RESEARCH ARTICLE
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Stretchable, Breathable, and Washable Fabric Sensor for
Human Motion Monitoring
Lina Sanchez-Botero, Anjali Agrawala, and Rebecca Kramer-Bottiglio*
In situ collection of human motion datais crucial for advancing the current stateof a broad range of disciplines including human–robot interactions,[2] virtual
reality,[3,4] sports performance,[5,6] andpersonalized health monitoring [7] andrehabilitation.[8] Most commonly, human motion data is collected via useof optical, electromagnetic, and inertial
measurement unit (IMU) motion capture systems.[9] Although optical motion
capture systems—consisting of multiple cameras oriented around a subject—
are broadly considered the gold standard for high accuracy,[10] these systems
are susceptible to measurement errors
and loss of analyzable data due to occluded lines of sight.[9,11] Such setups
limit the spatial volume of the analysis and necessitate controlled laboratoryenvironments.[9,10,12] In contrast, electromagnetic motion capture systems employ sensors that provide measurements
without requiring lines of sight.[9,13]
However, this method is still limited
to use in controlled settings because
electromagnetic interference from thesurrounding environment can lead tomeasurement errors.[9,13] Like electromagnetic motion capture
sensors, IMUs can be mounted onto the subject and provide measurements using only the onboard gyroscope and accelerometer.
While the use of IMUs is not limited to controlled laboratory settings, they exhibit positional drift in long-term measurements[10]and are made of rigid components, which limit user comfort.
The development of soft strain sensors has shown promise forcircumventing the existing challenges of traditional motion capture systems and enabling unobtrusive integration of human motion monitoring. Typically, such soft strain sensors employ conductive composites with ﬁllers such as carbon black,[14,15] carbon
nanotubes,[16–18] metallic nanoparticles,[19] silver nanowires,[3,20]
graphene,[21,22] liquid metals,[23–26] and/or ionic ﬂuids[27,28] to create electrodes. Silicone-based elastomers such as polydimethylsiloxane (PDMS), Ecoﬂex, and Dragon Skin[29] usually serve as
both insulating host materials and highly stretchable substrates.
The use of such compliant materials allows soft sensors to conform to curvilinear surfaces (e.g., elbows, knees) and withstandthe everyday skin deformations of human joints (in the range of
40%–55% strain[30–33] ) without impeding natural motion. Most
soft strain sensors transduce uniaxial mechanical deformation

Wearable strain sensors for movement tracking are a promising paradigm toimprove clinical care for patients with neurological or musculoskeletal
conditions, with further applicability to athletic wear, virtual reality, andnext-generation game controllers. Clothing-like wearable strain sensors can
support these use cases, as the fabrics used for clothing are generally
lightweight and breathable, and interface with the skin in a manner that ismechanically and thermally familiar. Herein, a fabric capacitive strain sensor
is presented and integrated into everyday clothing to measure humanmotions. The sensor is made of thin layers of breathable fabrics and exhibitshigh strains (>90%), excellent cyclic stability (>5000 cycles), and high water
vapor transmission rates (≈50 g/h m2 ), the latter of which allows for sweat
evaporation, an essential parameter of comfort. The sensor’s functionality is
veriﬁed under conditions similar to those experienced on the surface of thehuman body (35°C and 90 ± 2% relative humidity) and after washing withfabric detergent. In addition, the fabric sensor shows stable capacitance at
excitation frequencies up to 1 MHz, facilitating its low-cost implementation in
the Arduino environment. Finally, as a proof of concept, multiple fabricsensors are seamlessly integrated with commercial activewear to collectmovement data. With the prioritization of breathability (air permeability andwater vapor transmission), the fabric sensor design presented herein paves
the way for future comfortable, unobtrusive, and discrete sensory clothing.

1. Introduction
In recent years, a growing interest in wearable electronic devices
has turned continuous health monitoring into an achievable andmainstream concept, with revolutionary implications for humanhealth, safety, and performance. While wearable commercial devices currently allow users to monitor physiological data such asheart rate, skin conductance, and respiration patterns,[1] there is
still a need for noninvasive human motion monitoring systems
capable of capturing the body strains involved in everyday activities.

L. Sanchez-Botero, A. Agrawala, R. Kramer-Bottiglio
School of Engineering & Applied Science
Yale University
17 Hillhouse Ave, New Haven, CT 06520, United States
E-mail: rebecca.kramer@yale.edu
The ORCID identiﬁcation number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/admt.202300378

DOI: 10.1002/admt.202300378

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Figure 1. Fabric sensor overview. a) three-layer construction. b) ﬁve-layer construction. Scale bars: 54 mm. c) Sensors integrated into garments forhuman motion monitoring.

into a change in either electrical resistance or capacitance. Resistive strain sensors have demonstrated higher sensitivities thancapacitive strain sensors overall,[14,29,32] but many also show limited electromechanical robustness, hysteresis, and low sensing
stability due to crack formation and mechanical damage at highstrains.[33] Since the sensor response of most capacitive sensors
relies on the overlapping area of electrodes, capacitive sensors
generally show more linear and stable behavior,[32] both of which
are particularly important in human motion monitoring.
Although there are many capacitive strain sensors in existing literature,[29,33,34] including previous works from our lab,[35–37]
there remain several key ongoing challenges in the ﬁeld regarding breathability, maximum measurement frequency, andsensor-garment integration. Here, we present a wearable capacitive sensor that addresses these identiﬁed limitations.
First, despite the prevalence of elastomer-based sensors in theliterature, such sensors compromise thermophysiological andskin sensorial comfort due to the low air permeability and water
vapor transmission of elastomers. Recent works have started exploring the use of fabrics to improve sensor comfort. Prior workby Atalay et al.[38] introduced a soft parallel-plate capacitor constructed using conductive fabric as electrodes and a silicone layer
as the dielectric material. Although this sensor uses fabric as theouter exposed (conductive) layers, the internal silicone dielectric
layer limits the overall breathability of the sensor. Another capacitive sensor by Park et al.[39] uses silicone to bind fabric layers suchthat fabrics serve as both the electrode and dielectric materials.
In both of these works, as well as most wearable electronics andsensor literature, the breathability of the sensor materials was
not characterized. To maximize wearer comfort and safety, andencourage real-world usage, we sought to create a reliable strain
sensor made of entirely conductive and non-conductive fabrics
bound together with thin ﬁlms of breathable thermoplastic fabric adhesive (Figure 1a,b).
Second, prior work has established that electrode resistancecan aﬀect the maximum frequency at which capacitance canbe accurately measured, with characteristic frequencies around
5 kHz.[36] In this work, we demonstrate stable capacitance measurements up to 1 MHz, such that our sensor data can be easily

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read using standard Arduino hardware. The ﬁdelity of the sensor
response at high frequencies indicates its suitability for broadertranslation into soft robotics applications.
Third, our fabric sensor can be directly embedded into commercial activewear, even using the garments as the dielectric layerof the sensors, thus overcoming existing challenges of bulky attachment modes and sensor detachment and/or slippage.[40,41]
By using fabrics and porous layers that oﬀer a unique combination of ﬂexibility, stretchability, and breathability, our aim is to prioritize sensor wearability and user tactile comfort (as measured
by air permeability and water vapor transmission) in a way thatexisting elastomer-based sensors do not. In this paper, we characterize sensor performance using three common fabrics—cotton,polyester, and nylon—as the dielectric materials to demonstrate
the respective advantages of each. We further report the sensor’s cyclic stability, frequency dependence, electromechanicalresponse to temperature and humidity, and washability. Alongwith its functional beneﬁts, the textile sensors are fabricated using a simple, highly reproducible, and low-cost stacked assembly method, which allows their seamless integration into commercial clothing to facilitate the collection of reliable human motion data.

2. Results and Discussion
2.1. Sensor Overview
The capacitive strain sensors shown in Figure 1a,b consists ofconductive fabric electrodes separated by dielectric fabric layers.
Each layer is stacked and aﬃxed with breathable adhesive ﬁlm.
Flexible wires are used to interface the sensors with external
data acquisition electronics (Arduino Pro mini and MPR121
Adafruit breakout circuit). We tested both three- and ﬁve-layer
sensor conﬁgurations (Figure 1a,b; sensor dimensions shown in
Figure S1). In the ﬁve-layer conﬁguration, the external electrodeis connected to ground, which reduces parasitic capacitance andshields the sensor, therefore making the device more suitable forcontact with human skin. The characteristics of the constituent
sensor materials and the straightforward fabrication process

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allow seamless sensor integration into existing knitted garments.
The result of this integration is a sensory garment capable ofmonitoring the movement of body joints (Figure 1c).

2.2. Air Permeability and Water Vapor Transmission
Characterization
Comfort is one of the most critical components of modern wearable devices, however, this feature is often overlooked in the development of new wearable sensors. For fabrics, tactile and thermophysiological comfort is related to the breathability of thematerial.[42] Thus, to evaluate the breathability of our sensor, wetested the air permeability and water vapor transmission rate
(WVTR) of the sensor’s constituent fabrics both with and without thermoplastic adhesive (Figure 2a,b). Our sensors are constructed using knit fabrics—a medical-grade conductive nylon
for the electrodes, and three diﬀerent fabrics for the dielectric
layers: nylon, polyester, and cotton. Air permeability was measured according to the ASTM 737–18 procedure,[43] which determines the volume rate of air ﬂow per unit area of fabric. Boththe conductive fabric (Figure 2c) and the dielectric nylon fabric(Figure 2d) exhibit a warp-knit tricot structure (Figure S2, Supporting Information), with air permeability values of 2253.3 and414 l/m2 s, respectively. Although both the conductive and the dielectric nylon fabrics have the same knit structure, the dielectricnylon fabric exhibits a tighter knit (and thus a lower air permeability) relative to the more open structure of the conductive fabric. In contrast, the polyester (Figure 2e) and cotton (Figure 2f)
dielectric fabrics exhibit a weft-knit jersey structure (Figure S2,
Supporting Information) with air permeability values of 183 and439 l/m2 s, respectively. As polyester is the heaviest and thickest of the fabrics we tested, it exhibits the lowest air permeability (Figure S2 and Table S1, Supporting Information). Additional fabric characteristics such as ﬁber hydrophilicity,[44] yarncount,[45] weave or knit structure,[46–48] fabric thickness,[49,50] andfabric porosity[51] have also been shown to aﬀect air permeability
and water vapor transmission.
We refer to fabrics coated with the thin ﬁlm adhesive (athermoplastic polyurethane fabric tape; morphology shown in
Figure S3, Supporting Information) as laminated fabrics. Onceadhered to the fabrics, the adhesive visually presents as a porous
membrane (third column in Figure 2c–f). As a result, the air permeability of the laminated fabrics is reduced compared to theirbare fabric counterparts (Figure 2a). On average, the air permeability of laminated nylon and polyester is 163 and 129 l/m2 s,
respectively. These values constitute a respective reduction by≈ 60% and ≈ 30% relative to the bare samples. Laminated cotton
exhibits a reduction in permeability of only ≈ 15%. In contrast,
laminated conductive nylon exhibits a reduced permeability of≈ 97.2%, which is attributed to the reduced porosity shown in
Figure 2c.
Although the air permeability of the laminated fabrics was reduced relative to the bare fabrics, the laminated dielectric fabrics
all showed air permeabilities greater than 100 l/m2 s, which falls
within the range of normal clothing breathability as reported by
Havenith et al.[52] On average, the laminated conductive fabricshowed an air permeability slightly less than this value (62 l/m2 s).
However, the order of attachment of the adhesive to the fabricAdv. Mater. Technol. 2023, 8, 2300378

likely plays a role in the porosity of the fabric-bonded adhesive
and air permeability of the overall composite. Thus, attaching theadhesive to the dielectric fabric ﬁrst enables the air permeabilityof composite layers to be greater than 100 l/m2 s.
Water-vapor permeability is another key physical property offabrics aﬀecting breathability since the loss of water vapor is
crucial for the wearer’s thermal equilibrium and physiological
comfort.[53] Measurements show high WVTRs for all the bare fabrics with average values between 45 and 51 g/h m2 . All laminatedfabrics exhibit similarly high WVTR, with average values between
38 and 41 g/h m2 (Figure 2b). The laminated fabrics behave as
porous membranes with WVTRs higher than the rate of transepidermal water loss (TEWL) of adult skin under normal conditions(5–10 g/h m2 ) and within the range of TEWL during sweating (6–66 g/h m2 ). Our samples also have WVTRs higher than those of
non-porous 8 μm[54] and highly porous (45%) 40 μm[55] ﬁlms ofpolydimethylsiloxane (5–6 and 20.3 g/h m2 , respectively), which
are elastomers commonly used in wearable devices. Therefore
the data cumulatively suggest that fabric lamination with the adhesive ﬁlm has a minimal blocking eﬀect on moisture permeability.

2.3. Electromechanical Characterization
Although the three-layer conﬁguration for capacitive sensors isthe most widely used in electrical and robotic applications, theﬁve-layer conﬁguration is most suitable for wearable applications
in contact with the human skin. In the ﬁve-layer conﬁguration,
the external electrode acts as an active shield when connected to
ground, mitigating parasitic and environmental interference factors and resulting in a high ﬁdelity signal. In the three-layer conﬁguration, while operational in wearable applications, direct skincontact with the signal electrode may lead to shorting and losses
in the electrical signal. We evaluated the strain sensing performance of the three- and ﬁve-layer sensors with nylon, polyester,or cotton dielectric layers by monitoring the relative change incapacitance, ΔC/C0 , during uniaxial tensile strain, 𝜖. While therelation between capacitance and strain monotonically increases
in all cases, we observed a degree of non-linearity in the measured
curves (Figure 3a–c for ﬁve-layer sensors; Figure S4a–c, Supporting Information, for three-layer sensors). It is important to notethat the relative change in the capacitance response to strain forboth three- and ﬁve-layer sensors are comparable (see the curves
overlap in Figure S5, Supporting Information), showing that increasing the area of one electrode in the ﬁve-layer conﬁguration
seems not to aﬀect the sensor response to deformation. The nonlinearity in the relative capacitance of our sensors can be explained by changes in the mesostructure of the fabric dielectric
layer under strain, such as reduction of the porosity,[56] partialalignment of the ﬁbers,[57] and compressive deformation.[58,59]
For the purpose of our analysis, we deﬁne three linear strain regions: 𝜖 < 25%, 25% <𝜖 < 50%, 𝜖 > 50%.
The sensitivity, S, in each strain region is deﬁned by the linear
𝛿(ΔC∕C0 ). Similar segmented linearity analyses haveﬁt slope:𝛿𝜀been used in nonlinear capacitance responses to deformationin pressure sensors with highly structured dielectric layers.[60–62]
Figure 3a–c shows that all three sensor types increase in sensitivity with increasing strain. The polyester sensors exhibit the2300378 (3 of 12)© 2023 Wiley-VCH GmbH2365709x, 2023, 17, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/admt.202300378 by Yale University, Wiley Online Library on [10/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

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Figure 2. Constituent sensor material breathability and morphological characterizations. a) Air permeability and b) water vapor transmission rate ofbare and laminated fabrics (i.e., fabrics coated with the adhesive). An average of three samples is shown for each. c–f) SEM images of (c) conductive
nylon, (d) nylon, (e) polyester, and (f) cotton. Columns from left to right: the front side of the bare fabrics, ﬁber morphologies, and laminated fabrics.

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Figure 3. Electromechanical characterizations of ﬁve-layer fabric sensors. a–c) Average relative change in capacitance as a function of strain for ﬁve
sensors with dielectric (a) nylon, (b) polyester, and (c) cotton. Three sensitivity regimes are shown. d–f) 5000 strain cycles to ≈ 60% for a representative
sensor with dielectric (d) nylon, (e) polyester, and (f) cotton. g–i) Average change in capacitance as a function of frequency for ﬁve sensors with dielectric(g) nylon, (h) polyester, and (i) cotton.

highest sensitivity (S = 0.74 for 𝜖 < 25%, S = 1 for 25% <𝜖 < 50%,
S = 1.46 for 𝜖 > 50%). The nylon sensors exhibit a similar, thoughslightly lessened, sensitivity (S = 0.5 for 𝜖 < 25%, S = 0.75 for
25% <𝜖 < 50%, S = 1.23 for 𝜖 > 50%). The cotton sensors exhibit
the lowest sensitivity (S = 0.2 for 𝜖 < 25%, S = 0.61 for 25% <𝜖 <50%, S = 1.2 for 𝜖 > 50%). Both the nylon and polyester sensors
exhibit sensitivity values that are comparable to prior textileinclusive capacitive sensors. For example, Atalay et al.[38] reporta sensitivity value of 1.23 up to 100% strain, and Geng et al.[63]report a sensitivity of 0.7 up to 30% strain. The suppressed sensitivity of the cotton sensors can be explained by several factors.
Cotton is the least elastic of the dielectric fabrics, with a spandex
percentage of only 5%, compared to 20% for nylon and polyester
fabrics. Although the thicknesses of the cotton and nylon dielectric fabrics are comparable, the weight of the cotton fabric is thelowest among the dielectric fabrics, with fewer courses and wales
per inch (see Table S1, Supporting Information). Thus, the reduced sensitivity of the cotton sensors is likely a combined resultof the ﬁber content, fabric thickness, and the dielectric properties

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of the cotton ﬁbers. While dielectric properties of fabrics aremainly deﬁned by the ﬁber’s polymer composition, in our case,nylon, polyester, and cotton, secondary parameters, such as yarn
structure and fabric construction, have also shown substantial
eﬀects on the fabric’s dielectric behavior. For further reading, we
refer readers to a recent review on fabric dielectric properties.[64]We note here that segmented linearity is one approach tomodeling the overall non-linear capacitance response to deformation. However, continuous non-linear models may be helpful to further predict sensor performance for a wide range ofsensor designs. As it is known that the Poisson’s ratio of elastic
and porous systems is dependent on strain,[58] we speculate that
there further exists a dependence between the dielectric properties of the sensors to strain, as the fabric’s microstructure undergoes compression during stretch inducing changes in the effective dielectric constant. Similar results have been observedin microstructure capacitive pressure sensors where the eﬀective dielectric constant changes with the displaced air in the dielectric layer upon compression.[60,61,65] By introducing these two

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strain-dependent parameters, Poisson’s ratio and eﬀective dielectric constant, we propose a non-linear empirical model, given in
Note S1 and Equation S19 (Supporting Information). The nonlinear model predictions are in agreement with experimental data
for nylon and polyester sensors, thereby providing a ﬁrst validation of the changes in capacitance for porous dielectric materials
such as fabrics, even under diﬀerent environmental conditions(see Figure S17 and Table S2, Supporting Information). However,further investigation is required to understand the capabilities
and robustness of the proposed empirical model.
The stress-strain behavior of the three- and ﬁve-layer sensors with nylon, polyester, or cotton dielectric layers is shownin Figure S6 (Supporting Information). The ﬁve-layer nylon
and polyester sensors showed maximum stresses of 0.80 and
1.11 MPa at 85% and 87% strain, respectively, while both thethree- and ﬁve-layer cotton sensors exhibited stresses of ≈2 MPa
at 82% strain. The observed mechanical responses are comparable to those of elastomeric strain sensors[66,67] and their constituent materials.[68]The cyclic stability of the sensors was assessed via 5000 loading
cycles with applied strain between 5% and 60%. All sensor types
completed the test without failure (Figure 3d,e for ﬁve-layer sensors; Figure S7, Supporting Information, for three-layer sensors).
The insets in Figure 3d,e provide a detailed view of the capacitance changes of representative sensors during tensile stretchingfor ten consecutive cycles (from the 2500th to the 2510th cycles).
Repeatability and reliability can be observed for the polyester
sensor, which shows a stable relative change in capacitance of
ΔC∕C0 ≈ 0.43% at ≈ 60% strain (Figure 3e). The nylon sensor exhibited a small drift during the cyclic test with ΔC∕C0 ≈ 0.51% at≈ 60% strain during the ﬁrst cycle and ΔC∕C0 ≈ 0.45% at ≈ 60%
strain for the 5000th cycle (Figure 3d). The cotton sensor hada short transient regime during the cyclical testing, reaching astable absolute value of ≈ 0.2% for ΔC/C0 after several hundred
loading cycles. This observed settling may be related to the slower
gradual rearrangement of the cotton fabric network during the
beginning cycles.[69] The spandex percentage in the cotton fabric is only 5%, compared to 20% for nylon and polyester fabrics,
resulting in more plastic deformation in the cotton sensors (seeFigure S8, Supporting Information). Additionally, the highly hygroscopic nature of cotton may further aﬀect its mechanical[70,71]
and dielectric[64,72] properties, as ﬁber rearrangement and deformation induce changes in exposed surface area during cyclic testing.
The dependence between capacitance and excitation frequencyof the manufactured sensors was investigated in the frequency
range from 20 Hz to 1 MHz at room temperature (Figure 3g–i).
The measured capacitance of both unstrained (0%) and strained(55%) sensors rapidly decreases at low frequency values, which
can be explained by the dielectric dispersion of the fabrics. In
polar polymers such as cotton, nylon, and polyester, at high frequencies of the applied electric ﬁeld, the electric dipoles do nothave time to align before the ﬁeld changes direction, leading
to a decrease in permittivity and, therefore, capacitance.[64] As
the applied frequency increases, the capacitance response for nylon and polyester sensors becomes almost independent of frequency, while for cotton sensors a monotonic decrease of thecapacitance as a function of frequency was observed. At thesame relative humidity (RH) conditions, cotton will have a higher

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moisture content than nylon and polyester fabrics due to its
hygroscopicity.[70,71] The higher content of bound water in cotton may further aﬀect dielectric permittivity,[64,72] resulting in amore monotonic frequency sweep curve.
The performance metrics of wearable polymer-based[32] andtextile-based[33] strain sensors have been rigorously summarized
in recent literature reviews. Herein, we provide the performance
results necessary to benchmark our textile sensor using the comparison tables and references included in these reviews. Relative to the two polymer-based capacitive sensors summarized
by Souri, et al.,[32] our sensor boasts a favorable response time
though less favorable gauge factor, stretchability, and linearity.
Further, we introduce air permeability and water vapor transmission rate as new metrics that have not been previously reported
for wearable sensors. Electromechanical characterizations relating to response time (Figure S9, Supporting Information), hysteresis (Figure S10, Supporting Information), and strain rate dependency (Figure S11, Supporting Information) are shown in
Supporting Information.

2.4. Electromechanical Dependence on Temperature and
Humidity
We investigated the eﬀects of temperature and humidity on theelectromechanical response of our nylon and polyester ﬁve-layer
sensors using our materials testing system (Instron 3345) outﬁtted with an environmental chamber (ETS, Model 5500-8485).
The nylon and polyester sensors were chosen for further characterization over the cotton sensors due to their higher sensitivity, greater cyclic stability, and reduced frequency dependence.
The sensors were tested in three conditions: 1) ambient humidity (51 ± 3% Relative Humidity (RH)) and room temperature (RT)(24 ± 1°C); 2) high humidity (90 ± 2% RH) and RT; and 3) highhumidity (90 ± 2% RH) and high temperature (35 ± 1°C). After
conditioning the sensors in each temperature and humidity setting for at least 3 h, the sensors were manually pre-stretched toremove any Mullins eﬀect. Sensors were then strained to 55% of
their new gauge length after the manual pre-stretch. Both sensor types showed a monotonically increasing relative capacitancewith strain in all conditions (Figure 4a,b). Thus, our sensors remain functional in high moisture settings without requiring additional silicone encapsulation that would increase their weight,
hinder their integration into clothing, and result in the loss ofbreathability and fabric feel.
While retaining function, the sensitivity of the sensors was impacted by the environmental conditions (Figure S12, Supporting
Information). The hydrophobic and hygroscopic properties of thefabrics used as dielectric layers in the sensors resulted in diﬀerent electrical responses with changing humidity. At higher relative humidities, ﬁbers will absorb moisture from the environment and have higher moisture contents, ﬁlling air voids within
the ﬁbers and in the porous fabric structure. In this process, therelative permittivity of the fabric increases because the permittivity of water (ϵr = 78 at 2.45 GHz and 25°C)[73] is much higher thanthat of air (ϵr ≈ 1).[73,74] This eﬀect is seen in the overall increase
in sensor capacitances at higher humidity levels (Figures S13 andS14, Supporting Information). At 0% strain, the increase in sensor capacitance of the ﬁve-layer nylon sensors at higher humidity

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Figure 4. Eﬀect of temperature, humidity, and washing on the electromechanical response of ﬁve-layer sensors. a,b) Average relative change in capacitance as a function of strain, under varying temperatures and humidities, for ﬁve sensors with dielectric (a) nylon and (b) polyester. c,d) Eﬀect of washingon the electromechanical response of ﬁve sensors with dielectric (c) nylon and (d) polyester.

(≈50 pF) is greater than that of the ﬁve-layer polyester sensors(≈24 pF), reﬂecting the greater hydrophobicity and lower moisture uptake of polyester relative to nylon. Within the tested strain
range, it is also evident that the magnitude of capacitance change(ΔC) is greatest in ambient humidity for both nylon and polyester
sensors, further contributing to the reduced sensitivity of thesensors at high humidity (Figure S13, Supporting Information).
Sensor capacitance showed more susceptibility to humidity thantemperature (Figure S15, Supporting Information). However, we
also observed an increase in sensor sensitivity at higher temperatures. We attribute this increased sensitivity to increased dryingof the sensors at higher temperatures, which would reduce theirwater uptake and partially counteract the eﬀects of higher humidity. Further investigation is needed to decouple the eﬀects of thesevariables from strain to enable robust and reliable motion tracking.
The sensors were also washed with fabric detergent, thendried and tested three times in room conditions (24 ± 1°C and
51 ± 3%). The electromechanical response of both the nylon andpolyester ﬁve-layer sensors showed a slight decrease in sensitivity after the initial wash cycle, but no noticeable changes after
repeated wash cycles (Figure 4c,d). Previous literature has used
SEM imaging to verify that washing silver nanoparticle-coatedknitted fabrics reduces the concentration of conductive nanoparticles on ﬁber surfaces, resulting in decreased conductivity.[75]Thus, the mechanical and frictional forces involved in the ﬁrst
wash cycle may have led to a decrease in the conductivity of theelectrode fabric and a subsequent drop in the sensitivity of thesensors. During the washing process, the ﬁbers in the fabric lay-Adv. Mater. Technol. 2023, 8, 2300378

ers also experienced axial and transverse swelling. During drying, the contact network between ﬁbers may have changed, affecting the tightness of the knit structures and the permittivity
properties of the dielectric fabrics. Nevertheless, the repeatability of sensor performance with repeated washes supports the hygienic reusability of the sensor.

2.5. Wearable Applications of the Capacitive Strain Sensor
Monitoring human activity is a key component of advancing thepromising ﬁelds of human-machine interactions (HMI) and personal healthcare. The stretchability, signal ﬁdelity, and permeability of our capacitive strain sensors allowed monitoring oflarge-range human motions by placing six sensors on each of
the main human joints—elbows, hips, and knees. The six sensors were seamlessly integrated into commercial, nylon-based
compression garments using the same breathable adhesive usedin the sensor construction (described further in §4.6). The garment itself served as one of the dielectric layers in the ﬁvelayer sensor structure, with the second dielectric layer made ofan additional layer of nylon (Figure 5a). The volunteer wearing the sensory garment was asked to perform diﬀerent compound body movements such as squats, sit-to-stand, and stepups. The distinct motions of the joints were unambiguously reﬂected in the capacitance changes of all six sensors. The measurements were also reproducible, without any obvious loss ofthe capacitive signal during repeated movements. For instance,
when the volunteer performed a set of 10 squats, the capacitive

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Figure 5. Sensory smart garment capable of monitoring the movement of body joints. a) Sensor placement on the knees, elbows, and hips of thegarments. Photographs and capacitance responses of the sensors during the following human motions: b) squats (10 cycles), c) sit–to–stand cycles(10 cycles) d) step–ups (10 cycles) and e) retrieving an object from the ﬂoor (2 cycles).

response of the sensors exhibited several peaks (Figure 5b,ii) andvalleys (Figure 5b,i) in the plot corresponding to the bending
motions of the six joints under monitoring. The capacitive response of all body-mounted sensors increased when the wearer
was gradually squatting down, and remained nearly constantas long as the joints remained static (see Video S1, Supporting
Information).
Another validation experiment involved tracking the volunteer’s movement while sitting in a chair (Figure 5c). As the participant was sitting and leaning back on the chair’s backrest, a postural modiﬁcation of the arms—a swaying motion—was noticeablein every cycle of the test. These observations are reﬂected in thedata acquired by the sensors located on the elbow joints. During
these actions, the elbows’ ﬂexion and swaying motions resulted
in a characteristic double peak in the capacitive signal. Similarly,
as the participant leaned forward to stand up, the arms’ extension
resulted in valley-shaped signals. We also observed that the dou-Adv. Mater. Technol. 2023, 8, 2300378

ble peak signals were diﬀerent in every cycle and the intensity ofthe signal increased as the motion range increased. Moreover, thesensor’s signals for the lower body (i.e., hips and knees) display
peak-and-valley signals that can be correlated to the bending of
the joints observed during the squat motion (see Video S2, Supporting Information).
The capacitive strain sensors were used to diﬀerentiate rangesof human motions during a step-up exercise (Figure 5d). During
the step-up movement, the volunteer was asked to place his rightfoot onto the black box and then bring his left foot up until he
was standing on the box with both feet. He was then asked to step
down ﬁrst with the right foot, and then with the left foot so both
feet were on the ﬂoor. The asynchronous movement of the legs
during the step-up movement was distinct in the data acquiredby the sensors located in the hips and knees joints. The produced
capacitive signal during ﬂexion and extension movements exhibited a multi-peak pattern that was repeatedly observed during all

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the cycles of the movement. In addition, the elbow joint-mounted
sensors exhibited small but noticeable capacitive responses thatreﬂected the diﬀerent, subtle swaying motions of the elbow joints
during each cycle (see Video S3, Supporting Information).
Characterization of common human motions (e.g., picking up
objects from the ﬂoor or holding a cup while drinking water)
may provide useful information for the treatment of some movement disorders.[76–78] As a demonstration of applicability to theseapplications, the volunteer was tasked with picking up a paper
cup, drinking from it, and ﬁnally returning the cup to the ﬂoor
(Figure 5e). The signals from knee- and hip-mounted sensors displayed a repetitive increase and decrease in capacitance resulting
from the successive ﬂexion and extension of the joints during thesquat-like movement involved in picking up and returning theobject from and to the ﬂoor. The elbow joint-mounted sensors
also exhibited varying responses matching the diﬀerent motionsof the elbow joints. Overall, the capacitive signal increased withthe bending degree of the elbow and returned to its initial value
when the arm recovered its initial extended position. Thus, when
the left arm is slightly bent during the pick-up movement, the left
elbow sensor outputs an increased signal. This increase in capacitance was then followed by a drop to its initial value when the volunteer returned to the standing position and ﬁnally, by anotherslight increase as the volunteer returned the object to the ﬂoor.
Simultaneously, the right elbow sensor exhibits a three-peak signal, with a ﬁrst peak corresponding to the arm bending during
the pick-up movement. This increase in capacitance, however, ismore intense compared to the left elbow because the right arm
ﬂexes to a greater degree. The second peak corresponds to thearm ﬂexion during the drinking movement and the third peak
results from the slight bending movement of the right arm as
the volunteer returns the object to the ﬂoor (see Video S4, Supporting Information).

3. Conclusion
In this work, we leverage the characteristics of commonly worn
fabric materials to introduce a sensing technology explicitly designed for comfort and long-term functionality in real-world human motion monitoring. The materials and sensor designs presented here serve as a foundation for skin-interfaced wearable
sensing technologies, enabling the creation of sensory garments
capable of recording physiological movements with high signal
ﬁdelity. The air permeability and water vapor transmission properties of the materials used allow the sensor to be highly breathable, which is crucial for maintaining thermophysiological comfort, a characteristic often neglected in wearable systems. Our
sensor has not only demonstrated a strain-sensing range, sensitivity, and cyclic performance comparable to other state-of-theart soft strain sensors, but it also allows for easy integration withcommercial activewear, retaining a comfortable clothing-like feel.
The easy and low-cost implementation of the fabric sensors in
the Arduino environment, as well as the adaptability and customization of the manufacturing process, allows the technology’s
rapid deployment for the detection of motion of large joints (elbows, hips, and knees) and potentially smaller joints (e.g., ﬁnger joints). We further propose a relation to predict the sensor’s
relative change in capacitance as a function of its elastic properties, dielectric properties, and environmental factors such as

Adv. Mater. Technol. 2023, 8, 2300378

temperature and humidity. Future work will focus on the characterization of positional drift and accuracy to enable in situ longterm motion monitoring. Adapted versions of our sensors canbridge the gap between skin-sensor interfacing to facilitate thetranslation of these technological advances to sports medicine
and clinical settings addressing a broad spectrum of conditions,
including movement disorders, knee osteoarthritis, and running
injuries.

4. Experimental Section
Materials: Medical grade conductive fabric (76% Nylon and 24% elastic ﬁber, Cat. #A321) was purchased from Less EMF Inc. Nylon 4-way
stretch fabric (80% Nylon and 20% Spandex) and stretch cotton jersey
fabric (95% Cotton and 5% Spandex) were purchased from Amazon.
Polyester-Lycra Spandex fabric (710LY) was purchased from Paylessfabrics. The thermoplastic polyurethane-based adhesive ﬁlm was producedby Bemis Associates Inc. (3410 Sewfree Tape).
Sensor Fabrication: The sensor electrodes were cut in a dogbone shape(dimensions given in Figure S1, Supporting Information) from the knit
conductive fabric using a laser (VLS 3.50, Universal Laser Systems Inc) at
70% intensity and 50% speed. For polyester and nylon dielectric layers, thelaser settings were also set at 70% intensity and 50% speed for 2 passes.
Cotton was cut with 1 pass with the laser settings at 100% intensity and at
100% speed. The thermoplastic adhesive ﬁlms used for lamination werelaser cut with the adhesive facing up at 90% intensity and 100% speed using 1 pass. Sensors were produced using a stacked assembly method. The
three-layer capacitive sensor consists of a pair of conductive electrodes
separated by a dielectric layer. First, the dielectric material was laminatedwith thermoplastic adhesive ﬁlm on either side (3410 Sewfree Tape), followed by the application of the fabric electrodes on each side of the dielectric. It should be noted that one of the electrodes has a smaller width toprevent shorting of the electrodes. Similarly, for the ﬁve-layer sensor, theinternal stacked structure consists of one small electrode and two dielectric layers. This assembly was then encased by one big external electrodeforming two more layers in the stacked structure (see Figure S1, Supporting Information), with the external electrode connected to ground. All lamination sequences were performed at 160 °C using a heat-press machinefor 30 s. The sensors were then interfaced with an LCR meter (E4980AL,
Keysight Technologies) using a ﬂexible silicone-sheathed wire (30 AWG)
attached to the electrode fabrics (Figure 1). Strain-limiting custom-built
tabs made of adhesive laminated woven fabric were attached at each endof the sensor’s dogbone shape to facilitate wire interfacing and clamping
during the electromechanical tests.
Electromechanical Characterization: Each tested specimen was cyclically pre-stretched 10 times to 100% strain (original stretchable gauge
length, L0 = 106 ± 2 mm) to remove the Mullins eﬀect and achieve aﬁxed level of plastic deformation. After the pre-stretching cycles, the newstretchable length of the specimens was registered as the new gauge
length. Sensors were then stretched to their original stretchable length(106 ± 2 mm), which was approximately 82%–88% strain of the new registered L0 , at a rate of 5 mm/s using the materials testing system (Instron3345).
The capacitance of the sensors was recorded with the LCR meter(E4980AL, Keysight Technologies) at an excitation frequency of 1 kHz. The
measured capacitance was adjusted to represent only the capacitance ofthe stretchable area by subtracting the capacitance of the stationary tab
areas from the LCR measurements. The capacitance of the tab areas wascalculated as a percentage of the initial capacitance C0 using the relative
size of the tab areas reported in Figure S1 (Supporting Information).
Excluding the three-layer cotton sensors, the averaged response of ﬁve
sensors was shown for each dielectric material and sensor conﬁguration(three- or ﬁve-layer). For the three-layer cotton sensors, the averaged
response of three sensors was shown due to sensor shorting during
testing. The bands shown represent the standard deviations of theaveraged responses from all the tested sensors. Unless otherwise noted,

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all electromechanical characterizations of the sensors were performed at
a temperature of 24 ± 1°C and relative humidity of 51 ± 3%.
Dynamic electromechanical characterization of the sensors was carriedout through 5000 cycles of straining up to 60% using a custom-built cyclic
tester, as described by Porte and co-authors.[36] For cyclic testing, representative data from one sensor was selected for each dielectric materialand sensor conﬁguration.
Sensor frequency sweep testing was performed using the frequency
sweep function of an LCR meter (Keysight E4980A/AL). The excitation
frequency ranged from 20 Hz to 1 MHz. Measurements were performed
while the sensor was stationary at 0% and 55% strain and averaged forﬁve sensors.
Humidity and temperature dependence of the sensors’ electromechanical properties were investigated with a materials testing system (Instron3345) equipped with a custom-built environmental chamber (Model 5500
- 8485, ETS). For humidity tests, the sensors were left inside the environmental chamber for at least 3 h prior to testing and an average response ofﬁve sensors of each type was shown. Subsequent testing was performed
at 90 ± 2% RH, and two diﬀerent temperatures, 35 and 25 °C, to simulate
sweating conditions.
Materials Characterization: The morphology of the fabrics was investigated using a scanning electron microscope Hitachi SU8230 UHR coldﬁeld emission. Air permeability of the bare and laminated fabric samples
was measured according to the standard test method for textile fabrics(ASTM D737), using an air permeability tester (SDL Atlas MO21A) with atest area of 20 cm2 and at a constant pressure drop of 200 Pa. Laminatedfabric samples were tested with the adhesive ﬁlm against the bottom plate.
The water vapor transmission rate (WVTR) was determined according to
the standard test (ASTM E 96), using a WVTR Analyzer (Mocon AQUATRAN 3) with a cup diameter of 2.5 inches. Samples undergoing air permeability and WVTR tests were pre-conditioned at a temperature of 21 ± 1° C
and a relative humidity of 65 ± 2% according to the standard describedin ASTM D1776. The thickness of fabric samples was measured using aparallel presser digital caliper. Photographic images of the fabrics were
captured using a handheld USB digital microscope with LED illumination(plugable UTP200X020MP). All measurements were repeated three times.
Washability: The washing test of the sensors was conducted at room
temperature by diluting 3 mL of a commercial neutral detergent (TexCare,#A289-L) into 1000 mL of deionized (DI) water at a pH 6, and the subsequent continuous stirring for 30 min. After this, the textile sensors wererinsed with DI water and dried overnight in an oven at 60°C followed bya conditioning step at a temperature of 24 ± 1°C and relative humidityof 51 ± 3%. The washing procedure was conducted three times. The electromechanical properties of the washed sensors were monitored after eachwashing-drying cycle. For each dielectric material, the electromechanicalresponse was reported as an average of ﬁve sensors.
Manufacturing of Conformable Sensory Bodysuit and Data Acquisition: A
sensory bodysuit was manufactured to characterize integration and performance at the human-sensor interface. Commercial form-ﬁtting garments
were utilized to manufacture the sensory bodysuit consisting of a men’scompression long-sleeve T-shirt (Under Armour) and a pair of men’s leggings (Willit sports). Six sensors with the same dogbone shape were heatpressed into the garments, with the garment’s fabric serving as one ofthe dielectric layers of the ﬁve-layer sensor structure (see Figure S1, Supporting Information). With the exception of the garment fabric, all otherlayers of the sensors were cut using the same laser settings described in§4.2. First, fabric electrodes were interfaced with ﬂexible silicone-sheathedwire (30 AWG) before the sensor construction. The garment was then laminated with thermoplastic adhesive (3410 Sewfree Tape), followed by theapplication of the inner fabric electrode. Then, a second dielectric layer was
stacked and attached with the same thermoplastic adhesive. After this,a small slit was cut in the garment to wrap the larger external electrodearound the sensor, forming the last two layers in the ﬁve-layer sensor (seeFigure S1, Supporting Information). Finally, strain-limiting custom-built
tabs made of adhesive laminated woven fabric were attached at each endof the sensor’s dogbone shape to facilitate wire interfacing and to cover
the slit made in the garment in the previous step. All manufacturing sequences were performed at 160 °C using a heat-press machine for 30 s. The

Adv. Mater. Technol. 2023, 8, 2300378

sensors were positioned at the major joints—elbows, knees, and hips—todetect the motion of the upper and lower limbs. No additional calibration
process or manufacturing adjustments were required to achieve the sensor responses shown in these demonstrations. The agreements between
each pair of sensors on the same type of joint were achieved on the ﬁrst
attempt of sensor integration and testing. The change in capacitance versus time was measured using a commercial capacitive sensor breakout
board (MPR121, Adafruit) and an Arduino Pro mini using the CoolTerm
application for data acquisition.

Supporting Information
Supporting Information is available from the Wiley Online Library or fromthe author.

Acknowledgements
L.S.-B. and A.A. contributed equally to this work. The authors thank Prof.
Holly Yanco and Cheryl A. Gomes, with the Fabric Discovery Center at
UMass Lowell, for their assistance in collecting permeability data; Min Li
from the west campus materials characterization core at Yale University
for access to characterization equipment; and Dr. Dylan Shah and Dr. Elze
Porte for valuable discussions regarding the capacitive sensors and applications of this work. This material was based upon work supported by the
NSF under grant no. IIS-1954591. The study received IRB approval (Protocol ID 2000031654, approved by the Human Investigation Committee
2B with the Yale Human Research Protection Program) and appropriate
guidelines were followed. The proof-of-concept demonstrations includedin this work were conducted in accordance with the ethical principles of
the Declaration of Helsinki. Informed consent for participation and publication was obtained from the volunteer.

Conﬂict of Interest
The authors have declared no conﬂict of interest.

Data Availability Statement
The data that support the ﬁndings of this study are available from the corresponding author upon reasonable request.

Keywords
fabric sensors, soft robotics, soft sensors, textile sensors, wearable electronics
Received: March 9, 2023
Revised: May 15, 2023
Published online: July 7, 2023

[1] P. Nogueira, J. Urbano, L. P. Reis, H. L. Cardoso, D. C. Silva, A. P.
Rocha, J. Gonçalves, B. M. Faria, J. Med. Syst. 2018, 42, 101.
[2] V. G. Motti, in Wearable Interaction, Springer, Berlin, 2020.
[3] M. Amjadi, A. Pichitpajongkit, S. Lee, S. Ryu, I. Park, ACS Nano 2014,
8, 5154.
[4] S. Patel, H. Park, P. Bonato, L. Chan, M. Rodgers, J. Neuroeng. Rehabil.
2012, 9, 21.
[5] E. Mencarini, A. Rapp, L. Tirabeni, M. Zancanaro, IEEE Trans. Hum.
Mach. Syst. 2019, 49, 314.

2300378 (10 of 12)© 2023 Wiley-VCH GmbH2365709x, 2023, 17, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/admt.202300378 by Yale University, Wiley Online Library on [10/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

www.advancedsciencenews.com

www.advmattechnol.de[6] S. Coyle, D. Morris, K. T. Lau, D. Diamond, N. Moyna, in Proc. IEEE
6th Int. Workshop on Wearable and Implantable Body Sensor Networks,
Berkeley, CA, USA 2009, pp. 307.
[7] K. Guk, G. Han, J. Lim, K. Jeong, T. Kang, E. K. Lim, J. Jung, Nanomaterials 2019, 9, 813.
[8] S. M. De Rossi, N. Vitiello, T. Lenzi, R. Ronsse, B. Koopman, A.
Persichetti, F. Giovacchini, F. Vecchi, A. J. Ijspeert, H. van der Kooij,
M. C. Carrozza, in IEEE Annual International Conference of the IEEE
Engineering in Medicine and Biology, 2010, p. 1279.
[9] B. R. Hindle, J. W. Keogh, A. V. Lorimer, Appl. Bionics Biomech. 2021,
2021, 6628320.
[10] D. Kim, J. Kwon, S. Han, Y. L. Park, S. Jo, IEEE/ASME Trans. Mechatron. 2019, 24, 56.
[11] G. B. Guerra-Filho, Rev. Inform. Teor. Apl. 2005, 12, 61.
[12] J. Taborri, J. Keogh, A. Kos, A. Santuz, A. Umek, C. Urbanczyk, E. vander Kruk, S. Rossi, Appl. Bionics Biomech. 2020, 2020, 2041549.
[13] E. van der Kruk M. M. Reijne, Eur. J. Sport Sci. 2018, 18, 806.
[14] J. Shintake, E. Piskarev, S. H. Jeong, D. Floreano, Adv. Mater. Technol.
2018, 3, 1700284.
[15] W. Yi, Y. Wang, G. Wang, X. Tao, Polym. Test. 2012, 31, 677.
[16] M. Nankali, N. M. Nouri, M. Navidbakhsh, N. G. Malek, M. A.
Amindehghan, A. M. Shahtoori, M. Karimi, M. Amjadi, J. Mater.
Chem. C 2020, 8, 6185.
[17] X. Hu, F. Yang, M. Wu, Y. Sui, D. Guo, M. Li, Z. Kang, J. Sun, J. Liu,Adv. Mater. Technol. 2022, 7, 2100769[18] C. Choi, J. M. Lee, S. H. Kim, S. J. Kim, J. Di, R. H. Baughman, Nano
Lett. 2016, 16, 7677.
[19] J. Lee, S. Kim, J. Lee, D. Yang, B. C. Park, S. Ryu, I. Park, Nanoscale
2014, 6, 11932.
[20] Y. Yang, S. Duan, W. Xiao, H. Zhao, Sens. Actuators, A 2022, 343,
113653.
[21] J. Li, G. Zhang, R. Sun, C. P. Wong, in Three-Dimensional GrapheneBased Composite for Elastic Strain Sensor Applications, Cambridge University Press, Cambridge, Vol. 1, 2016, pp. 2415–2420.
[22] H. R. Na, H. J. Lee, J. H. Jeon, H. J. Kim, S. K. Jerng, S. B. Roy, S. H.
Chun, S. Lee, Y. J. Yun, npj Flexible Electron. 2022, 6, 2.
[23] Y. Wu, Y. Zhou, W. Asghar, Y. Liu, F. Li, D. Sun, C. Hu, Z. Wu, J. Shang,
Z. Yu, R.-W. Li, H. Yang, Adv. Intell. Syst. 2021, 3, 2000235.
[24] D. Zhang, J. Zhang, Y. Wu, X. Xiong, J. Yang, M. D. Dickey, Adv. Intell.
Syst. 2022, 4, 2100201.
[25] J. Chen, J. Zhang, Z. Luo, J. Zhang, L. Li, Y. Su, X. Gao, Y. Li, W. Tang,
C. Cao, Q. Liu, L. Wang, H. Li, ACS Appl. Mater. Interfaces 2020, 12,22200.
[26] C. B. Cooper, K. Arutselvan, Y. Liu, D. Armstrong, Y. Lin, M. R. Khan,
J. Genzer, M. D. Dickey, Adv. Funct. Mater. 2017, 27, 1605630.
[27] A. Frutiger, J. T. Muth, D. M. Vogt, Y. Mengüç, A. Campo, A.
D. Valentine, C. J. Walsh, J. A. Lewis, Adv. Mater. 2015, 27,
2440.
[28] D. Y. Choi, M. H. Kim, Y. S. Oh, S. H. Jung, J. H. Jung, H. J. Sung, H.
W. Lee, H. M. Lee, ACS Appl. Mater. Interfaces 2017, 9, 1770.
[29] M. Amjadi, K. U. Kyung, I. Park, M. Sitti, Adv. Funct. Mater. 2016, 26,
1678.
[30] T. Yamada, Y. Hayamizu, Y. Yamamoto, Y. Yomogida, A. IzadiNajafabadi, D. N. Futaba, K. Hata, Nat. Nanotechnol. 2011, 6,
296.
[31] C. Shi, Z. Zou, Z. Lei, P. Zhu, G. Nie, W. Zhang, J. Xiao, Research 2021,
2021, 9846036.
[32] H. Souri, H. Banerjee, A. Jusuﬁ, N. Radacsi, A. A. Stokes, I. Park, M.
Sitti, M. Amjadi, Adv. Intell. Syst. 2020, 2, 2000039.
[33] S. Seyedin, P. Zhang, M. Naebe, S. Qin, J. Chen, X. Wang, J. M. Razal,
Mater. Horiz. 2019, 6, 219.
[34] S. Z. Homayounfar T. L. Andrew, SLAS Technol. 2020, 25, 9.
[35] E. L. White, M. C. Yuen, J. C. Case, R. K. Kramer, Adv. Mater. Technol.
2017, 2, 1700072.

Adv. Mater. Technol. 2023, 8, 2300378[36] E. Porte, T. Sipple, L. S. Botero, D. Shah, R. Kramer-Bottiglio, in 2021
IEEE 4th Int. Conf. on Soft Robot., RoboSoft 2021, New Haven, CT,
USA 2021, 412.
[37] R. A. Bilodeau, A. M. Nasab, D. S. Shah, R. Kramer-Bottiglio, Soft
Matter 2020, 16, 5827.
[38] A. Atalay, V. Sanchez, O. Atalay, D. M. Vogt, F. Haufe, R. J. Wood, C.
J. Walsh, Adv. Mater. Technol. 2017, 2, 1700136.
[39] H. Park, J. Cho, J. Park, Y. Na, J. Kim, IEEE Rob. Autom. Lett. 2020, 5,3525.
[40] Y. Mengüç, Y. L. Park, E. Martinez-Villalpando, P. Aubin, M. Zisook,
L. Stirling, R. J. Wood, C. J. Walsh, in Proc. IEEE Int. Conf. on Robot.
and Autom., IEEE, Piscataway, NJ, USA 2013, 5309.
[41] Y. Mengüç, Y. L. Park, H. Pei, D. Vogt, P. M. Aubin, E. Winchell, L.
Fluke, L. Stirling, R. J. Wood, C. J. Walsh, Int. J. Rob. Res. 2014, 33,
1748.
[42] E. T. Özkan B. Meriç, Text. Res. J. 2015, 85, 62.
[43] A. S. for Testing M. (ASTM), D737-18, standard test method for air
permeability of textile fabrics.
[44] R. P. Jamdagni, S. Bhattacharya, N. Kumar, ACC J. 2015, 21, 54.
[45] B. Das, A. Das, V. K. Kothari, R. Fangueiro, M. de Araújo, J. Text. Inst.
2009, 100, 588.
[46] D. Y. Limeneh, M. Ayele, T. Tesfaye, E. Z. Liyew, A. F. Tesema, J. Nat.
Fibers 2020, 19, 4148.
[47] A. Bivainytė, D. Mikučionienė,Fibres Text. East. Eur. 2011, 19, 69.
[48] B. Wilbik-Hałgas, R. Danych, B. Wiecek, K. Kowalski, Fibres Text. East.
Eur. 2006, 14, 77.
[49] E. Onofrei, A. M. Rocha, A. Catarino, J. Eng. Fibers Fabr. 2011, 6, 10.
[50] C. Prahsarn, R. L. Barker, B. S. Gupta, Text. Res. J. 2005, 75, 346.
[51] K. K. Kim, I. H. Ha, M. Kim, J. Choi, P. Won, S. Jo, S. H. Ko, Nat.
Commun. 2020, 11, 2149.
[52] G. Havenith H. O. Nilsson, Eur. J. Appl. Physiol. 2004, 92, 636.
[53] M. Cao, Y. Li, Y. Guo, L. Yao, Z. Pan, IEEE Comput. Graphics Appl. 2016,
36, 70.
[54] L. Tian, B. Zimmerman, A. Akhtar, K. J. Yu, M. Moore, J. Wu, R.
J. Larsen, J. W. Lee, J. Li, Y. Liu, B. Metzger, S. Qu, X. Guo, K. E.
Mathewson, J. A. Fan, J. Cornman, M. Fatina, Z. Xie, Y. Ma, J. Zhang,
Y. Zhang, F. Dolcos, M. Fabiani, G. Gratton, T. Bretl, L. J. Hargrove, P.
V. Braun, Y. Huang, J. A. Rogers, Nat. Biomed. Eng. 2019, 3, 194.
[55] M. Seok, S. Yoon, M. Kim, Y. H. Cho, Nanoscale Adv. 2021, 3, 4843.
[56] V. Kumar, V. R. Sampath, C. Prakash, J. Text. Inst. 2016, 107, 1213.
[57] G. Dusserre, L. Balea, G. Bernhart, Composites, Part A 2014, 64, 185.
[58] E. Porte, R. Kramer-Bottiglio, Adv. Mater. Technol. 2021, 6, 2001247.
[59] Z. Laufer, Y. Diamant, M. Gill, G. Fortuna, Int. J. Polym. Mater. Polym.
Biomater. 1978, 6, 159.
[60] S. C. Mannsfeld, B. C. Tee, R. M. Stoltenberg, C. V. Chen, S. Barman,
B. V. Muir, A. N. Sokolov, C. Reese, Z. Bao, Nat. Mater. 2010, 9, 859.
[61] L. Ma, X. Shuai, Y. Hu, X. Liang, P. Zhu, R. Sun, C. P. Wong, J. Mater.
Chem. C 2018, 6, 13232.
[62] Z. He, W. Chen, B. Liang, C. Liu, L. Yang, D. Lu, Z. Mo, H. Zhu, Z.
Tang, X. Gui, ACS Appl. Mater. Interfaces 2018, 10, 12816.
[63] W. Geng, T. J. Cuthbert, C. Menon, ACS Appl. Polym. Mater. 2021, 3,
122.
[64] Y. Yamada, Textiles 2022, 2, 50.
[65] S. R. A. Ruth Z. Bao, ACS Appl. Mater. Interfaces 2020, 12, 58301.
[66] A. Yoshida, Y.-F. Wang, T. Sekine, Y. Takeda, D. Kumaki, S. Tokito, ACS
Appl. Energy Mater. 2022, 14, 5721.
[67] H. Nakamoto, H. Ootaka, M. Tada, I. Hirata, F. Kobayashi, F. Kojima,
IEEE Sens. J. 2015, 15, 2212.
[68] J. C. Case, E. L. White, R. K. Kramer, Soft Rob. 2015, 2, 80.
[69] M. E. Messiry A. Mohamed, J. Text. Inst. 2016, 107, 431.
[70] T. Igarashi, M. Hoshi, K. Nakamura, T. Kaharu, K. I. Murata, J. Phys.
Chem. C 2020, 124, 4196.
[71] S. Yagihara, H. Saito, H. Sugimoto, T. Kawaguchi, M. Fukuzaki, T.
Igarashi, M. Hoshi, K. Nakamura, Asian J. Mater. Sci. 2021, 56, 17844.

2300378 (11 of 12)© 2023 Wiley-VCH GmbH2365709x, 2023, 17, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/admt.202300378 by Yale University, Wiley Online Library on [10/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

www.advancedsciencenews.com

www.advmattechnol.de[72] E. Jayamani, S. Hamdan, M. R. Rahman, M. K. B. Bakri, Procedia Eng.
2014, 97, 536.
[73] R. Salvado, C. Loss, Gon, P. Pinho, Sensors 2012, 12, 15841.
[74] C. L. Ng M. B. I. Reaz, Sensors 2017, 17, 574.
[75] S. Ahmad, K. Subhani, A. Rasheed, M. Ashraf, A. Afzal, B. Ramzan,
Z. Sarwar, J. Electron. Mater. 2020, 49, 1330.

Adv. Mater. Technol. 2023, 8, 2300378[76] N. Jalloul, Biomed. J. 2018, 41, 249.
[77] M. Pau, S. Caggiari, A. Mura, F. Corona, B. Leban, G. Coghe, L.
Loreﬁce, M. G. Marrosu, E. Cocco, Mult. Scler. Relat. Disord. 2016,
10, 187.
[78] I. Carpinella, D. Cattaneo, G. Bonora, T. Bowman, L. Martina, A.
Montesano, M. Ferrarin, Arch. Phys. Med. Rehabil. 2017, 98, 622.

2300378 (12 of 12)© 2023 Wiley-VCH GmbH2365709x, 2023, 17, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/admt.202300378 by Yale University, Wiley Online Library on [10/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

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