ElectroRing: Subtle Pinch and Touch Detection with a Ring
Wolf Kienzle∗

Eric Whitmire∗

Chris Rittaler

Hrovje Benko

wkienzle@fb.com
FRL Research
Redmond, WA, USA

ewhitmire@fb.com
FRL Research
Redmond, WA, USA

crittaler@fb.com
FRL Research
Redmond, WA, USA

benko@fb.com
FRL Research
Redmond, WA, USA

(a)

(b)

(e)

Pinch

(c)

(d)

(f)

Touch

Figure 1: (a) ElectroRing prototype worn on the index fnger. (b, c) ElectroRing’s 3D-printed shell with red Velcro strap, red circuit board with shielded microcontroller/Bluetooth module, and Lithium-polymer battery. (d) Electrodes for touch detection
placed on the inside of the ring. (e) Real-time plot of raw touch sensor readings while user performs pinch gestures (f) Raw
touch sensor readings while user touches their opposite palm.

ABSTRACTKEYWORDS

We present ElectroRing, a wearable ring-based input device that
reliably detects both onset and release of a subtle fnger pinch,
and more generally, contact of the fngertip with the user’s skin.
ElectroRing addresses a common problem in ubiquitous touch interfaces, where subtle touch gestures with little movement or force
are not detected by a wearable camera or IMU. ElectroRing’s active
electrical sensing approach provides a step-function-like change
in the raw signal, for both touch and release events, which can beeasily detected using only basic signal processing techniques. Notably, ElectroRing requires no second point of instrumentation, but
only the ring itself, which sets it apart from existing electrical touchdetection methods. We built three demo applications to highlightthe efectiveness of our approach when combined with a simple
IMU-based 2D tracking system.

smart ring; touch detection; mixed-reality

CCS CONCEPTS• Human-centered computing → Interaction devices.
∗ Both authors contributed equally to this research.

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor proft or commercial advantage and that copies bear this notice and the full citationon the frst page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specifc permission
and/or a fee. Request permissions from permissions@acm.org.
CHI ’21, May 8–13, 2021, Yokohama, Japan© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8096-6/21/05. . . $15.00
https://doi.org/10.1145/3411764.3445094

ACM Reference Format:
Wolf Kienzle, Eric Whitmire, Chris Rittaler, and Hrovje Benko. 2021. ElectroRing: Subtle Pinch and Touch Detection with a Ring. In CHI Conference on
Human Factors in Computing Systems (CHI ’21), May 8–13, 2021, Yokohama,Japan. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3411764.
3445094

1

INTRODUCTION

Increased user mobility requires new ways to interact with computers beyond touchscreens, mice, and keyboards. One promisingstrategy is to appropriate the user’s skin for touch input [16, 17, 52].
For example, in an augmented reality (AR) application, a user couldpress a virtual button on their open palm or on their forearm. Besides being always available, the skin as an input surface ofers tactile and proprioceptive feedback, which can provide an additional
sense of agency. Unfortunately, the efectiveness of this approachhinges on robust, low-latency touch detection, which is challenging
to implement with small, unobtrusive sensors that can be worn all
day without encumbering the user [52].
Here we present ElectroRing (Figure 1), an always-available,
wearable input device for touch contact detection on the user’sskin. ElectroRing uses an active electrical sensing approach, similar
to [52, 55]. Compared to cameras or IMUs (inertial measurement
units), this electrical approach has the advantage of providing a
step-function-like change in the sensor output on both touch andrelease, even for subtle gestures. Because ElectroRing focuses on
detection of the precise moment of touch, rather than what or where

CHI ’21, May 8–13, 2021, Yokohama, Japanthe user touched, we envision ElectroRing as an ideal companion
device for an interactive system like AR glasses, which typicallycannot detect subtle or occluded touch gestures using only head
mounted cameras [52].
A challenge with the active electrical approach [52, 55] is thatit requires two points of instrumentation on the body (transmitter and receiver). To our knowledge, ElectroRing is the frst solution that requires only a single point of instrumentation. To detect
touch, two electrodes on the inside of the ring couple an AC signal onto the user’s fnger, and two further electrodes—shieldedfrom the transmitter—detect the small portion of the signal thatfows through the fnger into the touched surface (Figure 1e, 1f).
The ring is battery-powered and transmits data wirelessly to ahost application. It also features an on-board accelerometer and agyroscope—not for touch detection, but to track the ring’s positionin our example applications.
In comparison to prior work, ElectroRing combines a number ofdesirable properties. First, the fngertip is left un-instrumented, andno other points of instrumentation are required (e.g., no accompanying wristband). Second, the signal-to-noise ratio (SNR) of the raw
touch signal (Figure 1e, 1f) is substantial (25 dBV) and largely independent of gesture velocity or mechanical impact at contact. This is
in contrast to cameras or IMU based solutions, which often require
an unnatural range of motion or forceful contact to detect touches
reliably. Third, ElectroRing detects both touch and release events, arequirement for rich user interfaces with 2-state transactions, suchas drag & drop or inking [4].
The contributions of our work are:
• The design of a ring that electrically senses subtle touchand release events on the user’s skin, using a single point of
instrumentation• A characterization of the raw touch signal vs. gesture intensity and an evaluation of an end-to-end pinch and palmtouch detection system• Three example applications that combine the ring’s touch
sensor with its on-board IMU to highlight possible implementations of a fully functioning input device.

2

RELATED WORKElectroRing provides precise touch segmentation, which is a critical piece of the long-standing research vision of enabling ondemand computing surfaces [16, 33, 44]. Alternatives to appropriating everyday surfaces for touch input include the use of explicit touch surfaces on personal computing devices like smartphones, smart watches, or head-mounted displays [23, 25], or the
instrumentation of environmental objects with optical [1, 15, 26],
acoustic [28, 30, 31], electric impedance tomography [49, 51, 53], or
capacitive [8, 9, 24, 42, 54] sensors. We focus the remainder of thissection on wearable sensors that scale to new environments. Specifically, we review prior work on touch detection with camera-based
systems, capacitive sensors, systems that instrument the wrist, andsystems that instrument the fnger.

2.1

Camera-based Systems

Egocentric cameras are an attractive solution to ubiquitous touch
input due to the potential to solve both touch segmentation andWolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko

touch localization in one device. The proliferation of commodity
depth cameras, such as the Microsoft Kinect, has made this problem
much more tractable. For example, OmniTouch [16], uses a wearable
depth camera and projector system to turn everyday body and world
surfaces into interactive touch surfaces. Touch detection is realizedusing image processing techniques on a depth map and results inabout 1 cm to 2 cm of ambiguity in touch contact. MRTouch [45]improves on this work using the refned depth sensing system in a
Microsoft HoloLens, but still sufers from touch ambiguity at close
distances.
Because ElectroRing uses an electrical sensing technique, it is
not based on the fnger’s height above the surface; instead, it ofers
performance more similar to widely used capacitive touch surfaces,
which allow very subtle and precisely timed touches. We argue thatsuch touch precision is essential to enabling robust interfaces thatgive users confdence.

2.2

Capacitive Sensing

Capacitive sensing is one of the most ubiquitous methods of detecting precise touch between a user and an interactive device withhigh SNR. Beyond its frequent use for touch detection in devices likesmartphones, watches, and tabletop surfaces, recent advances haveenabled new kinds of interaction techniques. DiamondTouch [8, 9]
and similar eforts [18, 42] show how capacitive sensing can be used
to capture user and object identity. Touché uses swept-frequency
capacitive sensing to detect both touch and gestures [35]. Other
devices instrument on-body touch surfaces through wearable fngertip devices [46], clothing [34], or touchable tattoos [19, 29, 43]. A
common feature of these systems is the need for instrumentation at
the touch interface. ElectroRing detects touch between the fngertip
and the body without any instrumentation of the touch surface.
Instead, a more conveniently placed ring remotely detects when
the fngertip has made contact with the skin.
ElectroRing is most similar to an active capacitive sensing system operating in both transmit and receive mode, sometimes called
intrabody coupling [12]. However, it would be more accurately
described as a galvanic intrabody system. Unlike most capacitive
systems, ElectroRing’s electrodes are fxed to the skin and the capacitance between the electrodes and user does not change. ElectroRing
remotely senses touch events by measuring the current between
diferential receive electrodes.

2.3

Instrumented Wrists

Touch contact can be detected through electrical, inertial, or acoustic sensors embedded in a wristband. ElectroRing is most similar
to the active electrical touch detection techniques demonstrated in
SkinTrack [55] and ActiTouch [52]. SkinTrack [55] uses a ring that
couples an 80 Mhz AC signal to the body and a wristband on the
opposing arm to detect the presence and location of the fngertip
as it touches the palm. Although ElectroRing does not attempt todetect touch location, it is capable of detecting touch state withjust a single point of instrumentation. ActiTouch uses a similar
technique to couple a signal to the user’s wrist and measure thesignal fow through the body on a head-mounted device. Although
the sensing principle is similar, ElectroRing eliminates the needElectroRing: Subtle Pinch and Touch Detection with a Ringfor head or wrist instrumentation by directly measuring the signal
fow in the same device.
Other wristbands or armbands use acoustic [17] or inertial methods [50] to detect touch. These techniques are limited to on-bodytouches and require signifcant tap force. Moreover, these methods are incapable of detecting release events. Mujibiya et al. [27]demonstrate an ultrasound-based technique using an armband anda ring to detect touch state and location. ElectroRing’s electricalapproach requires only a single point of instrumentation.

2.4(a) Electrodes
RxV(b) Pinch
Tx

Shield(c) Palm-touchInstrumented Fingers

A variety of sensing techniques have been explored in rings andother fnger-mounted form-factors to enable interaction in mobile scenarios. Miniature cameras have been embedded in rings
to support hand tracking [5], to read text for users with visual
impairments [3, 37], or to understand the user’s context of interaction [36, 47]. Magic Finger [47] uses a fngertip-mounted camera
to detect touch and classify surfaces based on texture. In contrast,
ElectroRing detects touch in a ring form-factor without covering
the fngertip.
Other devices instrument the fngertip to electrically detect touchcontact with the thumb. Tip-Tap [20] detects thumb-to-fnger gestures without a battery using tattoos containing RFID antennas.
TipText uses a capacitive sensing array at the fngertip for subtle
text entry using the thumb [46]. While ElectroRing requires a battery and focuses exclusively on touch detection, it ofers a moreconvenient form-factor by leaving the fngertips unencumbered
and requiring only a single point of instrumentation in a ring.
Magnetic approaches have been proposed for occlusion-freetracking of the fngertip [6, 7] or a fnger-worn ring [2, 32]. Howevereven with precise fnger tracking, robust segmentation of touch
requires additional sensing. Inertial sensing techniques have beenused on the fnger for a variety of tracking and pointing tasks [14,21, 48] as well as touch detection [10, 11, 13]. In this work, we showthat ElectroRing can potentially detect lighter touches than inertial
techniques and can also detect release events, which is often not
feasible with an IMU.
It is worth noting that the touch detection in ElectroRing complements many other sensing and interaction techniques previously
demonstrated. In this work, we demonstrate three basic applications
using an IMU as the only additional sensor, but richer applications
can be realized by combining ElectroRing’s touch detection with,for example, the fnger tracking approach in LightRing [21].

3

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

THEORY OF OPERATIONElectroRing detects touch state by coupling a 10.7 MHz AC signal
to the body and indirectly measuring the signal fow through thefnger. This frequency is sufciently high to conduct well throughthe body. It is also a standard intermediate frequency used in radio
receivers, which makes it easier to fnd small integrated oscillatorsand other components that operate in this range. ElectroRing uses
two pairs of electrodes: 1) a proximal diferential transmit pair that
couples the AC signal to the body and 2) a distal diferential receive
pair that measures the voltage gradient along the fnger. A ffthshield electrode between the transmit and receive pairs acts as an
AC return path. It improves the signal-to-noise ratio by blocking

Figure 2: (a) ElectroRing uses fve electrodes: two transmit
electrodes (closest to the palm) diferentially couple the ACsignal to the fnger, the middle electrode shields direct coupling between the Tx and Rx electrodes, two receive electrodes (distal) measure the gradient of the signal along thefnger. (b) During a pinch, the signal travels through the
thumb and galvanically back to the transmit electrode. (c)Touching the opposing palm creates a galvanic path throughthe body back to the proximal transmit electrode.

any fringe felds from the transmit electrodes from directly coupling
into the receive electrodes. Figure 2a shows the electrode layout
along the fnger. When the user pinches or touches their opposing
palm, a small current fows in a circuit through the fnger, into thetouched surface, and back to the ring (Figure 2b, 2c).
ElectroRing uses diferential transmitter electrodes (shown as
Tx in Figure 2a), which means the signal fows from one of thetransmitter electrodes back into the other. When in contact withthe skin, most of the signal travels through the short path in the
body between these two transmitter electrodes. To detect touch,ElectroRing relies on trace current that fows along an electricallyparallel path through the body that is formed when the fnger makes
contact with the thumb or palm. While not touching anything, both
receiver electrodes (shown as Rx in Figure 2) measure roughly thesame level of the transmitted signal; there is little diference between
the two receivers because there is no current fow between them. In
contrast, when the user touches their skin, a return path is formed
between the touched surface and the proximal transmitter electrode.
The fow of current, although small, creates a diferential voltage
between the two distal receive electrodes, which, with sufcient
amplifcation, can be measured and used as a touch signal.
The path along which the current travels back to the transmit electrode varies depending on the which part of the body istouched. Figure 2b and 2c show the current path for both pinch
and palm-touch, respectively. When the user performs a thumbto-fnger pinch, this creates a skin-skin interface which the highfrequency signal can easily cross. The signal then follows a fairly
low-impedance galvanic path through the thumb and back to the

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

Electrodes

Wolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko

Tx Driver

Tx ElectrodeBuﬀer

Tx Electrode

Convert todiﬀerential

Oscillator
10.7 MHz

Shield Driver
Shield Electrode

Peripherals

Bias Voltage

Battery

IMU

Buﬀered DC@ 1.65 V
Rx Electrode

Ampliﬁer
Rx Electrode

PSoC 6

Rx Front-end~30x gain

Filter
10.7 MHz crystal
ﬁlter

ADC
~700ksps

Goertzel Filter
256 sample window
100 Hz output

BLE
To PC

Figure 3: Block diagram of ElectroRing hardware. The transmit driver bufers a 10.7 MHz signal and applies it to the twotransmit electrodes. A shield driver holds the shield electrode at a DC voltage. The receive front-end amplifes and flters thesignal, which is sampled by an ADC on the PSoC. The PSoC applies a Goertzel flter and streams the values to a PC.

proximal transmit electrode. A touch on the opposing palm also
creates a skin-skin interface as well as a galvanic path through thebody.
Although proximity of the fnger to the thumb or opposing palm
does create some capacitive coupling even when not touching, it
does not act as an efective proximity sensor and there is a distinct
increase in the signal at the moment of physical contact (see Figure 4). While current paths are shown as distinct lines in Figure 2,
these are only illustrative. In reality, the body is a complex networkof resistive and capacitive components. Additional complexities
are introduced at the ring by the tissue-skin and skin-electrodeinterfaces. However, our simplifed body model serves as a helpful
guide for reasoning about the properties of ElectroRing. For a morein-depth discussion of electrical body models, we refer the reader torelated work in the feld of body-coupled communications [38, 39].
This electrical method of touch detection is similar to that of ActiTouch [52], which placed transmit electrodes on a wristband andreceive electrodes on a head-mounted display. Simply mounting
the two electrode pairs close together on a single device, however,results in saturation of the receiver electrode and poor sensitivity to touch. We were able to sense touch from a single point of
instrumentation by introducing an additional shield electrode between the transmit and receive electrodes, optimizing the receiver
amplifer topology, and moving the device closer to the fngertip.

4

HARDWARE

ElectroRing consists of 1) a small, battery-powered printed circuitboard (PCB) that handles signal generation, measurement, andcommunication, 2) fve electrodes that contact the fnger, and 3) amechanical enclosure in a ring form-factor. Figure 3 shows a systemlevel overview. For detailed schematics, refer to the appendix. On
the PCB, the 10.7 MHz square wave signal is generated by a MEMS
oscillator (DSC6003). A unity-gain bufer and inverting amplifer(dual OPA2836) converts the signal from single-ended to diferential.
This 3.3 V diferential output is capacitively coupled to the twotransmitter electrodes. The two receive electrodes are amplifed

with 30 times gain and converted to a single-ended signal using an
instrumentation amplifer (3x LTC6253). The signal is then flteredusing a narrow-band ceramic flter and bufered for sampling by
an ADC.
A Cypress PSoC 6 system-on-chip handles data acquisition, touchstate detection, and communication with a PC over BLE. The receive
signal is under-sampled by the on-board ADC at approximately
700 ksps. A frmware algorithm detects the amplitude of the aliased
10.7 MHz signal using a software Goertzel flter with 256 samples.
The exact sampling frequency was optimized to minimize frequency
spreading and maximize the signal power in a single frequency bin.
Because the hardware ceramic flter has narrow bandwidth, there
is little other noise that could alias into the measured frequencyband.
The data sampling is duty-cycled and the data rate can be controlled depending on application needs. In this work, the data rate
was fxed to 100 Hz. For data collection purposes, the raw signal
was streamed over BLE to a host PC for processing and logging.
To minimize BLE communication overhead, the entire algorithm
can also be implemented in frmware, so that only discrete touch
events need to be sent over BLE. ElectroRing also contains an onboard IMU to enable applications that require positional tracking
for pointing or swiping. For the purpose of this paper, all IMU data
was streamed over BLE and processed on a PC.
ElectroRing is implemented on a custom PCB that measures
27 mm by 17 mm. It is powered by a small 407 mW h lithium
polymer battery afxed to the side of the ring. Overall, the system consumes approximately 220 mW of power (for a lifetime of
1-2 hours). However, we note that this system is currently optimized for fexibility and not power consumption. Initial eforts to
reduce power through hardware optimization and duty-cycling themeasurements has yielded a device that consumes just 18.5 mW(approximately 22 hours continuous operation on the same battery)with no noticeable degradation in signal quality.
To maximize ft across users and make it easier to don, the ringconsists of two 3D-printed shells, connected by a hinge on one side

ElectroRing: Subtle Pinch and Touch Detection with a Ring

ElectroRing (mV)

Normal pinch (x3)

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

Light pinch (x3)

Weak pinch (x3)

Pencil tap (x3)

Hand wiggle

Table bump (x3)

26
24
22
20

Accel (g)

2
1
0
−1
−2

Gyro (dps)

500
250
0
−250
−500
0

5

10

15

20

25

30

Time (s)Figure 4: Raw touch sensor (top), accelerometer (middle), and gyroscope (bottom) data for six diferent activities as functionof time: normal pinch, light pinch, weak pinch, and three non-touch distractor activities. Pinch states are shown as shaded
vertical bars. Note how subtly the third set of pinches was performed—peak acceleration (middle plot) and angular velocity(bottom plot) during these "weak" pinches are close to noise level.

and Velcro on the other (Figure 1b). The hinge allows the ring toswing open and closed to ft the user’s fnger and the Velcro strap
ensures a snug ft. The interior surface of the ring is covered witha thin layer of foam for comfort. The electrodes are printed on afex circuit board using a copper conductor with tin plating and are
attached to the foam lining with double-sided tape.
The exact size and spacing between the electrodes can be adjusted depending on the user’s fnger and application requirements.
In this work, we built two rings, which performed comparably:
one with 2 mm wide electrodes, and one with 3 mm wide electrodes. Both rings had 1 mm gaps between electrodes, leading toan overall electrode width of 14 mm and 19 mm, respectively. The
corresponding 3D-printed ring shells are 15 mm and 20 mm wide,
respectively.

5

SIGNAL PROCESSING

Before designing the touch detection algorithm, we visually inspected the raw signal from the touch sensor for diferent activities
as shown in Figure 4. The top plot shows the output of the Goertzel
flter in mV. The plots below show unfltered readings from theon-board accelerometer and gyroscope, respectively. The sampling
rate for all three plots was 100 Hz.
An initial observation from Figure 4 is that touch states—denotedby the shaded vertical bars—clearly stand out visually in the
raw ElectroRing touch signal. At touch/release events, the signal

rises/falls very quickly, typically between 10% and 90% from one
sample to the next at 100 Hz. Notably, the touch signal level during
touch states is largely independent of 1) the impact of the fngertip
coming into contact with the surface—indicated by a sharp peak in
the accelerometer reading, and 2) the angular velocity of the fnger
during the gesture (amplitude of the gyroscope signal).
The second half of the time series in Figure 4 shows that the raw
touch signal is quite robust to fnger motion that is not related to a
touch gesture, but clearly registers on the IMU, and could lead tofalse detections in an IMU-based touch detector. Finally, we observe
some baseline drift in the touch signal, but it is slow and small
compared to the signal change at touch and release events.
To further characterize how the touch signal varies with gesture
type and intensity, we recorded fve short pinches and palm touches
from an expert user at three subjective intensity levels: "normal","light", and "weak". The two bottom graphs in Figure 4 show an
example of IMU readings vs. subjective intensity level, with amplitudes decreasing from "normal" to "light" to "weak". Here, "weak"refers to a touch in which an efort was made to make contact
as gently as possible while exerting no force. We computed thesignal-to-noise ratio (SNR) of each touch event as described in
Equation 1.

SNR = 20 log10mean(v touch ) − mean(v no-touch )std(v noise )(1)

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

Pinch

50

Palm-touch

50

40"!Ő7(ő"!Ő7(őWolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko

30
20
10
0

40
30
20
10
0

0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

;-h-11;Ѵ;u-ঞomŐ]ő

50

50

40

40

"!Ő7(ő"!Ő7(ő;-h-11;Ѵ;u-ঞomŐ]ő

30
20
10
0

oul-Ѵ
b]_|
Weak

30
20
10
0

0

100

200

300

400

;-h-m]Ѵ-u;Ѵo1b|Ő7rvő

500

0

100

200

300

400

500

;-h-m]Ѵ-u;Ѵo1b|Ő7rvő

Figure 5: Scatter plots of ElectroRing SNR for pinch andpalm touch gestures vs. intensity as measured by accelerometer (top row) and gyro (bottom row). Color represents gesture intensity as intended by the user during the recording.
For both pinch and palm touch, SNR values are consistently
around 25 dBV, even for the weakest gestures (green), where
the fnger moved extremely slowly, below 10 dps and 0.1 g.

v touch are samples from a touch segment, v no-touch are samples
from a non-touch segment, and v noise are samples from the preceding non-touch segment. We also computed the peak accelerationand peak angular velocity around each touch event as a measureof gesture intensity (for acceleration, a 1-second moving average
was subtracted to remove the efect of gravity). Figure 5 shows SNR
consistently around 25 dBV for both pinch and palm touch, andessentially independent of gesture intensity. This suggests that we
can robustly detect very subtle pinches and touches.
Motivated by these observations, we implemented a simple touchdetection algorithm (Figure 6): we frst remove the high frequency
noise with a 3-tap median flter (30 ms window at 100 Hz samplerate). Then, we compute the signal change between the current andprevious sample. If the signal change is greater than a threshold, we
report a touch event. If the change is less than a second (negative)
threshold, we report a release event. The two touch thresholds
were tuned on a small training data set, as described in the next
section. The algorithm keeps track of the current touch state tosuppress touch events while in contact, and release events while notin contact. Finally, to de-bounce the detector output, we suppress
any touch events closer than 6 samples (60 ms) to the last release.

6

by each author in their respective homes (except the PCB) andshould exhibit some variation in their function. Second, our signal
processing algorithm is extremely simple, with only a small numberof parameters.
We collected pinch and palm touch data from two of the authors over 5 days with 2 sessions per day. Each session consisted of 10 light pinches and 10 light palm touches. The frst
5 pinches/touches within a session were short taps, about one
second apart. The second 5 pinches/touches were performed withdrag/swipe motion during contact. This yielded a total of 400 touchand release events, which were manually annotated by marking
the half-way point in each touch/release ramp in raw touch signal.
The average length of a touch state was 322 ms (SD 229 ms) andthe average length of a no-touch state was 415 ms (SD 293 ms). We
used data from the frst session (the frst 10 pinch and 10 tap eventsfrom both users, i.e., 40 events total) to tune the two thresholds inour algorithm, and the remaining data (9 sessions, both users) toevaluate the resulting system’s accuracy.
To tune the touch and release threshold in our algorithm, wesearched over a two-dimensional grid of possible thresholds (inmV), minimizing the total number of false touches/releases andmissed touches/releases. A detection was counted as false or missed
if it occurred more than 80 ms before or after the true (manuallyannotated) event time. The search range was set to 0 mV to 3 mV
for touch, and -3 mV to 0 mV for release, in steps of 0.1 mV. This
yielded optimal thresholds of 1.1 mV for touch and -0.7 mV for
release. We evaluated the detector on the remaining 18 sessions(360 touch/release events) from the two users. We counted only twoerrors overall (0.6%): one missed pinch onset for one user, and one
early palm touch release for the other user. The threshold search
was performed once in this study, jointly for all users.
We also measured the latency between true event times—as manually labeled in the raw data—and detected events returned by
our algorithm, not counting downstream BLE communication etc.
2. Compute signal derivative

1. InputRaw touch signal

3-tap median ﬁlter

Temporal diﬀerence(mV @ 100 Hz)

x

3. Update touch statex > touch threshold andin No Touch for > 60 ms

No
Touch

4. Output
Touch Event

Touch
Release Eventx < release threshold

EVALUATION

Due to logistic constraints caused by the COVID-19 pandemic, allexperiments were performed in the two frst authors’ homes, andby the authors only. While this left us with a small number of test
subjects, which have to be considered experts, we believe that thegenerality of our results is supported by two benefcial efects. First,
we used two hardware prototypes that were assembled separately

Figure 6: Touch detection pipeline. The raw signals from theElectroRing hardware are fltered and temporal diferences
are computed. This signal drives a state machine that detects
touch and release events. Simple touch and release thresholds are used to determine state transitions.

ElectroRing: Subtle Pinch and Touch Detection with a Ring(a) Pinch to Drag & Drop

CHI ’21, May 8–13, 2021, Yokohama, Japan(b) Pinch to Draw(c) Palm-touch Carousel

Figure 7: ElectroRing’s ability to detect subtle touch/release events with precise timing enables a number of useful applications
when combined with a pointing technique such as an IMU: (a) In-air drag & drop using fnger pinches. (b) Pinch to segment
strokes while drawing in mid-air. (c) Carousel/slider with inertia controlled by taps/swipes on the palm.

Note that due to the electrical sensing approach, the time between aphysical touch event and the corresponding ramp in the raw sensor
reading should be negligible. In contrast, our detection algorithm
may introduce noticeable latency. The 3-tap median flter delays
sudden signal level changes by one sample (10 ms) already. Fortunately, our algorithm did not add signifcantly to that. We measured
an average latency of 15 ms (SD 3.0 ms) for touch events and 11 ms(SD 3.1 ms) for release events.
These results highlight the system’s ability to detect touches
with high reliability (> 99%) and very low latency (roughly thetime of one video frame at 60 Hz), across 18 don/dof sessions, 5days, and 2 users with diferent hardware prototypes. As mentioned
above, a larger user experiment is required to claim wider generality.
However, given the high SNR of the raw signal and the simplicity of
the algorithm, we believe that similar performance can be achieved
for most users.

7

APPLICATION EXAMPLES

We built three application examples to highlight ElectroRing’s effectiveness: in-air drag & drop, in-air drawing, and navigating a
carousel-style date picker (Figure 7).

7.1

Pinch to Drag & Drop

This frst demo lets the user drag and drop documents between
two lists. Two-dimensional tracking is achieved by integratingrotation rates from the gyroscope (around the yaw and pitch axes,
the roll axis was ignored). With an IMU alone, there is no easy way
for the user to signal transitions between pointing and dragging
states [4]. ElectroRing lets the user indicate dragging vs. pointing
state with a subtle pinch gesture. Without reliable detection andprecise timing of these state transitions, documents could easily beplaced in unwanted slots.

7.2

Pinch to Draw

This example shows an in-air drawing experience. Here, the virtual
pen is only drawing while the user is pinching. As in the drag &drop example, the two-dimensional tool position is controlled byintegrating two of the gyroscope readings. Inking nicely illustrates
the importance of precise timing of pen up vs. pen down transitions;
any latency in pinch/release detection or ambiguity in the exact

touch moment would result in strokes that start or end in otherlocations than intended.

7.3

Palm-touch Carousel

To demonstrate the potential of on-body interfaces we implemented
a carousel date picker that is controlled by swiping on the user’spalm. While discrete swipe gestures are easy to detect with inertial
sensors, this application shows continuous directional input thatenables much richer usage, e.g. for kinetic scrolling. Here, the user
can lightly brush the palm surface to drag, quickly swipe to accelerate, or tap and hold to stop (brake) the carousel motion. Left/rightmotion is derived from the rotation around the vertical gyroscope
axis.
Figure 8 illustrates how the carousel application benefts from
ElectroRing’s ability to 1) detect subtle touches and 2) report touchand release events with low latency. First, with the fngertip moving
fast and mostly parallel to the surface, touch states are typicallyshort and make only light contact. ElectroRing’s SNR is still highin these cases, even when touch or release events are barely visible
in the IMU data. Second, touch and release events need to be localized precisely in time. Otherwise the system could misinterpret a
direction change at the beginning or end of a swipe as a swipe in
the opposite direction.

8

CONTACT WITH OFF-BODY SURFACES

While the primary aim of ElectroRing is to detect on-body touches,
we note that it can also detect contact with various conductive surfaces external to the body. As discussed previously, during "normal"
on-body operation the body forms a galvanic return path between
the fngertip and the proximal transmit electrode (Figure 2). For
of-body operation, the conductive environmental surface ofers acapacitive return path to the body. This scenario relies on the everpresent capacitive coupling between the human body and various
objects in the environment. Figure 9 shows a simplifed electricalpathway for touch on a conductive of-body object. If the object
touched is electrically grounded, it is likely to trigger a detectablesignal change regardless of size. However, ungrounded conductive
objects of sufcient size also have enough capacitive coupling to
the body to support a reasonable return path.

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

Wolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko

Finger motion:
Touch state:

touch

hover

touch

touch

hover

SNR. Most non-conductive objects, such as a wooden table, exhibit
no response at all.
While this approach does not lend itself to ubiquitous surface
touch interaction [16, 45], it does provide highly robust touch detection on a limited set of conductive surfaces. This could enablevarious interactions on known objects, such as the surface of ametal table, or provide a method for researchers to prototype suchinteractions simply by placing a conductive layer, such as aluminum
foil, on any surface.

touchElectroRing (mV)

24

23

22

Accel (g)

1

9
0
x
y
z

−1

Gyro (dps)

100
0

DISCUSSION AND FUTURE WORKElectroRing robustly detects both touch and release events, in contrast to systems that focus only on touch events [10, 11, 13, 31],This enables a rich set of two-state interactions, like inking, dragand-drop, and rubber banding [4], as shown in Figure 7. Moreover,because ElectroRing relies on an electrical detection technique, it
supports subtle touches, reducing the burden on users to perform−100

Oﬀ-body surface touch

−200
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Galvanic path

Time (s)Figure 8: The carousel application (see Figure 7c) requires robust and precisely timed touch and release events in order todetermine whether the user’s fnger motion occurs while incontact with the palm or while hovering. This fgure shows
sensor readings during a series of horizontal swipe/drag gestures on the palm: the top graph shows the touch signal
and states of contact (shaded areas). The gyro’s vertical axis
(green curve, bottom plot) drives the motion of the swipe(also shown as red arrows at the top). The touch signal showshigh SNR, even though touch and release events are masked
by other motion in the IMU data. Precise temporal segmentation allows ElectroRing to accurately capture fast, complex
motions like back and forth swipes on the palm without lifting the fnger.

To better understand ElectroRing’s ability to detect contact withvarious surfaces, we conducted a supplementary evaluation inwhich we compared the response of on-body surfaces to grounded,
conductive, and non-conductive of-body surfaces. One of the authors wore ElectroRing and touched 20 diferent surfaces fve times
each. For each surface, the average SNR was computed according
to Equation 1.
Figure 10 shows the results from this analysis, grouped by onbody and of-body surfaces. As expected, touching the bare skin of
the thumb, palm, or forearm achieves a high signal-to-noise ratio.
Touching the body through clothing signifcantly degrades thesignal. For of-body surfaces, large or grounded metal objects, suchas a metal table, perform comparably to touches on skin. Conductive
objects making contact with another point on the body, such as ametal object held in the hand also perform quite well. As the surface
area shrinks or the conductivity degrades, so does the measured

Capacitive path

Conductive
surface

Figure 9: Touching a conductive surface creates a capacitive
return path based on the coupling between the surface andthe user’s body.

Pinch
Arm
Palm
Arm through shirt
Leg through pants
Metal object in hand
Metal table
Refrigerator
Car exterior
Laptop (plugged in)
Doorknob
Aluminum foil
Metal door
Laptop (unplugged)
Metal object on table
Particle board table
Wood table
Wood door
Stone countertop
Wall

*
*
*
*
10

*SNR too low to estimate

Surface TypeOn-body
Off-body
15

20

25

30

35

40

SNR (dBV)

Figure 10: SNR for touches on various on-body and of-bodysurfaces. Touching bare skin or large conductive objects performs best. Non-conductive objects exhibit poor SNR or are
not detectable at all.

ElectroRing: Subtle Pinch and Touch Detection with a Ring

deliberate, tiring gestures. For example, when paired with an augmented reality headset, a user might quickly pinch and release
without moving their arms to dismiss a notifcation or pinch anddrag on a radial menu to interact with it.
Among prior work, ElectroRing is most similar to ActiTouch [52],which placed electrodes on a head-mounted display and a wristband
to detect contact between a fngertip and the opposing palm. Bothrely on coupling an AC signal to the body and measuring how
the signal changes during a touch event. ElectroRing builds on
ActiTouch by moving all instrumentation to a single point on the
body (one ring vs a wristband plus headset). This both provides lessencumbrance for the user and enables new kinds of interactions
that only require a single hand. Specifcally, ElectroRing enables
thumb-to-fnger pinch interactions and touch on certain conductive
of-body surfaces.
While these new use cases enable exciting new interaction possibilities, ElectroRing is not capable of diferentiating between contact
with diferent surfaces (e.g., thumb, palm, or conductive objects).
We envision relying on a complementary sensing method to differentiate between touch surfaces and track touch location. In thiswork, we used an IMU to highlight several simple use cases, but
the use of vision-based hand tracking can enable richer interactions [16, 45, 52]. Future work could also explore whether the useof capacitive coupling techniques common in body channel communications [39] can enable distinguishing between on-body andof-body touches. Another exciting avenue of research is to pairElectroRing with other wearable techniques for tracking fnger
motion [14, 21, 22, 32] to more fully capture the state of the handand fnger.
While this work focused on delivering binary detection of touchand release, it may also be possible to detect touch pressure. The
amplitude diferences in Figure 4 already demonstrate a modest
diference in signal amplitude for diferent touch intensities, which
are likely related to the contact area of the touch. We believe it will
be challenging to reliably estimate absolute pressure this way due
to other sources of signal variations of similar or higher amplitude,
such as changing electrode/skin impedance caused by small movements of the ring on the fnger, that could mask pressure-dependent
signal changes. However, tracking relative pressure while the fnger is resting on a surface is likely feasible. Future work could also
explore the use of frequency sweeps [35] to better characterize thetouch pressure or perhaps classify the type of surface touched.
With an overall width of 15 mm to 20 mm, the device is somewhat large for daily use. Future work should explore eliminating thecentral electrode and optimizing the electrode shape and size. For
example, it may be possible to split the electrodes between the palmar and dorsal sides of the fnger, reducing overall width. Anotherpotential optimization may be to use other electrode confgurations,e.g., a single-ended transmitter, which would use capacitive coupling of the PCB ground as the return path. The use of adjustablevelcro straps on the ring made it easy to control the tightness ofthe band and to ensure comfort during use. While the device does
not need to be tight, it is important that the electrodes make contact with the skin, a more difcult task for traditional rigid rings.
Future designs could explore techniques to add additional mechanical compliance or deformable electrodes to improve both comfort
and contact reliability. Additionally, while there is precedent for

CHI ’21, May 8–13, 2021, Yokohama, Japanthe use of a interactive ring on the index fnger [14, 21, 32], additional ergonomic studies are needed to understand the usability
and acceptability of this form-factor during extended use. Future
work should also consider alternative form-factors for this sensing
approach. ElectroRing has such a high SNR because of its proximity
to the fngertip, but if a similar technique can be achieved in asingle wristband form-factor, it may reduce overall user encumbrance and enable detection of touches by other fngers, not justthe instrumented fnger.
While we believe this approach will generalize across users, thecurrent evaluation of the system was performed with only two
expert users. Future studies should explore how variation amongusers, especially novice users, impacts performance. Variables liketouch technique, hand size, skin conductivity, and hydration may
have some efect on the measured signal strength. However, given
the high signal-to-noise ratios observed in this work, it is likelythat acceptable performance can be achieved even in the mostchallenging conditions. To reduce the potential for overftting ondata collected from two expert users, we chose an algorithm thatis extremely simple, with just two tunable parameters. If suitable
defaults cannot be found that ft a large population, these parameters could be personalized through a per-user calibration step, e.g.
by guiding users through a initial setup experience or game that
prompts them to pinch and tap a number of times after they frst
set up the device.
ElectroRing uses BLE to communicate data back to a host PC.
However, we note that systems that couple an AC signal to thehuman body have previously been used to transmit data in bodycoupled communications [11, 38–41]. By modulating the transmit
signal, it may be possible to send ElectroRing data to a wristband
or other wearable device with low power and minimal hardware
modifcations. Even without transmitting data, the system can be
duty cycled to save power consumption. Currently, despite continuous signal transmission, the ADC is only active less than 4% of
the time. By eliminating BLE transmission and duty cycling thesignal conditioning circuitry, we estimate we can reduce power
consumption to a level suitable for all-day use.

10

CONCLUSION

In this work, we presented ElectroRing, a ring that uses an electrical
technique for measuring the precise moment of contact and releasebetween the fngertip and the user’s skin. Unlike previous implementations of this technique, ElectroRing requires only a single
point of instrumentation on the body. By coupling a signal to the fngertip and measuring how the signal gradient changes with touch,ElectroRing can robustly detect even subtle fnger pinches or touchcontacts with the opposite palm. Not only does this enable on-bodyinterfaces, which provide tactile and proprioceptive cues, but thedegree of precision in ElectroRing’s touch segmentation enables
high-fdelity touch interfaces with capabilities beyond what can beachieved with inertial or computer-vision techniques alone. We also
demonstrated how ElectroRing can be used to detect contact withcertain conductive of-body surfaces, like a metal table. This work
has the potential to bridge the gap between the deliberate, discrete
input common in wearable devices and the expressive, low-efort
interactions aforded by modern touch screens.

CHI ’21, May 8–13, 2021, Yokohama, JapanACKNOWLEDGMENTS
The authors would like to thank Josh Weiher for his help on themechanical design of the ring, and Neil Weiss, Roger Boldu, andShiu Ng for insightful discussions on the sensing principle andapplications.

REFERENCES[1] A. Agarwal, S. Izadi, M. Chandraker, and A. Blake. 2007. High Precision Multitouch Sensing on Surfaces using Overhead Cameras. In Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer Systems (TABLETOP’07). 197–200.
[2] Daniel Ashbrook, Patrick Baudisch, and Sean White. 2011. Nenya: Subtle andEyes-Free Mobile Input with a Magnetically-Tracked Finger Ring. In Proceedingsof the SIGCHI Conference on Human Factors in Computing Systems (Vancouver,
BC, Canada) (CHI ’11). Association for Computing Machinery, New York, NY,
USA, 2043–2046. https://doi.org/10.1145/1978942.1979238[3] Roger Boldu, Alexandru Dancu, Denys J.C. Matthies, Thisum Buddhika, Shamane
Siriwardhana, and Suranga Nanayakkara. 2018. FingerReader2.0: Designingand Evaluating a Wearable Finger-Worn Camera to Assist People with Visual
Impairments While Shopping. Proc. ACM Interact. Mob. Wearable UbiquitousTechnol. 2, 3, Article 94 (Sept. 2018), 19 pages. https://doi.org/10.1145/3264904[4] William Buxton. 1990. A Three-State Model of Graphical Input. In Proceedingsof the IFIP TC13 Third Interational Conference on Human-Computer Interaction(INTERACT ’90). North-Holland Publishing Co., NLD, 449–456.
[5] Liwei Chan, Yi-Ling Chen, Chi-Hao Hsieh, Rong-Hao Liang, and Bing-Yu Chen.
2015. CyclopsRing: Enabling Whole-Hand and Context-Aware Interactions
Through a Fisheye Ring. In Proceedings of the 28th Annual ACM Symposium on User
Interface Software and Technology (Charlotte, NC, USA) (UIST ’15). Association
for Computing Machinery, New York, NY, USA, 549–556. https://doi.org/10.
1145/2807442.2807450[6] Liwei Chan, Rong-Hao Liang, Ming-Chang Tsai, Kai-Yin Cheng, Chao-Huai Su,
Mike Y. Chen, Wen-Huang Cheng, and Bing-Yu Chen. 2013. FingerPad: Privateand Subtle Interaction Using Fingertips. In Proceedings of the 26th Annual ACM
Symposium on User Interface Software and Technology (St. Andrews, Scotland,
United Kingdom) (UIST ’13). Association for Computing Machinery, New York,NY, USA, 255–260. https://doi.org/10.1145/2501988.2502016[7] Ke-Yu Chen, Shwetak N. Patel, and Sean Keller. 2016. Finexus: Tracking Precise
Motions of Multiple Fingertips Using Magnetic Sensing. In Proceedings of the 2016
CHI Conference on Human Factors in Computing Systems (San Jose, California,
USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA,
1504–1514. https://doi.org/10.1145/2858036.2858125[8] Paul Dietz and Darren Leigh. 2001. DiamondTouch: A Multi-User Touch Technology. In Proceedings of the 14th Annual ACM Symposium on User Interface
Software and Technology (Orlando, Florida) (UIST ’01). Association for Computing
Machinery, New York, NY, USA, 219–226. https://doi.org/10.1145/502348.502389[9] Paul H. Dietz, Bret Harsham, Clifton Forlines, Darren Leigh, William Yerazunis, Sam Shipman, Bent Schmidt-Nielsen, and Kathy Ryall. 2005. DT Controls: Adding Identity to Physical Interfaces. In Proceedings of the 18th Annual
ACM Symposium on User Interface Software and Technology (Seattle, WA, USA)(UIST ’05). Association for Computing Machinery, New York, NY, USA, 245–252.
https://doi.org/10.1145/1095034.1095075[10] Masaaki Fukumoto and Yasuhito Suenaga. 1994. “FingeRing”: A Full-Time Wearable Interface. In Conference Companion on Human Factors in Computing Systems(Boston, Massachusetts, USA) (CHI ’94). Association for Computing Machinery,
New York, NY, USA, 81–82. https://doi.org/10.1145/259963.260056[11] Masaaki Fukumoto and Yoshinobu Tonomura. 1997. “Body Coupled FingerRing”: Wireless Wearable Keyboard. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI’97). Association for Computing Machinery, New York, NY, USA, 147–154.
https://doi.org/10.1145/258549.258636[12] Tobias Grosse-Puppendahl, Christian Holz, Gabe Cohn, Raphael Wimmer, Oskar
Bechtold, Steve Hodges, Matthew S. Reynolds, and Joshua R. Smith. 2017. Finding
Common Ground: A Survey of Capacitive Sensing in Human-Computer Interaction. In Proceedings of the 2017 CHI Conference on Human Factors in Computing
Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 3293–3315. https://doi.org/10.1145/3025453.3025808[13] Yizheng Gu, Chun Yu, Zhipeng Li, Weiqi Li, Shuchang Xu, Xiaoying Wei, andYuanchun Shi. 2019. Accurate and Low-Latency Sensing of Touch Contact on
Any Surface with Finger-Worn IMU Sensor. In Proceedings of the 32nd Annual
ACM Symposium on User Interface Software and Technology (New Orleans, LA,
USA) (UIST ’19). Association for Computing Machinery, New York, NY, USA,1059–1070. https://doi.org/10.1145/3332165.3347947[14] Aakar Gupta, Cheng Ji, Hui-Shyong Yeo, Aaron Quigley, and Daniel Vogel. 2019.
RotoSwype: Word-Gesture Typing Using a Ring. In Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI

Wolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko’19). Association for Computing Machinery, New York, NY, USA, Article 14,
12 pages. https://doi.org/10.1145/3290605.3300244[15] Jeferson Y. Han. 2005. Low-Cost Multi-Touch Sensing through Frustrated Total
Internal Refection. In Proceedings of the 18th Annual ACM Symposium on User
Interface Software and Technology (Seattle, WA, USA) (UIST ’05). Association for
Computing Machinery, New York, NY, USA, 115–118. https://doi.org/10.1145/
1095034.1095054[16] Chris Harrison, Hrvoje Benko, and Andrew D. Wilson. 2011. OmniTouch: Wearable Multitouch Interaction Everywhere. In Proceedings of the 24th Annual ACM
Symposium on User Interface Software and Technology (Santa Barbara, California,
USA) (UIST ’11). Association for Computing Machinery, New York, NY, USA,
441–450. https://doi.org/10.1145/2047196.2047255[17] Chris Harrison, Desney Tan, and Dan Morris. 2010. Skinput: Appropriating the
Body as an Input Surface. In Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for
Computing Machinery, New York, NY, USA, 453–462. https://doi.org/10.1145/
1753326.1753394[18] Christian Holz and Marius Knaust. 2015. Biometric Touch Sensing: Seamlessly
Augmenting Each Touch with Continuous Authentication. In Proceedings of the
28th Annual ACM Symposium on User Interface Software and Technology (Charlotte,NC, USA) (UIST ’15). Association for Computing Machinery, New York, NY, USA,
303–312. https://doi.org/10.1145/2807442.2807458[19] Hsin-Liu (Cindy) Kao, Christian Holz, Asta Roseway, Andres Calvo, and Chris
Schmandt. 2016. DuoSkin: Rapidly Prototyping on-Skin User Interfaces Using
Skin-Friendly Materials. In Proceedings of the 2016 ACM International Symposium
on Wearable Computers (Heidelberg, Germany) (ISWC ’16). Association for Computing Machinery, New York, NY, USA, 16–23. https://doi.org/10.1145/2971763.
2971777[20] Keiko Katsuragawa, Ju Wang, Ziyang Shan, Ningshan Ouyang, Omid Abari, andDaniel Vogel. 2019. Tip-Tap: Battery-Free Discrete 2D Fingertip Input. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology(New Orleans, LA, USA) (UIST ’19). Association for Computing Machinery, New
York, NY, USA, 1045–1057. https://doi.org/10.1145/3332165.3347907[21] Wolf Kienzle and Ken Hinckley. 2014. LightRing: Always-Available 2D Input on
Any Surface. In Proceedings of the 27th Annual ACM Symposium on User Interface
Software and Technology (Honolulu, Hawaii, USA) (UIST ’14). Association for
Computing Machinery, New York, NY, USA, 157–160. https://doi.org/10.1145/
2642918.2647376[22] David Kim, Otmar Hilliges, Shahram Izadi, Alex D. Butler, Jiawen Chen, Iason
Oikonomidis, and Patrick Olivier. 2012. Digits: Freehand 3D Interactions Anywhere Using a Wrist-Worn Gloveless Sensor. In Proceedings of the 25th Annual
ACM Symposium on User Interface Software and Technology (Cambridge, Massachusetts, USA) (UIST ’12). Association for Computing Machinery, New York,NY, USA, 167–176. https://doi.org/10.1145/2380116.2380139[23] Jihyun Lee, Byungmoon Kim, Bongwon Suh, and Eunyee Koh. 2016. Exploring
the Front Touch Interface for Virtual Reality Headsets. In Proceedings of the 2016
CHI Conference Extended Abstracts on Human Factors in Computing Systems (SanJose, California, USA) (CHI EA ’16). Association for Computing Machinery, New
York, NY, USA, 2585–2591. https://doi.org/10.1145/2851581.2892344[24] SK Lee, William Buxton, and K. C. Smith. 1985. A Multi-Touch Three Dimensional
Touch-Sensitive Tablet. In Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems (San Francisco, California, USA) (CHI ’85). Association
for Computing Machinery, New York, NY, USA, 21–25. https://doi.org/10.1145/
317456.317461[25] Kent Lyons. 2016. 2D Input for Virtual Reality Enclosures with Magnetic Field
Sensing. In Proceedings of the 2016 ACM International Symposium on Wearable
Computers (Heidelberg, Germany) (ISWC ’16). Association for Computing Machinery, New York, NY, USA, 176–183. https://doi.org/10.1145/2971763.2971787[26] Nobuyuki Matsushita and Jun Rekimoto. 1997. HoloWall: Designing a Finger,
Hand, Body, and Object Sensitive Wall. In Proceedings of the 10th Annual ACM
Symposium on User Interface Software and Technology (Banf, Alberta, Canada)(UIST ’97). Association for Computing Machinery, New York, NY, USA, 209–210.
https://doi.org/10.1145/263407.263549[27] Adiyan Mujibiya, Xiang Cao, Desney S. Tan, Dan Morris, Shwetak N. Patel,and Jun Rekimoto. 2013. The Sound of Touch: On-Body Touch and GestureSensing Based on Transdermal Ultrasound Propagation. In Proceedings of the 2013
ACM International Conference on Interactive Tabletops and Surfaces (St. Andrews,
Scotland, United Kingdom) (ITS ’13). Association for Computing Machinery, New
York, NY, USA, 189–198. https://doi.org/10.1145/2512349.2512821[28] Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan, and Shyamnath Gollakota.
2016. FingerIO: Using Active Sonar for Fine-Grained Finger Tracking. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose,
California, USA) (CHI ’16). Association for Computing Machinery, New York, NY,
USA, 1515–1525. https://doi.org/10.1145/2858036.2858580[29] Aditya Shekhar Nittala, Anusha Withana, Narjes Pourjafarian, and Jürgen Steimle.
2018. Multi-Touch Skin: A Thin and Flexible Multi-Touch Sensor for On-Skin
Input. In Proceedings of the 2018 CHI Conference on Human Factors in Computing
Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery,ElectroRing: Subtle Pinch and Touch Detection with a Ring

New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3173607[30] Shijia Pan, Ceferino Gabriel Ramirez, Mostafa Mirshekari, Jonathon Fagert,
Albert Jin Chung, Chih Chi Hu, John Paul Shen, Hae Young Noh, and Pei
Zhang. 2017. SurfaceVibe: Vibration-Based Tap & Swipe Tracking on Ubiquitous Surfaces. In Proceedings of the 16th ACM/IEEE International Conferenceon Information Processing in Sensor Networks (Pittsburgh, Pennsylvania) (IPSN’17). Association for Computing Machinery, New York, NY, USA, 197–208.
https://doi.org/10.1145/3055031.3055077[31] J. A. Paradiso, Che King Leo, N. Checka, and Kaijen Hsiao. 2002. Passive acoustic
sensing for tracking knocks atop large interactive displays. In SENSORS, 2002
IEEE, Vol. 1. 521–527 vol.1.
[32] Farshid Salemi Parizi, Eric Whitmire, and Shwetak Patel. 2019. AuraRing: Precise
Electromagnetic Finger Tracking. Proc. ACM Interact. Mob. Wearable UbiquitousTechnol. 3, 4, Article 150 (Dec. 2019), 28 pages. https://doi.org/10.1145/3369831[33] Claudio S. Pinhanez. 2001. The Everywhere Displays Projector: A Device toCreate Ubiquitous Graphical Interfaces. In Proceedings of the 3rd International
Conference on Ubiquitous Computing (Atlanta, Georgia, USA) (UbiComp ’01).
Springer-Verlag, Berlin, Heidelberg, 315–331.
[34] Ivan Poupyrev, Nan-Wei Gong, Shiho Fukuhara, Mustafa Emre Karagozler,
Carsten Schwesig, and Karen E. Robinson. 2016. Project Jacquard: Interactive Digital Textiles at Scale. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 4216–4227. https://doi.org/10.1145/2858036.2858176[35] Ivan Poupyrev, Chris Harrison, and Munehiko Sato. 2012. Touché: Touch andGesture Sensing for the Real World. In Proceedings of the 2012 ACM Conference
on Ubiquitous Computing (Pittsburgh, Pennsylvania) (UbiComp ’12). Association
for Computing Machinery, New York, NY, USA, 536. https://doi.org/10.1145/
2370216.2370296[36] Shanaka Ransiri, Roshan Lalintha Peiris, Kian Peen Yeo, and Suranga
Nanayakkara. 2013. SmartFinger: Connecting Devices, Objects and People Seamlessly. In Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration (Adelaide, Australia)(OzCHI ’13). Association for Computing Machinery, New York, NY, USA, 359–362.
https://doi.org/10.1145/2541016.2541064[37] Roy Shilkrot, Jochen Huber, Wong Meng Ee, Pattie Maes, and Suranga Chandima
Nanayakkara. 2015. FingerReader: A Wearable Device to Explore Printed
Text on the Go. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 2363–2372. https://doi.org/10.1145/2702123.2702421[38] Y. Song, Q. Hao, K. Zhang, M. Wang, Y. Chu, and B. Kang. 2011. The Simulation
Method of the Galvanic Coupling Intrabody Communication With Diferent Signal Transmission Paths. IEEE Transactions on Instrumentation and Measurement60, 4 (2011), 1257–1266.
[39] Virag Varga. 2019. Reinventing Touch with Body Channel Communication - System
Design from Electric Fields To Mixed Reality. Ph.D. Dissertation. ETH Zurich,
Zurich. https://doi.org/10.3929/ethz-b-000356345[40] Virag Varga, Gergely Vakulya, Alanson Sample, and Thomas R. Gross. 2018.
Enabling Interactive Infrastructure with Body Channel Communication. Proc.
ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 169 (Jan. 2018),
29 pages. https://doi.org/10.1145/3161180[41] Virag Varga, Marc Wyss, Gergely Vakulya, Alanson Sample, and Thomas R.
Gross. 2018. Designing Groundless Body Channel Communication Systems:
Performance and Implications. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (Berlin, Germany) (UIST ’18).
Association for Computing Machinery, New York, NY, USA, 683–695. https://doi.org/10.1145/3242587.3242622[42] Edward Jay Wang, Jake Garrison, Eric Whitmire, Mayank Goel, and Shwetak
Patel. 2017. Carpacio: Repurposing Capacitive Sensors to Distinguish Driver andPassenger Touches on In-Vehicle Screens. In Proceedings of the 30th Annual ACM
Symposium on User Interface Software and Technology (Québec City, QC, Canada)(UIST ’17). Association for Computing Machinery, New York, NY, USA, 49–55.
https://doi.org/10.1145/3126594.3126623[43] Martin Weigel, Tong Lu, Gilles Bailly, Antti Oulasvirta, Carmel Majidi, and JürgenSteimle. 2015. ISkin: Flexible, Stretchable and Visually Customizable On-Body
Touch Sensors for Mobile Computing. In Proceedings of the 33rd Annual ACM
Conference on Human Factors in Computing Systems (Seoul, Republic of Korea)(CHI ’15). Association for Computing Machinery, New York, NY, USA, 2991–3000.
https://doi.org/10.1145/2702123.2702391[44] Andrew D. Wilson and Hrvoje Benko. 2010. Combining Multiple Depth Cameras
and Projectors for Interactions on, above and between Surfaces. In Proceedingsof the 23nd Annual ACM Symposium on User Interface Software and Technology(New York, New York, USA) (UIST ’10). Association for Computing Machinery,
New York, NY, USA, 273–282. https://doi.org/10.1145/1866029.1866073[45] Robert Xiao, Julia Schwarz, Nick Throm, Andrew D. Wilson, and Hrvoje Benko.
2018. MRTouch: Adding Touch Input to Head-Mounted Mixed Reality. IEEE
Transactions on Visualization and Computer Graphics 24, 4 (April 2018), 1653–1660.

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

https://doi.org/10.1109/TVCG.2018.2794222[46] Zheer Xu, Pui Chung Wong, Jun Gong, Te-Yen Wu, Aditya Shekhar Nittala,
Xiaojun Bi, Jürgen Steimle, Hongbo Fu, Kening Zhu, and Xing-Dong Yang. 2019.
TipText: Eyes-Free Text Entry on a Fingertip Keyboard. In Proceedings of the 32nd
Annual ACM Symposium on User Interface Software and Technology (New Orleans,
LA, USA) (UIST ’19). Association for Computing Machinery, New York, NY, USA,
883–899. https://doi.org/10.1145/3332165.3347865[47] Xing-Dong Yang, Tovi Grossman, Daniel Wigdor, and George Fitzmaurice. 2012.
Magic Finger: Always-Available Input through Finger Instrumentation. In Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology(Cambridge, Massachusetts, USA) (UIST ’12). Association for Computing Machinery, New York, NY, USA, 147–156. https://doi.org/10.1145/2380116.2380137[48] Hui-Shyong Yeo, Juyoung Lee, Hyung-il Kim, Aakar Gupta, Andrea Bianchi,
Daniel Vogel, Hideki Koike, Woontack Woo, and Aaron Quigley. 2019. WRIST:
Watch-Ring Interaction and Sensing Technique for Wrist Gestures and MacroMicro Pointing. In Proceedings of the 21st International Conference on HumanComputer Interaction with Mobile Devices and Services (Taipei, Taiwan) (MobileHCI’19). Association for Computing Machinery, New York, NY, USA, Article 19,
15 pages. https://doi.org/10.1145/3338286.3340130[49] Sang Ho Yoon, Ke Huo, Yunbo Zhang, Guiming Chen, Luis Paredes, Subramanian
Chidambaram, and Karthik Ramani. 2017. ISoft: A Customizable Soft Sensor with
Real-Time Continuous Contact and Stretching Sensing. In Proceedings of the 30th
Annual ACM Symposium on User Interface Software and Technology (Québec City,
QC, Canada) (UIST ’17). Association for Computing Machinery, New York, NY,
USA, 665–678. https://doi.org/10.1145/3126594.3126654[50] Cheng Zhang, AbdelKareem Bedri, Gabriel Reyes, Bailey Bercik, Omer T. Inan,
Thad E. Starner, and Gregory D. Abowd. 2016. TapSkin: Recognizing OnSkin Input for Smartwatches. In Proceedings of the 2016 ACM International
Conference on Interactive Surfaces and Spaces (Niagara Falls, Ontario, Canada)(ISS ’16). Association for Computing Machinery, New York, NY, USA, 13–22.
https://doi.org/10.1145/2992154.2992187[51] Yang Zhang and Chris Harrison. 2018. Pulp Nonfction: Low-Cost Touch Tracking for Paper. In Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing
Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3173574.3173691[52] Yang Zhang, Wolf Kienzle, Yanjun Ma, Shiu S. Ng, Hrvoje Benko, and Chris
Harrison. 2019. ActiTouch: Robust Touch Detection for On-Skin AR/VR Interfaces.
In Proceedings of the 32nd Annual ACM Symposium on User Interface Software
and Technology (New Orleans, LA, USA) (UIST ’19). Association for Computing
Machinery, New York, NY, USA, 1151–1159. https://doi.org/10.1145/3332165.
3347869[53] Yang Zhang, Gierad Laput, and Chris Harrison. 2017. Electrick: Low-CostTouch Sensing Using Electric Field Tomography. In Proceedings of the 2017 CHI
Conference on Human Factors in Computing Systems (Denver, Colorado, USA)(CHI ’17). Association for Computing Machinery, New York, NY, USA, 1–14.
https://doi.org/10.1145/3025453.3025842[54] Yang Zhang, Chouchang (Jack) Yang, Scott E. Hudson, Chris Harrison, andAlanson Sample. 2018. Wall++: Room-Scale Interactive and Context-Aware
Sensing. In Proceedings of the 2018 CHI Conference on Human Factors in Computing
Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery,
New York, NY, USA, 1–15. https://doi.org/10.1145/3173574.3173847[55] Yang Zhang, Junhan Zhou, Gierad Laput, and Chris Harrison. 2016. SkinTrack:
Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the
Skin. In Proceedings of the 2016 CHI Conference on Human Factors in Computing
Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 1491–1503. https://doi.org/10.1145/2858036.2858082

A APPENDIXTo enable replication of this work, we provide schematics for boththe transmit driver and receiver front end. ElectroRing uses a PSoC 6
to sample the AC signal and extract the frequency of interest, butanother suitable SoC or MCU with external ADC would be appropriate to use as well.

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

Wolf Kienzle, Eric Whitmire, Chris Ritaler, and Hrovje Benko

0.1uF

ELEC5

R18

TX_P Electrode

OPA2836
U10

10R

C6

R20

R21

10k

10k

0.1uF

C8

OSC1
R23
10R
10.7MHz Square Wave

0.1uF

ELEC4

R19

TX_N ElectrodeU9
OPA2836

10R

C7

R22
V_BIAS
10k

Figure 11: Schematic diagram of ElectroRing transmit driver.

0.1uF

ELEC3
SHIELDU6
LT6253

10R

C5

Vcc

V_BIAS

R17

R14

U5

100k

LT6253

10R

R15
0.1uF

100k

C4

R16

0.1uF

ELEC2
RX_N ElectrodeU2
C1

LT6253

R9

R5
10k

R12

R13

1k00

1k00

V_BIAS

1k00
R3

R6

1k00

200R

F1
R1
402R

V_BIAS

R4
10k

ELEC1

0.1uF

RX_P Electrode

R2

R7

1k00

200R

U3
LT6253R10330R
10.7MHz Ceramic Filter

1k00

U4

0.1uF

LT6253ADC1ADC Internal to PSOC

C3

330R
R11

R8
U1
LT6253

V_BIAS
V_BIAS

C2

Figure 12: Schematic diagram of ElectroRing receiver analog front end. The shield electrode is biased at the half rail voltage.