Proceedings of the ASME 2018 International Design Engineering
Technical Conferences and Computers and Information in Engineering Conference
IDETC/CIE 2018
August 26-29, 2018, Quebec City, Quebec, Canada

DETC2018-85703

SENSORY GLOVE FOR DYNAMIC HAND PROPRIOCEPTION AND TACTILESENSINGJohn C.S. McCaw, Michelle C. Yuen∗
School of Mechanical Engineering
Purdue University
West Lafayette, Indiana 47907
Email: {mccaw, yuenm}@purdue.edu

Rebecca Kramer-Bottiglio*
School of Engineering and Applied Science
Yale University
New Haven, Connecticut 06510
Email: rebecca.kramer@yale.edu

ABSTRACTThe fields of soft robotics and soft electronics have introduced novel technologies for tracking dynamic pose of the human body. These new flexible sensors use material softness andelasticity to allow for high conformability, low risk of injury or
discomfort, and ease of integration into other flexible materials
such as clothing. Here, we present a glove embedded with soft
capacitive strain sensors for measuring finger bending and fingertip pressure, while leveraging low-cost components. Previous
capacitive sensing approaches have shown limitations in dimensional scalability of the sensor geometry and system-level scalability of sensors, wiring, and networking methods. We overcome
these previous limitations by using sensors with novel geometry
and leverage I2 C communication protocol to reduce the quantityof interfacing wires to four, even for sensor counts on the orderof 103 . We show that the sensory glove is able to capture thesalient features of hand proprioception by sensing the presence
and continuous intensity of touch and finger curvature. Finally,
we demonstrate the application of this glove towards the recognition of the letters comprising the American Manual Alphabet,which is used to augment the American Sign Language, and utilize the glove to perform a teleoperation task.

gesture-based control for teleoperation, state reconstruction of
the hand in virtual and augmented reality, and gesture-based language input. With 27 degrees of freedom, the human hand canserve as a much richer input device relative to devices like joysticks, mice, and keyboards [1]. While many degrees of freedom
enables the high dexterity of human hands, it is also a complex
mechanical system that is difficult to resolve without compromising the natural biomechanics of hand motion. Ideally, a hand
proprioception and tactile sensing system would be able to measure the state of the fingers without interfering with their range ordynamics of finger motion. Furthermore, when considering useof such a device, the overall system would ideally be as physically discrete as possible, so as to be imperceptible to the user.
Developments in soft robotics and high-strain flexible technologies have provided a variety of methods for hand proprioception and tactile sensing, which fit into five primary categories: resistive strain sensors [2–8], capacitive strain sensors [9–11], optical sensors [12–14], inertial measurement units (IMUs) [15–17],
and visual motion capture systems [18, 19]. One of our goals inthis work was to minimize the effect of the wearable device onnatural hand biomechanics. As such, since IMUs are not a ’soft’component, they are not considered herein.
Currently, motion capture systems provide the most accurate
joint angle data with a high degree of repeatability, with recent
work consistently achieving <1% error [18]. However, the space
limitations and obstructions incurred by using motion capture
cameras make it not viable for use in open environments. Additionally, the high cost of motion capture systems makes them1

Introduction
Hand proprioception and tactile sensing offer a wide variety of applications in human-computer interaction, including∗ M.C. Yuen is also affiliated with Yale University.

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inviable in all but a few specialized, high-value applications.
Optical sensors have been shown in sensing gloves to determine joint angle with high degrees of sensitivity, and are notsusceptible to electromagnetic interference [12–14]. However,
due to this sensor type operating on the basis of light intensity
through a fiber-optic cable, they are highly susceptible to damage and require specialized electronics.
Soft strain and pressure sensors utilizing resistive [2–6] andcapacitive [9–11] sensing modalities have been demonstrated ina variety of forms to measure finger bending and contact pressure. Both of these sensing methods rely upon direct deformation of the soft material comprising the sensor body to change
their measured resistance or capacitance. Compared to the othersensing methods, resistive and capacitive strain sensors provide
the benefit of simple incorporation into an electronic circuit.
Deformation-sensitive resistors need only be placed in a voltage divider circuit to provide meaningful output, and similarly,
deformation-sensitive capacitors may be placed in a circuit measured with the time constant of a first order system.
A key difference between resistive and capacitive strain sensors is the dependence on material properties: resistive sensors
are sensitive to changes in the geometry and the resistivity of a
resistive element, while capacitive sensors measure changes inbulk sensor geometry. As a result, capacitive sensors are lesssusceptible to material degradation that can result in long-term
signal drift in resistive sensors, with the added advantage thatmaterial constraints in resistivity are no longer a primary concern. Furthermore, while many issues with material degradationand device failure in high-deformation resistive sensors can be
mitigated with the use of liquid metals and ionic liquids, thesesensors are difficult to fabricate and are often susceptible to failure at the poor physical interface between rigid wires and liquid
conductors.
One concern with hand proprioception is the number ofwires required to capture signals from a large number of sensors required to capture the dynamics of the hand. Traditional
capacitive sensing devices require three lines to operate: ground,power, and signal. Other methods of sensing, such as opticalmethods, require many more wires [14]. To reduce the numberof wires, researchers have incorporated an I2 C communication
protocol to transmit a binary on-off value back to a central processor [9].
In this work, we present a high-resolution, propioceptive
glove functionalized with soft, capacitive strain sensors. The
number of wires on the glove are reduced by implementing the
I2 C communication protocol. the work presented here also extends our prior work [20] by showing that the geometry of theelastomer-based strain sensors can still operate well when scaled
to a very small size and operating in close proximity to charged
bodies (human bodies). Similar to the prior work by Seehra, etal. [17], our glove and sensors are fabricated from low-cost materials. Finally, we demonstrate sign language capture and tele-

FIGURE 1. Images of the sensory glove. (A) Glove worn on a hand,seen from the ventral plane, showing the pressure-sensitive fingertips,(B) the dorsal plane, showing the length of the sensor responsive tobending the fingers, and (C) a perspective angle on a mannequin hand.

operation using this glove.

2 Materials and Manufacture
2.1 Sensor Fabrication
A schematic of the sensor fabrication is shown in Figure 2.
The sensors presented here are composed of platinum-cure silicone elastomer (Dragon Skin 10 Slow, Smooth-On) and a silicone elastomer and expanded graphite composite material. The
sensors are constructed as parallel-plate capacitors, following thesame procedure for film-based sensors as described in [20]. In
brief, to manufacture the conductive electrode layers, expanded
intercalated graphite (EIG) (Expandable Graphite Flakes, SigmaAldrich) was incorporated into the silicone elastomer at a loadingof 10wt% with the aid of an organic solvent (Cyclohexane, VWR
Analytical). The material was cast into a film by rod-coating (1/2
in. Acme Threaded Rod, McMaster-Carr). We allowed the solvent to evaporate and the elastomer to cure. The dielectric layer
was constructed by preparing unmodified silicone elastomer androd-coating over the electrode layer in the same fashion. Before the dielectric layer had fully cured, the assembly was folded
in half resulting in a four layer structure: electrode-dielectricdielectric-electrode.
The individual sensors were laser-cut from the larger sheet(30W CO2 laser; Universal Laser Systems VLS 2.30) using a2
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capacitive sensor, the internal clock is used to record the time ittakes for the capacitor to discharge to a fixed voltage value, dictated by the component values in the circuit. This time is stored
in I2 C accessible memory, and the process continues whenever
the microprocessor is powered on.
This I2 C Protocol is well documented, though a brief
overview of how it is used in this work is included for readers
unfamiliar with its operation. The master device, which in thiscase is the Arduino microcontroller, uses the communicationslines to send out the address of the slave with which it wishes
to communicate. Following this, the master sends the memory
address (on the slave) it wants, which is followed by the slave
sending this information to the master. Because this system uses
7-bit addresses, there are 128 possible sensors, though I2 C systems with 10-bit addresses (1024 addresses) are also possible.

FIGURE 2. Fabrication procedure. (Top left) Expanded intercalated
graphite and silicone elastomer composite is rod-coated into a film. (Topmiddle) After curing, silicone elastomer is rod-coated on top of the composite film. (Top right) The film is folded over onto itself to form the capacitor structure when the silicone elastomer has cured to a tacky stage.
(Bottom right) The film is laser cut into the sensor patterns. (Bottom
middle) The sensors are cleaned, interfaces are attached, and the sensors
are dipped in silicone elastomer to form a thin insulating coat. (Bottom
left) The sensors and custom microprocessor boards are glued on the
glove using silicone elastomer as adhesive. The four I2 C wires, VCC(power), GND (ground), SDA (digital data), and SCL (digital clock),extend from the final sensor to the microcontroller (not pictured).

3

Signal Processing
During operation, we applied a simple exponential filter tothe data passing through the microprocessor with a decay rate,
α = 0.2. Thus, the value recorded and reported here is st = αxt +(1 − α)st−1 , where xt is the current, raw sensor reading, and st−1is the previous filtered value. This filtering method is memorylight (only a single value per sensor is held in memory at any onetime), which is beneficial for wearable systems wherein the goal
is to minimize the footprint of computing hardware and power
supply. Additionally, for further clarity in reporting, a low-pass
FIR filter with Kaiser Window was applied after data collection,
having a passband frequency of 0.2 Hz and a stopband frequencyof 20 Hz attenuated by 20 dB. In the plots presented in this paper,
we normalized the readings by dividing the reading by the initial,
unloaded sensor value across all tests.

custom pattern designed for each finger’s length. To interface
with the capacitance measurement electronics, copper contacts
were attached to each conductive side with laser-cut polystyrenetabs held together with thread, shown in Figure 1B-C. The entire assembly was coated in silicone elastomer for environmental
protection by dipping the sensors into the uncured elastomer andallowing it to flow by gravity until cured.
2.2

Glove Assembly
The polystyrene tabs, shown in Figure 1A, were glued to
the glove with silicone elastomer, and the sensor body was prestrained to 10% before being fixed down with more silicone elastomer at the copper contacts, halfway along their length, and at
the fingertips. The custom microprocessor boards used to measure the capacitances of the sensors (reported in [20]) were fixed
to the glove with cotton thread and wired in parallel to form theI2 C bus. Here, the I2 C master was implemented using an Arduino Pro Mini microcontroller (Adafruit), which sends the dataover UART to a computer running a custom Python script. The
total cost of the materials and electronics involved was less than
$40USD at the time of writing.

4

Sensor Response Characterization
We characterized the sensor response to finger curvature andpressure at the finger pads, and furthermore, demonstrated that
individual sensor responses are independent from one another(i.e., we observe no electrical coupling despite the implementation of the the communication protocol and the close proximityof the sensors). The mechanical response of these sensors tostrain was previously characterized by White, et al. [20], where
the authors found the sensors to have a linear electrical response
to applied strain, to have no observable change in function over1000 cycles and to remain functional through 100,000 cycles.
Further, the sensors are able to function through 250% strain
and return to normal function. Because the behavior of the standalone sensors have been thoroughly studied, we have focusedon characterizing the glove as a whole system, rather than characterizing individual sensors.

2.3

Electronics Operation
The individual custom microprocessor boards operate on thebasis of a fixed-time charging circuit. The positive side of the soft
capacitive strain sensor is allowed to charge for a fixed time, using the internal clock of the microprocessor. After charging the3
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tact with the scale, in addition to the deformation due to applied
load. Contact with objects typically results in an increase in capacitance, regardless of making a conductive connection. The
polarizability of material near the sensor increases the measured
capacitance. As the load increases, the contact between the sensor and the scale is the same as when smaller amounts of load areapplied.
We note here that it is not currently possible for this system to distinguish between finger curvature and applied pressure- both result in an increase in the sensor reading. This is because
the current sensor construction consists of a single soft capacitive stretch sensor that begins before the metacarpophalangeal
joint and wraps around the tip of the finger, terminating on thepad. Future iterations may separate the sensor into two individual
sensors in order to measure both values simultaneously.
4.3

De-coupled Response
Finally, we observed the de-coupled nature of the sensors,
aside from natural biomechanical coupling of the third, fourth,and fifth fingers. Figure 4 shows the bending of individual fingers in succession, and their corresponding sensor output as afunction of time. Some of the regions in this plot show a largemagnitude activation alongside smaller magnitude sensor activations on proximate fingers, due to their biomechanical coupling.
This demonstration shows that the individual sensors are electrically de-coupled, despite their mutual proximity and proximity
to the communication lines.

FIGURE 3. (A) Plots showing the radius of curvature and (B) pressure response characterization of the glove. All error bars represent
mean and 95% confidence bounds calculated from 100 readings at eachmeasurement point. Photo insets show an example of the hand positions
in each experimental setup.

4.1

Curvature Response
Curvature response was determined by grasping cylinders ofdifferent diameters, while minimizing pressure on the fingertips(Figure 3A). We sampled the sensors 100 times at approximately
500 Hz for each radius of curvature. The thumb was positioned
in the opposite direction of the fingers except for the largest two
diameters, for which it was necessary to position the thumb parallel to the fingers. All fingers had similar levels of responseto the applied curvature. At the largest diameters, there is little change in the sensor readings because the fingers lay nearlyflat. With the exception of the smallest two radii of curvature,
the confidence bounds for all fingers at each remaining measurement point are similarly narrow, indicating that the resolution
with which we can distinguish different curvatures remains consistent. Using this data, one can correlate curvature of the fingers
to the sensor reading.

5

Applications
The developed glove shows promise with applications related to gesture-based proprioception, such as control of video
games, gesture-based user interfaces, and capture of manual al-

4.2

Pressure Response
The pressure response of the finger pads, shown in Figure 3B, was determined by applying a force on a scale with thehand and sampling the sensors 100 times at approximately 500
Hz. The plot shows the sensor reading (mean and 95% confidence bound) for each finger as a function of applied load. We
found that all the fingers displayed similar levels of response to
the same load. Furthermore, We note the sharper increase at
smaller loads, and attribute this to the effect of the sensor con-

FIGURE 4. Real-time output of the sensor glove showing individual
finger response. The images above the plot show which finger is being
bent at each region.

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phabet, sign language or natural language gestures. The Peregrine Glove is an example of a glove-based video game controller, but is limited to on/off switches made of conductive traceson the glove [21]. A continuous, analog approach, such as the
glove presented herein, allows the user to further control theiractions beyond on-off switching. For example, a user could send
the game an instruction of a direction and velocity in that direction to move a character. Alternatively, different degrees of joint
angle may be mapped to discrete key codes so multiple, traditional keystrokes may be logged per finger.
The signals from the glove can also be used to map gestures
to text to allow for sign language communication without a teleinterpreter, or similar gesture-to-text or gesture-to-speech tools.
Here, American Sign Language (ASL) will be used as an example since it can be used to communicate text and abstract ideas
with a single hand, such as the American Manual Alphabet andfull words or phrases. To exemplify each, a spelling example
and a single-gesture word are presented. We then demonstrate
the application of this glove towards performing a teleoperation
task by controlling various joints of a robotic arm.
5.1

Capture of American Sign Language
To demonstrate the application of this glove to American
Sign Language, we signed two common categories of signs (Figure 5). One component of American Sign Language is the American Manual Alphabet, which consists of 26 signs that correspond to the 26 letters of the English Alphabet. In Figure 5A,
the operator signs the letters spelling ’BOILERUP’. It is apparent from the plot that the letters here can be clearly distinguishedfrom one another using the five sensors on the glove. It is important to clarify that the set of 26 characters cannot be fully determined by a strain sensor across each finger; some characters
are spatial rotations of others, such as the difference between ’H’
and ’U’, and others have the similar finger curvatures such as’O’ and ’E’ but have different orientations relative to the hand.
Integration of multiple strain sensors per finger to further clarify location of finger curvature, as well as an additional sensor
across the wrist to register hand orientation, would allow for thefull character set to be classified.
A second major component of American Sign Language is
non-alpha-numeric gestures and positions that represent words
and phrases, and are often dependent on the order, speed, andtiming of the motions. To demonstrate the ability of the sensory
glove reported here, a common ASL sign was made that represents the phrase, ’I Love You,’ shown in Figure 5B.

FIGURE 5. Demonstration of American Sign Language recognition.
The plots show the normalized sensor values of all fingers with respect
to time. (A) Individual letters to spell out ’BOILERUP’ are demonstrated, along with the phrase ’I Love You’, followed by an open hand,
and then repeated two more times (B).

joints. The index finger and ring finger sensor measurements
were mapped linearly to two of the joints in the robot arm. The
middle finger and pinky finger sensor measurements were used toswitch between two motor positions. The sensors were sampled
at approximately 500Hz and an exponential filter was appliedwith a decay rate α = 0.97. Using all four fingers, we controlledthese four degrees of freedom of the robotic arm in performinga pick-and-place operation, followed by a re-positioning of theobject. This teleoperation task shows the potential of using theglove as a continuous input device. Furthermore, the teleoperation task demonstrates that only a minimal amount of signal
processing is needed to use the data from the glove. The observed stability and robustness of the sensors reduces the amountof computing hardware and power supplies, thus reducing rigidmaterials (i.e. PCBs) on the body in wearable applications.

6

Conclusions and Future Work
The glove presented in this paper demonstrates the application of soft, flexible sensors to hand proprioception and tactile sensing. By using soft and compliant materials for both theglove and sensors, the resulting glove accommodates the wide
variability in hand shape, meeting a key criterion in Design for

5.2

Teleoperation
We demonstrated the application of the glove toward teleoperation (Figure 6). The four fingers of the sensory glove were
each used to independently control a different degree of freedom(Row 1 in Figure 6) on a robotic arm with servos at each of the5
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Copyright © 2018 ASMEFIGURE 6. Teleoperation demonstration. Box 1 shows the hand and robotic arm in its initial position. The top row shows the individual joints
controlled by each finger. The index and ring fingers control their respective joints in a continuous manner. The pinky and middle fingers trigger achange between two motor positions dependent on the sensor value passing a threshold value. The bottom two rows show a sequence in the teleoperation
task: the robot arm reaches down and picks up a box, rotates to a new position, deposits the box, and then re-positions the box.

of spurious signals from the ambient environment, and would reduce the amount of filtering required for the signal, paving theway for a more immediate, real-time system response.

Human Variability (DfHV). The sensors are constructed fromlow-cost materials, and the fabrication process allows for facilecustomization of sensor design. Use of I2 C protocol reduces thenumber of control wires needed, while also providing a low-cost
alternative. While this glove measures fewer degrees of freedom than are present in the human hand, the information provided allows the finger curvature to be determined, which may be
used in a variety of applications ranging from human-computerinterfaces to sign language capture. Additionally, the pressuresensitive finger pads provide another mode of input, alongside
finger curvature. Finally, as self-reported by the authors, finger
and hand motions with the functionalized glove were indistinguishable from an unmodified glove, achieving our goal of not
restricting mechanical motion of the hand. While the current
glove uses sensors that cannot distinguish between pressure andcurvature, the two modes can be sensed using separate sensors as
desired for a given application.
In future iterations of wearable devices enabled by capacitive strain sensing, a more advanced electrical model of the sensor is needed. As the geometry decreases past what is presented
here, the linear relationship between capacitance and strain will
no longer accurately predict the state (strain) of the system because the change in impedance of the conductive EIG composite
electrodes under strain is expected to have a more dominant effect on the sensor reading. Another area of focus in the future isto reduce the size of the custom microprocessor boards used tointerrogate the sensors, to further increase the conformability ofthe system. Finally, the addition of a shielding layer of conductive composite surrounding the sensors would reduce the impact

7

Acknowledgements
The authors thank Dr. Edward L. White and Dylan S. Shahfor assistance in electronic design and signal processing, as well
as for general advice during this project, and R. Adam Bilodeau
for assisting with photography. JCSM and this work were supported by a grant from Intel Corporation. MCY is supported by
the National Science Foundation Graduate Research Fellowship(Grant DGE-1333468).

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