2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
October 1-5, 2023. Detroit, USA

Dynamic hand proprioception via a wearable glove with fabric sensors

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | 978-1-6654-9190-7/23/$31.00 ©2023 IEEE | DOI: 10.1109/IROS55552.2023.10342129

Lily Behnke1 , Lina Sanchez-Botero1 , William R. Johnson III1 , Anjali Agrawala1 , and Rebecca Kramer-Bottiglio1

required for sensing, while maintaining properties traditional
to garments.
Fabric-based electronics allow the tight coupling of technology into traditional garments [20]–[22]. We previously
introduced fabric-based strain sensors that can be easily integrated into garments while maintaining properties native tofabrics, including breathability and washability [23]. Whilefabric-based technologies have been implemented toward
fine motor motion monitoring, limitations in fabric-based
sensing gloves still remain with respect to the quantificationof accuracy, minimalistic design, and the conservation ofproperties inherent to textiles [12]–[14].
This work adapts our previously developed fabric sensor
to measure the fine motions of the hands. We detail the
fabrication process of a fabric sensing glove and demonstrate
the glove’s ability to accurately estimate joint angles compared to ground truth from a motion capture system. The
use of the fabric sensor in the form of a glove improves
upon current solutions that include bulky, cumbersome, anduncomfortable components. This work seeks to demonstrate
the manufacturing and quantified accuracy of a fully fabricsensing glove toward dynamic hand proprioception.

Abstract— Continuous enhancement in wearable technologies
has led to several innovations in the healthcare, virtual reality,
and robotics sectors. One form of wearable technology is wearable sensors for kinematic measurements of human motion.
However, measuring the kinematics of human movement isa challenging problem as wearable sensors need to conformto complex curvatures and deform without limiting the user’s
natural range of motion. In fine motor activities, such challenges
are further exacerbated by the dense packing of several joints,
coupled joint motions, and relatively small deformations. This
work presents the design, fabrication, and characterizationof a thin, breathable sensing glove capable of reconstructing
fine motor kinematics. The fabric glove features capacitive
sensors made from layers of conductive and dielectric fabrics,
culminating in a non-bulky and discrete glove design. This
study demonstrates that the glove can reconstruct the joint
angles of the wearer with a root mean square error of 7.2
degrees, indicating promising applicability to dynamic posereconstruction for wearable technology and robot teleoperation.

I. INTRODUCTION
Wearable systems invite tangible interactions with robots
in contexts such as virtual reality (VR), augmented reality(AR), and teleoperation [1]–[3]. Recent developments inwearable technology have incorporated soft sensing mechanisms for kinematic measurements [4], [5]. However, human motion invokes challenges associated with complex
curvatures and form factors to which rigid systems do notcomply. Soft sensors present a promising solution for thechallenges associated with measuring human kinematics dueto their deformability and robustness under strain [6], [7].
Notwithstanding the advantages of soft sensors, fine motor
joints present unique challenges for kinematic estimation due
to their small angular displacements, coupled joint motions,and number of joints in close proximity.
Many sensing gloves have been designed for applications
ranging from VR to robotics [8]–[19]; however, current
solutions include bulky mechanical components and complex wiring systems that impede motion and cause discomfort [9]–[11]. Other gloves include elastomer-based sensors
that prevent the overall breathability and washability of the
glove [15]–[19]. Here, we present a wearable sensing glove
made entirely of fabrics to minimize the amount of material

II. D ESIGN AND FABRICATION
A. Capacitive Strain Sensors
We previously introduced a fabric capacitive strain sensor
for integration with commercially available garments forgross motor motion monitoring [23]. This sensor is madefrom multiple layers of conductive fabric (76% Nylon and
24% elastic fiber, Less EMF Inc. Cat. #A321) separated bydielectric layers (Nylon 4-way stretch fabric, 80% Nylon
and 20% Spandex), which are adhered to each other witha breathable thermoplastic adhesive film (Bemis Associates,Inc.). Full fabrication details can be found in our previous
work [23]. The mechanics of the sensor are modeled afteran ideal parallel plate capacitor.
Ad
As the sensor is stretched, the overlapping area between the
conductive electrodes increases and the dielectric thickness
decreases, thereby increasing the sensor’s capacitance.
C = εε0*This work was supported by a NSF 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)
1 Schoolof Engineering and Applied Sciences, Yale University, New Haven, CT, USA email:{lily.behnke,

B. Glove Fabrication
The design and fabrication of the glove are shown in Figure 1. Ten capacitive fabric sensors capture the motion of themetacarpophalangeal (MCP) and proximal interphalangeal(PIP) joints of each finger. The distal interphalangeal (DIP)

lina.sanchez-botero, will.johnson,
anjali.agrawala,rebecca.kramer}@yale.edu
978-1-6654-9190-7/23/$31.00 ©2023 IEEE

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Fig. 1. (a) Photograph of the sensorized glove with the capacitive sensors along the middle phalanges of each finger to capture the movement of themetacarpophalangeal and proximal interphalangeal joints. Inset shows a close-up schematic of the cross-section of the sensors in the 5-layer capacitive
sensor configuration. Fabrication steps of the sensorized glove (b) Top layer (common ground) and bottom layer of the glove were laser cut into desired
shapes. (c) The extended shapes of the top layer were inserted through the cutouts of the bottom layer. (d) Placement of inner electrodes to sense themovement of both the metacarpophalangeal and proximal interphalangeal joints. (e) Second dielectric layer overlay. (f) Alignment and sewing of the top
and bottom hand silhouettes. (g) Electrodes were folded to complete the 5-layer configuration of the capacitive sensor. The glove was turned inside out,
and an elastic wristband was sewn on (see photograph in (a)). Capacitive sensor values were digitized using an MPR121 connected to an Arduino Uno.

joint is excluded since its motion is coupled to the motion of
the PIP joint and it possesses a limited range of motion [24].
Each sensor is directly integrated into the garment rather thansewn into a commercially available glove. The entire top
layer of the glove acts as a common electrical ground for allof the sensors, and the bottom layer serves as one of the twodielectric layers within each sensor, as shown in Figure 1a.
The sensors reside on the underside of the glove’s top layer,
which, when worn, allows each sensor to rest directly on
top of the finger. By fabricating the sensors as a part of
the garment itself rather than attaching them onto one, we
minimize the amount of material added, improving comfort
and reducing any mechanical restriction of finger motion.

together at 160◦ C. The glove was then sewn together and thesensors were folded over and adhered to complete the fivelayer configuration (Figure 1f-g). Wire leads were attached toeach electrode within the glove, and one more was connectedto the base of the top layer, which is the common ground.
These leads were sewn to the base of the glove closest to
the wrist so as to not interfere with motion. Following the
fabrication of the glove, a cuff was sewn onto the wrist of
the glove with a Velcro fastener (Figure 1a). This cuff acts
as a stabilization mechanism to prevent shifting and slippingof the glove during motion.

The layout of the sensors with respect to a single finger isshown in Figure 1a. The sensors were sized to each finger
joint in order to accurately capture its motion. To fabricate
the glove, the top outer electrode layer and bottom dielectric
layers were laser cut first, then the top layer of the glove
was looped through the bottom patterned layer of the glove
to act as the common ground and first dielectric layer of eachsensor (Figure 1b-c). Next, the remaining inner electrode anddielectric layers of each sensor were added to the existingbase ground and dielectric (Figure 1d-e). The breathable
adhesive and a heat press were used to adhere all the layers

To investigate the response of the sensor, electromechanical characterization of the unit was performed both in freespace and with on-hand boundary conditions. The free spacecharacterization of the sensor was performed via a uniaxial
tension test using a materials testing system (Instron 3345)
at a rate of 5 mm/s with a 0.2 N preload. Raw capacitance
was measured using an LCR meter (E4980AL, KeysightTechnologies). The sensors were loaded into the Instron suchthat their initial gauge length was the distance between theclamps. Strain limiting tabs were placed on either end of
the sensors to prevent strain where the grips of the InstronIII. U NIT C HARACTERIZATION

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middle, ring, and pinky fingers, while the motion of thethumb is unique. Strain limiting tabs were placed to outline
the gauge length of the sensors, as done in the free spacecharacterization. The integrated sensors were pre-stretched
to 100% strain 10 times to expose the sensors to the sameamount of plastic deformation as the free space sensors.
During data collection, the joint being characterized was
moved into the frame of the motion capture system (PhaseSpace, Inc.) at a neutral horizontal (zero-degree) position. The
respective joint was bent to the maximum range achievable,
held for three seconds, and then returned to the neutral horizontal position. Capacitance was measured with a commercial capacitive sensing breakout board (MPR121; Adafruit)and an Arduino Uno, and the capacitance measurements weresynchronized with the motion capture measurements using
the Robot Operating System (ROS).
The four subplots in Figure 3 relate the normalized change
in capacitance to joint bending angle (θ ) for four different
joints. The top row shows the relationship between thebending angle (θ ) and normalized change in capacitance for
the PIP joints of the thumb (in red) and pointer finger (inblue) while the bottom row represents the same relationshipfor the MCP joints. In these bound-unit characterizations,the sensors are subjected to pressure effects in addition to
axial strain, introducing additional nonlinearities and giving
the curves a different shape than the free space experiments.
This phenomenon is especially apparent in the sensors on
PIP joints, which are subjected to greater compression frombending over the PIP joint and fingertip [30]. The PIP joints
are also subjected to larger ranges of θ than the MCP joints.
In general, the change in capacitance imposed by the
PIP and MCP joints is much smaller than the change incapacitance observed in free space. A comparison between

Fig. 2. Average normalized change in capacitance vs. strain for five fabricsensors. The capacitance of the sensors was recorded using the materials
testing system (Instron 3345) and an LCR meter (E4980AL, KeysightTechnologies) at an excitation frequency of 1 kHz.

clamped the sensors (shaded regions in Figure 2 inset). Fivesensors were pre-stretched to 100% of their initial gauge
length 10 times each before testing to account for the plastic
deformation resulting from the initial strain [25].
Figure 2 shows the free space electromechanical characterization of the unit sensors. The dimensions of the sensor are
shown in the top right inset. The shaded regions of the sensor
schematic represent strain-limiting tabs while the remaining
portion of the sensor is the gauge area. The vertical axis
shows capacitance values normalized with respect to thecapacitance of the gauge of each sensor since the strain
limiting tabs did not stretch throughout the duration of theuniaxial tension test. The lower strain region (ε < 10%) ishighlighted in the top left corner of the graphic. This regime
shows the expected operation region of the sensors resulting
from the small displacements they will experience on thehand. The blue line demonstrates the normalized change incapacitance as a function of strain for the sensor with anerror cloud representing the standard deviation of five samples. Elastomer-based capacitive strain sensors [26] exhibit alinear signal response because the dielectric elastomer is an
isotropic material. In contrast, the sensors in this work havea nonlinear signal response due to the fabrics’ anisotropy
and inherent changes in the fabric’s mesostructure during
deformation [27]–[29].
Following free space characterization, further characterization to evaluate the effects of on-hand boundary conditions was performed. In contrast to the previous free-spacecharacterization, strain in the on-hand characterization was
attributed to joint bending and the associated pressure points.
As such, change in capacitance with respect to joint bend
angle was measured rather than strain. Using the fabrication
process previously described, a sample glove was fabricatedwith sensors only spanning the pointer finger and thumb.
The pointer and thumb were selected because it is assumed
that the motion of the pointer finger is representative of theFig. 3. Average normalized change in capacitance vs. bending angles of
the PIP (top) and MCP joints (bottom) of the thumb (red) and the pointerfinger (blue). Error cloud represents one standard deviation.

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Fig. 4. Calibration of average relative change in capacitance vs. bending angles of the PIP (top row) and MCP joints (bottom row) of the thumb shown inred, index finger shown in blue, middle finger shown in yellow, ring finger shown in green, and pinky finger shown in pink. The insets depict the reference
axis (zero-degree axis) defined for each joint bending angle to serve as ground truth.

the observed normalized change in capacitance with respect
to bending angle (θ ) can be mapped to strain in the regimeof 0-10% in Figure 2.
IV. S YSTEM C HARACTERIZATION
To map the corresponding change in capacitance of eachsensor to joint bend angle for the fully fabricated glove,
data correlating these metrics were obtained. The same datacollection process discussed above using motion capture was
replicated for the fully fabricated glove system to calibrate
the relationship between capacitance and ground truth joint
angles from the motion capture system. Six trials were taken
for each respective joint with the glove being removed andre-worn between trials to account for variations caused by
the shifting placement of the glove expected in a practicalapplication. Following the completion of the data trials, angle
data representing the flexion of the joints from the neutral
axis were extracted and aligned with the capacitance data.
The final calibration curve for each sensor on each joint ispresented in Figure 4, where the markers represent the mean
and the error cloud represents the standard deviation. The xaxis of each subplot refers to the angle defined in each inset
diagram.
TABLE IE RROR BETWEEN MEASURED AND ACTUAL JOINT ANGLES

FingerPointer
Middle
Ring
Pinky
Thumb

Joint
MCP
PIP
MCP
PIP
MCP
PIP
MCP
PIP
MCP
PIP

Mean Error (degrees)
5.399
8.794
4.498
9.486
5.045
5.010
7.972
7.359
3.096
5.305

Standard Deviation (degrees)
4.326
7.574
3.742
5.679
4.378
4.237
7.250
4.715
2.352
3.294

The pressure effects can be observed in the sensor response in Figure 4, especially for the PIP joints, which iscongruent with the bound-unit characterization results shownin Figure 3. Overall, the PIP joint data are similar in bothmagnitude and trend. The MCP joint data are shown inthe bottom row of Figure 4. The MCP joint of the middle
finger has the most prominent protrusion and curvature when
flexed, so there is a greater pressure imposed on that sensor
at higher bend angles, resulting in a more sharply increasing
capacitance value at higher angles. There is a tapering effectfor the MCP joint of the pinky at higher joint angles. The
pinky MCP joint protrudes the least of any joint on the glove.
Therefore, it is not surprising that the resulting change incapacitance is relatively low with a small range.
Following the calibration of the system shown in Figure 4, further experimentation was performed to determine
the accuracy of the glove. Data was acquired to obtainground truth joint bend angle and a measured joint bend
angle was calculated from the capacitance value recorded
during motion. These calculated angles were compared to
the ground truth angle measurement to assess the accuracyof the glove. Similar to previous modes of data acquisition,a motion capture system (PhaseSpace, Inc.) was used totake another data trial for each joint. The mode of datacollection remained the same as the calibration step exceptthat there was no extended hold at the maximum flexion
point; instead, there was a constant motion between the zeroand maximum joint bend angle. The resulting capacitancemeasurements were then used to predict the joint angle
compared to the ground truth angle from the motion capturesystem. A nearest-neighbor interpolation model was applied
using each of the calibration curves outlined in Figure 4 as
the known relation to calculate the measured angle directlyfrom capacitance. The resulting mean error and standard
deviation between the measured and ground truth angles arereported for each joint in Table I. The thumb MCP joint
demonstrates the lowest mean error (3.096 degrees) while themiddle PIP joint has the highest mean error (9.486 degrees).
Figure 5 shows the resulting ground truth versus measured

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V. D EMONSTRATION
Following the quantification of the accuracy of the glove,
we sought to visually present the joint bend angles directlyfrom the glove’s capacitance readings. To demonstrate theaccuracy and utility of the fabric sensor glove, we dynamically reconstructed the pose of a hand in Euclidean
free space. The corresponding segmented images from thereal-time reconstruction of the moving hand are shown in
Figure 6. A demonstration of varying gestures was invokedthrough the use of American Sign Language spelling out“YALE.” The top row shows the actual position of thehand while the bottom row shows the reconstruction of thehand with the intended letter from the capacitance values
recorded from each sensor during motion. Figure 6 shows
similar matching between the intended position and thereconstruction. Throughout each gesture, the thumb is themost inconsistent when compared to the actual form factorof the hand. Due to the number of degrees of freedom of thethumb and its complex motions, this is an expected result.
While this work only characterized the motion of the thumbwith respect to a single plane, it paves the way for greater
data acquisition yielding more advanced reconstructions in
the future.
VI. C ONCLUSION
Fig. 5. Plots of ground truth and measured pose angles for (a-b) middle
MCP joint, and (c-d) middle PIP joint.

With this research, we sought to fabricate, calibrate, andquantify the accuracy of a fabric sensing glove. The fabrication demonstrated an array of ten capacitive fabric sensors
with minimal infrastructure, such that the full natural motion of the hand remains intact. Free-space characterization
demonstrates the electromechanical response with respect touniaxial strain. Bound-unit characterizations performed on
the hand for the pointer finger and thumb demonstrated theeffects of coupled strain and localized pressure points when
the sensor is applied to finger joints. The PIP and MCP joint
sensors exhibited monotonic, nonlinear signal responses. Onhand calibration of the whole glove shows a repeatable andrecognizable change in capacitance with respect to joint bend
angle for all joints. Overall, the system demonstrates theability to reconstruct joint bend angles with a root meansquare error of 7.2 degrees. Finally, the glove was used toreconstruct dynamic hand poses in American Sign Language
using the output capacitance values from the sensors.

angles for the joints with the highest (middle PIP) andsecond-lowest (middle MCP) reported mean errors. Although
the thumb MCP shows the lowest mean error, we plot the
middle MCP joint instead because it has a greater range of
motion. The ground truth and measured angles for the joints
over five flexion cycles are shown as a function of time inseconds in Figure 5a,c while in Figure 5b,d the measured
angle is plotted vs. ground truth angle (the error cloud shows
the standard deviation). The one-to-one mapping between themeasured and actual angles confirms the accuracy and utilityof the sensors in our glove application.
Figure 5a,c shows that the model is under-predicting the
maximum value of θ at the peak and is most accurate during
dynamic motions, which could be an effect of small amounts
of noise present in the sensor when held at a constant value.
Further, the calibration step did not account for angles above
the defined neutral axis, and thus any motion corresponding
to a negative θ is not accurately estimated. We suspect thatsuch negative θ values resulted from the hand not being held
directly perpendicular to the plane in which the analysis was
performed or from joint hyperextension. Future work will
aim to include these more complex motions with extended
calibration. Finally, we note that the timescale of the datataken for the MCP joint is slightly longer than that of the
PIP joint, which points toward future work regarding theeffect of rate on accuracy and sensitivity.

Fig. 6.
Pose reconstruction (top row) and corresponding photographs
depicting the intended hand positions for the letters spelling the word “Yale”
in American Sign Language (ASL) (bottom row).

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This work has demonstrated the dynamic reconstructionof the PIP and MCP joints of the hand using a fabricsensing glove. Future work aims to improve the calibration
by including a broader range of motions, accounting for
hyperextensions and more degrees of freedom. The uniquemotion of the thumb requires further characterization in
multiple planes for accurate reconstruction of its complex
movements. Furthermore, the development of this technology
at multiple scales to account for a range of hand forms
and sizes may call for further characterization beyond thescope of what is presented here. Leveraging machine learningalgorithms would aid in increasing the robustness of thesystem from a modeling perspective. We have demonstrated aform-fitting, lightweight, and comfortable sensing glove that
accurately and dynamically monitors human hand motion
toward VR applications and gesture-controlled robots.

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ACKNOWLEDGMENT
This research was funded by the National Science Foundation through IIS under grant no. IIS-1954591. We thankDr. Huanbo Sun for helpful discussions.
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