ comfort with EMS,

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng

we calibrated it so that all electrode channels operated pain-free.
To ensure that all target muscles were correctly stimulated (seeImplementation for details), we gradually increased the intensity
during calibration, following calibration process similar to [5]. If
a participant felt any discomfort before reaching the target intensity, we moved to another electrode position. To minimize fatigue,
participants rested their elbow on a resting base.
After calibration, we recorded each participant’s exact electrodelocations by making a custom sleeve with marked positions. These
13 sleeves were later used in Experiment #2, where we examined
impersonation attacks (i.e., each impersonator wore the sleeve of alegitimate user to attack our authentication system).
During the study, participants did not receive any specific instruction, since we wanted them to react naturally to the EMS
impulses.
EMS challenges. The EMS challenges in our study were configured as previously described, i.e., a challenge was comprised of asequence of single-shot square-impulses with an intensity of 10mA
and a pulse-width of 200µs; these sequences were of length-1, -2 or-6. In between each pair of impulses we included a time gap. Each
1 s, 2 s, ..., 6 s, 7 s); thus,
gap was one of seven possible durations ( 303030 30
the recording duration of a length-1, -2, and -6 challenges were
0.6s, 0.8s and 1.2s, respectively. While length-1 challenges werecollected in this experiment, these were only used for an analysis
in Experiment#2 (anomaly detector performance).
Procedure. To test whether ElectricAuth correctly authenticatesour 13 participants, we first registered each participant. Our system
did this automatically: (1) a participant feels an EMS challenge,(2) their forearm muscles react involuntarily, and (3) our system
records the response. We repeated this process 10 times per challenge. These ten responses were shuffled to remove potential sequence effects. These responses were then randomly divided intoa training set (eight responses) and a testing set (two responses).
Then, our system took these eight responses (for all challenges) andtrained the anomaly detector and challenge classifier for each participant. As cross-validation, we repeated this process to produce
10 authentication models per participant and reported the average
test results of these models in all our subsequent experiments.
For length-1 and -2 challenges, we tested the full set of challenges(a total of four for length-1 and 112 for length-2). For length-6
challenges, we were forced to test only a subset, since the fullset includes 68, 841, 472 challenges, which would be fatiguing forparticipants. Therefore, we randomly chose 115 challenges fromthe full set.
In total, each participant performed 2310 trials: 40 trials of thefour length-1 challenges (10 repetitions); 1120 trials of the 112
length-2 challenges (10 repetitions); and, 1150 trials of the 115
length-6 challenges subset (10 repetitions).
Results: overall authentication accuracy. We first examine
the accuracy of the end-to-end authentication model, which depends on the accuracy of both the anomaly detection model andthe challenge classification model. We defined overall accuracy
as the probability that a legitimate response successfully passed
the two-step authentication. Note that the accuracy is dependent
on the anomaly threshold used by ElectricAuth’s authentication
model. During model training, we configured the threshold to reachUser Authentication via Electrical Muscle Stimulation

CHI ’21, May 8–13, 2021, Yokohama, Japan

P7

2.5

5.0

2%

5%

P8

2.3

5.5

P1

2.3

6.1

P9

3.2

5.8

P2

2.1

5.5

P10

2.2

5.0

P3

2.1

4.9

P11

2.3

5.0

P4
P5

1.7
2.6

5.4
4.8

P12

2.8

5.5

P13

2.6

5.4

P6

2.7

6.2

AVG(SD)

2.4(0.4) 5.4(0.4)

Table 1: The measured false rejection rate (FRR, %) for all registered participants (P1-P13) closely matched the planned
FRR. The measured FRR was calculated for each participant
using their test responses to 115 length-6 challenges.

accuracy (%)

a planned false rejection rate (FRR). Note that the threshold is determined using just the training data (without the knowledge ofany run-time testing data). Ideally, the run-time measured FRR (i.e.,1−accuracy) should equal to the planned FRR.
For each of the 13 registered participants, Table 1 summarizes themeasured FRR (i.e., = 1−accuracy) aggregating the results acrossall 115 challenges (of length 6). Here we reported the results forplanned FRR of 2% and 5%. We see that the measured FRR closely
matched the planned FRR. Across all the participants, the mean
measured FRR is 2.4% (SD of 0.4%) and 5.4% (SD of 0.4%), respectively,
matching the two planned FRR values (2% and 5%).
Results: challenge classification accuracy. Digging deeper
into the accuracy of our system, we turn to evaluate the accuracy ofchallenge classification model (as it is the main component protecting against replay attacks). Our accuracy findings are depicted in
Figure 7. For length-2 challenges (complete set, i.e., 112 of them) the
average accuracy is 99.89% (SD=0.19% across users). And for length6 subset of challenges we found an accuracy of 99.78% (SD=0.50%).
These results also show that the challenges (full set of length-2,
subset of length-6) are unique across each other.
100

99.89 (SD=0.19)

99.78 (SD=0.50)

complete set of length-2

subset of length-6500Figure 7: ElectricAuth’s challenge classification accuracy for
length-2 and length-6 challenges.

7.2

Experiment#2: Impersonation Attacks

In this user study, we measured our system’s ability to resist impersonation attacks.
Participants. For this study, we invited all 13 participants from
Study#1. Participants were briefed that they would play an attacker
trying to impersonate other participants. Participants were compensated with 50 USD for their time.
Procedure & apparatus. For each target participant, we applied
their customized challenges (used in Experiment #1) to the other 12participants (as impersonators) and collected their responses. Impersonators were asked to wear the sleeve fabricated for each target

participant in Experiment #1. These sleeves grant the impersonatorwith the exact electrode positions of the legitimate user. We also
tested cases where impersonators wear their own sleeves and otherparticipants’ sleeves and found that wearing the target participant’s
sleeve leads to the most effective attack; thus we focused on it.
In total, each participant performed 3240 trials: 480 trials of thelength-1 challenges (10 repetitions per challenge, impersonated 12other participants); 2760 trials of the length-6 challenges subset (2repetitions per challenge, impersonated 12 other participants).
Impersonating someone else by using their electrode placement
does not guarantee comfortable use, i.e., we did not adjust electrodes to preserve the legitimate participant’s placement. While noparticipant felt uncomfortable with length-1 challenges, there wassome discomfort on a few length-6 trials (3.8% of the total); anytime
a participant voiced discomfort, we stopped the stimulation anddiscarded this trial.
Results: performance of anomaly detector. To deepen our
understanding of intersubject variability and the anomaly detection
model performance, we first compared the responses to a single
stimulus (or length-1 challenge), submitted by each target participant in Experiment#1 and the 12 impersonators in this experiment.
We fed these responses to the target participant’s anomaly detection
model and recorded their anomaly scores. For the sake of visualclarity, we normalized these anomaly score values by the target
participant’s average anomaly score value (see Figure 8).
10

normalizedanomaly scoreplanned FRR
participant

5
12 impersonators
legitimate user
0
P1

P2

P3

P4

P5

P6

P7

P8

P9 P10 P11 P12 P13

Figure 8: Normalized reconstruction error for the responses
to each participant’s length-1 challenges, submitted by boththe legitimate user and the 12 impersonators. For visual clarity, we capped the value at 10.
We found that our anomaly detector for each participant is welltrained and can distinguish impersonators from the legitimate participant. This is clear as Figure 8 depicts a large separation between
the legitimate participant and the impersonators. It also confirms
EMS intersubject variability.
Results: robustness against impersonation attack. We examined the end-to-end success rate of impersonation attacks against
each participant, using the attack data collected on length-1 challenges (complete set) and length-6 challenges (the 115 subset).
Figure 9(a) depicts the false acceptance rate (aggregated across 13
participants’ models since they are consistent) against length-1 andlength-6 challenges, for the planned FRR of 2% and 5%, respectively.
With length-1 challenges (4 challenges), the impersonation attack
failed. With length-6 challenges, the attack exhibited a very low
success rate, only 0.83% (SD=1.14%) at planned FRR=2% and 0.17%(SD=0.32%) at planned FRR=5%. Again this suggests that our system is robust against impersonation attacks. Figure 9(b) shows the

CHI ’21, May 8–13, 2021, Yokohama, Japan

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng

a false acceptance rate (FAR) on impersonators for length-1
and length-6 challenges

false accept
rate (%)

10
FNR=2%
FNR=5%
5

0

0.00

0.83

0.00

complete set of length-1

0.17

subset of length-6

b ROC curves on impersonators for length-1 and -6 challenges
false rejectrate (%)

100

complete set of length-1
subset of length-6

50

0

0

20

40

60

false accept rate (%)

80

100

Figure 9: ElectricAuth’s robustness against impersonation
attacks.

ROC curves under impersonation attacks with length-6 challenges,
where ElectricAuth achieves an EER of 1.31%.

7.3

Experiment#3: replay and synthesis attacks

In this user study, we measured ElectricAuth’s robustness against
replay attacks and synthesis attacks, both trying to engineer a
response to bypass authentication after obtaining some knowledge
on the legitimate user’s responses.
We considered three replay attacks, and one synthesis attack,ranging in increased attack complexity:(1) human mimicry, where the attacker video-tapes and studies
a participant’s responses and then physically mimics the responses
without wearing any EMS;
(2) record-replay, where the attacker compromises the IMUs sothat they can perfectly record the target participant’s response tochallenges in previous authentication sessions, then during a newauthentication session (i.e., a new challenge), the attacker selects aprevious recorded response and directly feeds it to the IMUs;(3) breach-replay, where the attacker breaches the database or
the model to recover stored challenge-response data, and feeds one
response to the IMU’s circuit; here ElectricAuth reacts to the breachby asking users to re-register using new challenges and retraining
the models;(4) online synthesis, where the attacker compromises both EMS
and IMUs to record both the challenge and the response in previous sessions; then at run-time, the attacker searches through theserecords and attempts to synthesize and submit in real-time an engineered response to the current challenge. For these attacks, weevaluated ElectricAuth using the false acceptance rate (FAR) and
the ROC curve.
Participants. We recruited five participants to perform the human mimicry attack: three from our previous study (chosen at
random) and two new participants from our local institution (ages:
25 & 22 years old; weights: 55 & 99 kд; heights: 177 & 180 cm; one

female and one male). Participants were compensated with 50 USD
for their time.
Procedure. In the human mimicry attack, we asked participants to study 23 videos of finger movements of a target participant.
Each video was a recording of one single response to a length-6
challenge. Participants were allowed to study these videos as manytimes as they intended and in slow-motion (recorded at 240 fps,with clear and unobstructed view of the finger movements). Once
confident and ready, participants were asked to mimic these finger
movements while wearing only the IMU component of our system,in their best attempt to impersonate the target participant. Furthermore, as reference, we also asked the target participant that
had partaken in Experiment#1 to self-mimic 23 of his own EMS
responses after observing and studying them.
Results: robustness against human mimicry. We found thatnone of the study participants was able to fool our system by mimicking the target participant’s responses. Note that these participants were allowed to view the videos in slow motion and as manytimes as they want. The FAR was 0 for a FRR ≥ 2%. This confirms
our intuition that the EMS movements are indeed involuntary andincredibly hard to voluntarily replicate.
Results: robustness against record-replay attack. For this
we utilized data from Experiment#1. Even assuming perfect recording on the side of the attacker (i.e., their recording channel has
access to IMUs without any noise or sample rate issues), we found
our system to be robust against these attacks. In particular, forlength-6 challenges, the FAR (against any of the 115 challenges)
was less than 0.0014% across all 13 participants when FRR ≥ 2%. ThisFAR is significantly smaller than the challenge misclassification
rate of our authentication model (0.2%, see Experiment#1).
Results: robustness against breach-replay attack. Again we
utilized data from Experiment#1. For each participant, we randomly
split the 115 challenges (and their responses) into two equal sets (Aand B). We assume that the attacker, via data breach, obtains thedataset A and uses them to launch replay attacks against ElectricAuth. At the same time, ElectricAuth reacts to the data breach byasking users to re-register via a set of new challenges (i.e., datasetB) and retraining the authentication models using dataset B. Like
the above, we found our system to be robust against these replay
attacks ś the FAR was less than 0.0098% when FRR ≥ 2%. Moreover,both the anomaly detector and challenge classifier components inthe model were able to reject the attack responses.
Results: robustness against online synthesis. We evaluatedthe success rate of an online synthesis attack, using the data from
Experiment#1. We assume the attacker has access to the EMS and
IMUs without sample rate or noise issues, which is in itself very
unlikely. The idea behind a synthesis attack is that the adversary
records both challenges and their responses, and segments these
into chunks, as in "this impulse at electrode 1, moves this finger
by this much", and so forth. We referred to this approach as thesimple synthesis attack. A more advanced attack would capture theimpact of temporal dependency by segmenting responses into perpair-stimuli chunks, as in "these two impulses at electrodes 1 and 2,
move these fingers by this much"). After segmenting the responses,
the attacker will observe each incoming impulse of a new challenge
and inject a response into the IMUs in real-time. Note that evenUser Authentication via Electrical Muscle Stimulation

CHI ’21, May 8–13, 2021, Yokohama, Japana false acceptance rate (FAR) under different replay
false accept
rate (%)

and synthesis attacks at a planned FRR of 5%
10

7.47

5
2.46

2.23

0

0.00

0.00

0.00

human
mimic

recordreplaybreachreplay

0.87

0.12

0.16

latency 10ms 20ms
=0ms
synthesis (simple)

0ms 10ms 20ms
synthesis (advanced)

b ROC curves under different replay and synthesis attacks
false rejectrate (%)

100

record replay
breach replay
human mimicry50

simple synthesis (20ms)
advanced synthesis (20ms)

0

0

20

40

60

false accept rate (%)

80

100

Figure 10: ElectricAuth’s robustness against different replay
and synthesis attacks. For online synthesis, the attacker had
perfect records on responses to 50 challenges. Here ElectricAuth operates on length-6 challenges.

assuming best hardware and knowledge, assembling this response
will always have some latency.
Figure 10(a) plots the FAR of online synthesis attacks considering
three latency values, assuming the attacker has observed R=50
challenge-response pairs and the planned FRR is set to 5%. Even
under the extreme attack case (zero latency, which is physically
impossible), the attack success rate is low (i.e., FAR=2.2% and 7.5%
for simple and advanced attacks, respectively). When the synthesis
latency reaches 20ms, which still depicts an unlikely extremely fast
response, the FAR drops to 0.1-0.2%. The same applies when weraised R to 75 (i.e., the advanced attack’s success rate is only 0.25%
for latency=20ms).
Results: ROC and EER. Finally, Figure 10(b) plots the ROC
results for all the replay attacks and synthesis attacks (with latency=20ms). We see that ElectricAuth achieves noticeable EERs only
for the synthesis attacks (1.48% for simple synthesis and 1.59% foradvanced synthesis). These results show that ElectricAuth is robust
against replay and synthesis attacks, even those extreme ones.

8

EXPLORATORY LONGITUDINAL STUDY

We conducted an exploratory longitudinal study to examine ElectricAuth over time and against various environment and muscle
conditions. Specifically, we performed fixed-model-over-time
tests, which depicts how an authentication model trained using
the first three days of data will perform over time and under muscle
conditions (e.g., humidity, fatigue, etc.) and other non-predictableenvironmental factors that were not present in the training data;
Participants. Due to Covid-19, only two co-authors participatedin this study (ages: 25 & 24 years old; weights: 70 & 54kд; heights
170 & 163cm; one male and one female).
Procedure. In the day prior to the start of the 24-day period, we
conducted an initial calibration session (following the same method

and apparatus described in Experiment #1). Then, we followed with24 days of data collection. We collected data once a day. For eachparticipant, we randomly chose 115 length-6 challenges to collectuser responses.
For this study, we used fabric sleeves with embedded EMS electrodes at the precalibrated positions for each participant, following
a design similar to [36]. Each day, participants were asked to wear
their custom electrode-sleeves (depicting their calibrated locations).
Participants fitted the sleeve by themselves prior to the trials byaligning markings on the sleeve with their elbow and top of wrist.
If the electrode pads were dry, they re-gelled it using conductivegel. Then, they recorded their response to the 115 challenges everyday. For each challenge, they collected more than 6 responses perday. After the trials, they removed the sleeve until the next day.
Conditions. To explore the impact of environmental and physiological variations, we conducted data collection under combinationsof three conditions: (1) time of the day (morning/afternoon/late);(2) environment humidity (dry/damp); and (3) muscle fatigue (normal/fatigued). We randomly chose one combination per day, andeach combination was tested at least twice during the study. In thedamp condition, participants were asked to stay in their bathroom
with the humidity at over 80% and temperature over 29 ◦C for morethan 20 minutes right before the data collection. For dry condition,
participants stayed in an air-conditioned room of humidity 55%
and temperature 24◦C. To test our system right after the muscles
started to fatigue, participants were asked to do a routine of intense
forearm muscle training (dumbbell wrist flexion and extension) fora minimum of 15 minutes before collecting data.
During the days in which we tested ElectricAuth under normal muscle conditions, participants still performed their forearmmuscle training but after the data collection session. This allowed
us to study if extended muscle exercise would affect the system
performance.
Training the authentication model. For each participant, weused data collected in the first three days to train the authentication
model (the anomaly detector and challenge classifier). For bothparticipants, the training data were collected under the same (dry,pre-workout) condition. The rest of the data (21 days) were used
for testing our authentication models. The testing data contained
conditions both seen or unseen in the training data. We excluded
day 10 and 11 for participant 1 due to need for replenishing the
sticky gel on the electrodes, i.e., waiting for gel supply.
For all the trained models, we configured the anomaly detection
thresholds to achieve a planned FRR of 2%. As discussed before,such configuration is set using only the training data without theknowledge of any testing data.
Results: fixed-model-over-time tests. To understand the impact of a specific condition (time of the day, environment humidity
or muscle fatigue), Fig. 11(a)(b) shows the measured false rejection rate (FRR) under each condition. For both participants, themeasured FRRs are reasonably consistent across conditions andclosely match the planned FRR (2%). But more importantly, while
our authentication models are trained only under the combinationof dry and pre-workout conditions, they remain accurate underother conditions not seen during training. This provides initialevidence on the generality of ElectricAuth.

CHI ’21, May 8–13, 2021, Yokohama, Japan

Yuxin Chen, Zhuolin Yang, Ruben Abbou, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng

false rejectrate (%)

a P1’s false rejection rate (FRR) under each condition
10
50

c mean FRR

10

10

1.26

2.18

AM

PM night

dry

1.50

damp

humidity

1.64

2.25

normal fatigued

0.72

0

fatigueacross days

5

5

2.69
1.28

time

b P2’s false rejection rate (FRR) under each condition

AM

1.41

2.69

PM night

time

1.44

1.89

dry

damp

humidity

1.52

1.87

normal fatigued

0

P1

P2

fatigue

Figure 11: Results of fixed-model-over-time tests. (a) and (b) shows for both participants, our system is stable under various
conditions; (c) our system is stable over time (21 days) for both participants.
For both participants, we also plot the mean FRRs over time in
Fig. 11(c). We see that the FRR is stable over time, (mean=2.01%,
SD=3.13%) for participant 1 and (mean = 1.76%, SD=2.90%) for participant 2. No significant performance degradation over time wasfound for both participants. These results suggest that ElectricAuth
remains relatively stable on a monthly scale.

9

TECHNICAL EVALUATION

We deepened our understanding of how future interactive systems
might be built based on EMS authentication by measuring system
latency, training time, and the feasibility of depth cameras as analternative tracking modality.

9.1

Authentication latency

To measure ElectricAuth’s inference latency (i.e., time needed tomake a decision in run-time) and the model training, we utilized
the data from the participants of Experiment#1, i.e., 115 length-6
challenges, eight response records per challenge for training, two
response records for testing.
Run-time inference latency. As we probed the future of EMSbased authentication, we were interested in understanding how
ElectricAuth would perform on smaller platforms, such as laptops oreven embedded devices. As such, we ran our system on a MacBook
Pro with a Intel Core i9-9880H CPU and on a Nvidia Jetson Nanoembedded device (measuring 70 x 45 mm). Our results show that oursystem can authenticate a user in 3ms on laptop’s CPU and 35ms
on a small embedded device. This result suggests our approach isfeasible for quick authentications and even available on mobile or
wearable devices.
Training latency. Our results demonstrate that it took 35s (33sfor anomaly detector; 2s for the challenge classifier) to train thecomplete model on a Nvidia Titan RTX GPU and 542s on a laptop’s
CPU (501s for anomaly detector; 41s for the challenge classifier).

9.2

Using camera to capture finger movements

While we used IMUs to capture finger movements in our user
study, we believe these movements can also be captured via othermodalities, such as depth cameras, a common platform for handpose estimation [72, 84]. To test our belief, we carried out a simple
feasibility experiment. Here, we swapped out IMU sensors with a
RGB-D camera (Intel RealSense D435), which operates at 640x480
resolution and 30 frames per second. The camera was placed in
front of the participant with a distance of 50cm.

Following the same procedure of Experiment#1, we recorded, via
the depth camera, the responses to our 115 length-6 challenges on
one participant. We then used an available hand gesture recognition
model (from [38]) as our challenge verification model.
We found that the challenge classification accuracy for this simple feasibility experiment was 99.57% using the depth image. We
also measure a 0.00% success rate of a record-replay attack against
this participant’s model.

10

USING SYNTHETIC DATA TO TESTATTACKS AT SCALE

Our user study demonstrated that ElectricAuth was accurate inverifying each of the 13 participants and robust against any attacks
in that scale. However, gaining insight into how ElectricAuth would
perform in larger deployments (e.g., 100’s of users) is impractical bymeans of user studies at an early stage. To shed light into this, we explore a data-driven approach to evaluate ElectricAuth’s robustness
against impersonation attacks using synthetic data.
Procedure. We followed the recent approach of generating synthetic data by training deep generative models, which is shown to
produce diverse and natural data (e.g., objects [69], human faces [3,89], faces with emotions [50], and physiological data including ECG,
EEG, and so forth [25]) beyond the training set. Specifically, weused the PixelCNN++ model [69], a state-of-the-art deep generative model for images (since we treat each response as an image).
Following [69], we trained a generative model for each legitimate
user in our experiment #2 (see Section 7.2), using the impersonatorresponses collected for this user (12 subjects and 115 challenges),conditioned on the challenge. Once trained, the generator produces
random, natural variations of the training data, emulating responsesof potential impersonators beyond our user study. We validated
each generator using the well-known negative log likelihood (NLL),
which produced results on par with (and often slightly better than)those reported by [69] on object/face images. This indicated that
our trained generators are able to learn and follow the actual data
distribution rather than overfitting to the training data.
Results: robustness against synthetic impersonators. For
each of the 13 users in our experiment #2, we used the corresponding generator to produce 1075 impersonator responses against thisuser. These include 100 synthetic impersonators for each of 5 randomly selected challenges, and 5 additional impersonators for eachof 115 challenges. We then tested these impersonator responses
on ElectricAuth’s authentication model for this user (i.e., the same

User Authentication via Electrical Muscle Stimulation

authentication model used in our experiment #2). All impersonatorresponses were rejected (i.e., 0% FAR at 5% FRR). This result aligns
with our user study results, and sheds lights on ElectricAuth’s
robustness against impersonation attacks at larger scales.

11

CONCLUSIONS, APPLICATIONS &FUTURE WORK

We proposed, implemented and evaluated the use of electrical muscle stimulation (EMS) as a novel modality for active biometrics. We
engineered an interactive system, which we called ElectricAuth,that stimulates the user’s forearm muscles with a sequence of electrical impulses (i.e., an EMS challenge) and measures the user’s
involuntary finger movements (i.e., response to the challenge). The
key idea behind ElectricAuth is that it leveraged EMS’s intersubject
variability, i.e., the same electrical stimulation results in different
movements in different users because everybody’s physiology is
unique (e.g., differences in bone and muscular structure, skin resistance and composition, etc.). Moreover, we demonstrated that
ElectricAuth is secure against data breaches and replay attacks, as
it never reuses the same challenge twice in authentications ś the
key property that allowed ElectricAuth to achieve this is that injust one second of stimulation our system was able to encode oneof 68M possible challenges.

11.1 Potential applications
We believe that ElectricAuth is applicable to a range of interactive
scenarios in which users authenticate without needing to memorize
passwords or PINs. We believe this is of special interest for devices
that natively offer motion tracking or finger tracking, such as forvirtual reality (which we illustrated in Figure 1 using the Oculus
Quest), smartwatch-based interaction [52, 83, 90] or even leveraging a smartphone’s built in IMUs. Furthermore, we believe ourapproach is of particular interest for accessibility scenarios, suchas authentication for users with motor-impairments (e.g., spinal
cord injury, arguably the most impactful application of EMS in the
medical domain [61]) but with intact musculature.

11.2 Future work
We believe this first exploration of EMS for user authentication
provides fertile grounds for exploring subsequent challenges andopportunities: (1) while we have shown ElectricAuth worked wellon the full set of 112 length-2 challenges and a subset of 115 length6 challenges, growing the size of a challenge might enable new
applications, as such, research is needed to demonstrate that thisapproach works across an even larger set of challenges and over alonger time period; (2) while ElectricAuth worked well on the 13
participants from our user studies, more physiological research isneeded to deepen understanding of EMS’s intersubject variability;(3) while ElectricAuth worked well on the controlled wrist posture, more investigation is required to understand its performance
under other postures and their impacts; lastly, (4) as new EMS
systems emerge from the medical domain (e.g., higher resolution
electrode arrays [33, 36, 65], implanted devices [64], and so forth),a system like ElectricAuth will likely improve in wearability andperformance, which will require further investigations.

CHI ’21, May 8–13, 2021, Yokohama, JapanACKNOWLEDGMENTS
We thank the anonymous reviewers for their constructive feedback.
We also thank Michael Maire for his suggestions on synthetic data
generation, and Yuxin Guan for her help on data analysis. This
work is supported in part by the National Science Foundation grant
CNS-1949650. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors anddo not necessarily reflect the views of any funding agencies.

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