ActiTouch: Robust Touch Detection for
On-Skin AR/VR Interfaces
Yang Zhang1 Wolf Kienzle2 Yanjun Ma2 Shiu S. Ng2 Hrvoje Benko2 Chris Harrison1
1

Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA 15213{yang.zhang, chris.harrison}@cs.cmu.edu

2Facebook Reality Labs
9845 Willows Rd, Redmond, WA 98052{wkienzle, yanjun.ma, shiung, benko}@fb.com

ABSTRACT

Contemporary AR/VR systems use in-air gestures or
handheld controllers for interactivity. This overlooks the skinas a convenient surface for tactile, touch-driven interactions,which are generally more accurate and comfortable than freespace interactions. In response, we developed ActiTouch, a
new electrical method that enables precise on-skin touch segmentation by using the body as an RF waveguide. We combine this method with computer vision, enabling a systemwith both high tracking precision and robust touch detection.
Our system requires no cumbersome instrumentation of thefingers or hands, requiring only a single wristband (e.g.,smartwatch) and sensors integrated into an AR/VR headset.
We quantify the accuracy of our approach through a user
study and demonstrate how it can enable touchscreen-like interactions on the skin.
Author Keywords

Touch interaction; Finger tracking; Touch segmentation;
Augmented reality (AR); Virtual reality (VR).
CSS Concepts

Human-centered computing → Human computer interaction(HCI) → Interaction devices.
INTRODUCTION

Today’s augmented and virtual reality systems (AR/VR)
generally rely on either handheld controllers (e.g., HTC Vive[14], Oculus Rift and Touch [6]) or in-air bare hand gestures(e.g., Leap Motion [19], Microsoft HoloLens [24]) for user
input. Both of these approaches excel at fluid, coarse-grained
input, but are weaker at fine-grained interactions, such as
typing on a virtual keypad. Indeed, it is rare to see closely
packed targets in contemporary AR/VR interfaces, and when
they do appear, extra care must be taken by the user.
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https://doi.org/10.1145/3332165.3347869

Figure 1. ActiTouch enables robust on-skin touch segmentation using a wristband emitter (A) and sensors integrated intoan AR/VR headset (B). For spatial tracking of fingers, we use
Leap Motion (C) as a proof of concept. Together, this enablesprecise touch input (D) on the skin for AR/VR interfaces (E).

Fortunately, other input modalities are possible, which can
improve the precision, bandwidth and comfort of AR/VR interactions. One such opportunity is input on the skin, which
offers a convenient surface for tactile, touch-driven interactions (Figure 1). Prior work has shown that by operating on
physical surfaces, users are often more accurate and reporthigher comfort than equivalent free-space interactions [22].
Further, the ability for users to position arm-borne interfaces
as they wish, in concert with increased input precision, affords greater privacy and may be less socially disruptive.
A wide variety of approaches have been considered to enable
on-skin input, ranging from wearing a conventional trackpad
on the body [32], to worn range-finding sensors [26]. However, most common are systems that use worn cameras andcomputer vision (e.g., [4, 8, 12, 15, 25, 38]), which are generally very accurate at tracking fingers spatially. OmniTouch[12], which is perhaps the most closely related prior system,
offers a useful benchmark: mean Euclidian finger tracking
error of 11.9 mm (SD=7.3) on the hand and forearm.
However, a common weakness across camera-based systems
is the inability to accurately segment true touches from fingers hovering just above the skin. With visible light (RGB)
cameras, there may be no perceivable difference. For thisreason, depth cameras are often used, offering distance information that can help disambiguate touching vs. hovering fingers. However, limited depth camera resolution and sensor
noise makes this surprisingly challenging [35]. This hoverambiguity makes end user touch interactions more cumbersome, with users often having to perform exaggerated (zaxis) trajectories to compensate. Again, using OmniTouch asa benchmark, only fingers “above 2 cm were reliably seen as
hovering” [12]. Of course, on devices like smartphones, users rarely lift their fingers this high when e.g., typing or
scrolling.
In response, we developed ActiTouch, a new electricalmethod that enables precise, low-latency touch segmentationby using the body as an RF waveguide. Our method compliments the spatial tracking strengths of computer vision approaches, enabling a combined system with both high accuracy finger tracking and robust touch segmentation. Importantly, our system requires no cumbersome instrumentation of the hands or fingers, requiring only a single wristband(Figure 1A) and a headset (Figure 1B). We quantify the accuracy of our approach through a user study and demonstrate
touchscreen-like interactions on the skin in AR/VR.
RELATED WORK

Our work intersects with two disparate areas of research.
Most related to our application domain are systems with onskin interactivity. Even more closely related to our technicalapproach are systems that leverage the body’s natural ability
to conduct electrical signals for interactive sensing purposes.
On-Skin Interfaces

The immediacy of skin for on-the-go AR/VR interactivity
has drawn attention from the HCI community for over a decade, starting with early work by Karitsuka et al. [15] andYamamoto et al. [38], both of which used over-the-shoulder
cameras and projectors. Many technical approaches havebeen considered, which we now briefly review.
One option is to directly modify the body, for example, byadding a sensing layer to the skin’s surface, as seen in iSkin[34]. It is also possible to implant sensors under the skin [13],though not all users are comfortable with such instrumentation, and so there has also been much work on less invasive
techniques that can sense from afar. For example, the body
is a good conductor of sound, and so many projects have utilized bio-acoustics to enable on-body interactions [11, 18,26]. Acoustic sensing has also been used above the skin for
touch input, using e.g., sonar [21].
Optical approaches are also popular, the simplest of which
use arrays of infrared proximity sensors integrated into worn
devices [16, 36, 37]. Using a fingerprint sensor, SkInteract[28] was able to detect discrete locations on a user’s hand.
Many tag-based finger tracking schemes have been used inconcert with worn cameras, including retroreflective [15],colored [25] and fiducial markers [29]. PalmBit [38] avoids
using markers by tracking the contours of fingers, though
only finger-to-finger touches are supported.
Projects such as OmniTouch [12], Imaginary Phone [8] andPalmRC [4] enabled continuous on-skin touch tracking without markers by using depth cameras, though as discussed in
the introduction, this approach has other drawbacks. Our

proof-of-concept system also uses computer vision for finger
tracking, though we use an off-the-shelf solution (Leap Motion [19]) and combine it with ActiTouch for touch segmentation. As we will show, this combination, though incomplete individually, is potent when unified.
Body as an Electrical Medium

More closely related to ActiTouch are electrical methods forenabling on-skin interactivity. For example, Botential [23]utilized electromyography (EMG) to detect gestures, while
AuraSense [40] used electric field sensing for in-air finger
tracking. However, these approaches do not use the body as
an electrical medium. ActiTouch takes advantage of the human body’s electrical conductivity, brought to light in the
HCI community by papers such as DiamondTouch [5], Humantenna [3], and EMSense [17]. Recently, Varga et al. [33]published an in-depth investigation of this phenomena, focused on implications for Body Channel Communication.
With respect to on-body interactions, there are three projectsof particular note. EnhancedTouch [30] and Enhanced
TouchX [9] leveraged modulated electrical current to detecthuman-human touch events, but did not consider a user
touching his/her own skin for input. Closest to this work is
SkinTrack [39], which used a ring emitter to inject RF into awearer’s arm when touch contact is made. A smartwatch on
the opposing arm contained multiple receiver electrodes thatwere used to detect and track finger touches by comparing
the relative phase of received signals. This arrangement required both hands to be instrumented (i.e., not a single smartwatch, as we propose) and did not consider integration into
AR/VR headsets. We also explore a unique fusion between
RF sensing and computer vision, leveraging their strengths
whilst sidestepping weaknesses.
SENSING PRINCIPLE

ActiTouch leverages the conductivity of the human body,which conveniently serves as a transmission medium for RF
signals. When a user wears an emitter on the arm, the RF
signal mainly flows between the two electrodes (P1, Figure
2 left). When a user touches the other arm, a second path (P2)
is formed (Figure 2 middle). We also found that P2 increases
airborne RF radiation. By placing receiver electrodes in a location that is both along the path of P2 and proximate to airborne radiation, we can use P2 as a touch detection mechanism. Although an ideal setup would be to integrate the receiver into the emitter wristband, we found that proximity to

Figure 2. Dominant RF signal paths before (left) and after
touch (middle). The two electrodes on a headset receiver capacitively couple to P2 for touch detection (right).

Received Signal Amplitude (Vpp)

5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
CopperFigure 3. Average received signal when the receiver isplaced outside (left) and inside (right) the circuit.

P1 provided poor signal-to-noise ratio (SNR). Another possibility is to use a receiver worn on the opposite wrist, but
this means users must wear two wrist-worn devices, which isless typical. Instead, we opted to use the head (Figure 2
right), where an AR/VR headset offers an existing platform
for instrumentation.
We conducted a basic study to verify our sensing principle(5 participants, 2 female, mean age 24). To better reflect awearable scenario (i.e., no wall power, small ground plane),
we used a battery-powered AD5930 [2] to generate an RF
signal and a battery-powered Bluetooth-enabled ADC for
measurements. The emitter and receiver used two 2×2 cm
copper electrodes attached longitudinally to the user’s arm.
The emitter was worn on the wrist of the touching hand, andthe receiver sat on the opposite wrist (Figure 3). The emitter
was configured to output a 9 Vpp signal at 10.5 MHz (foundto be optimal in [10]). Our hypothesis was that placing thereceiver inside P2 should yield a stronger received signal.
After wearing the two wristbands, participants were asked touse their right index finger to touch on their opposing palmand forearm. These two locations meant the receiver wristband was inside and outside P2 respectively (Figure 3). At
each touch location, we collected 30 data points with participants touching the skin, and 30 data points with fingers hovering ~1 cm above the skin. We calculated the amplitude difference between touching and hovering as an indicator of P2.
We found that when the receiver wristband was outside P2,
the on-touch signal delta was 0.21 Vpp on average. When thereceiver was placed inside P2, the measured on-touch signal
delta increased to 2.30 Vpp. This lends credence to ourmodel, though we caution this is a preliminary result.

Copper + Tape Silver Fabric

Dry Medical Wet Medical
Electrode
ElectrodeFigure 4. Received signal amplitude across electrodematerials. Error bars are standard deviation (variation
mostly comes from swept frequency measurements).

mean age 23) and collected data from three body locations:front of the arm, back of the arm, and forehead.
We averaged these results, shown in Figure 4. Among allelectrode materials, the silver-based fabric [1] provided thestrongest received signal, and thus we adopted it for our final
implementation. However, copper with Kapton tape yielded
the most consistent response across all signal frequencies andwas therefore used for subsequent characterization studies.
ELECTRODE PLACEMENT INVESTIGATIONThe next factor that significantly effects on-body RF sensing
is electrode placement, which we also investigated. Priorwork has mainly studied two electrode configurations – capacitive and galvanic – which inspired our electrode placement study design. In a capacitive configuration (Figure 5top), the signal electrode is attached to a user’s skin while theground electrode is floating, capacitively coupling to earth
common ground. In a galvanic configuration, both signal andground electrodes are attached to a user’s body. We studied
two arrangements in this configuration – transversal and longitudinal (Figure 5, middle and bottom). We investigated all
pairings of emitter and receiver configurations, resulting in a
3×3 study design (Table 1).
Four participants were recruited for this study (2 female,mean age 24). For all nine emitter-receiver combinations, we
configured the emitter to sweep from 100 kHz to 12 MHz at

ELECTRODE MATERIAL INVESTIGATION

Efficient and reliable injection of AC signals into the humanbody requires careful material design of the electrodes. We
investigated five materials: copper, copper coated by a thinlayer of Kapton tape, silver fabric, dry medical electrode, andwet medical electrode. For each material, we made a pair of
2×2 cm electrodes (except for the dry medical electrode,which came as 1 cm disks). Each pair of electrodes was attached to a user’s skin 10 cm apart. We then inserted a 9 Vpp
swept-frequency signal (100 kHz to 12 MHz) with one electrode, while measuring the received signal amplitude fromthe other electrode. We recruited three participants (1 female,
Figure 5. Electrode configurations we studied.

Figure 6. Studied touch locations on the uninstrumented arm.

9 Vpp. We measured the received signal strength at nine
touch locations (Figure 6) while the participant was hovering~1 cm above the skin and touching the skin, recording theamplitude before and after touch. Results are shown in Table
1 (combining all participants, touch locations and frequencies for each emitter-receiver configuration). We found theconfiguration that used the galvanic longitudinal electrodeplacement for both the emitter and receiver yielded the best
on-touch signal. We also used our swept frequency data to
confirm 10.5 MHz offered the best SNR.
SKIN SAFTY INVESTIGATION

To test if ActiTouch is skin-safe, we measured the current
inserted into a user’s body using the configuration identified
in the previous sections (i.e., galvanic longitudinal, 2×2 cm
silver fabric electrodes placed 2 cm apart, 10.5 MHz excitation signal frequency). The injected current was estimated bymeasuring the bio-impedance at the wrist.
Four participants were recruited for this test (1 female, mean
age 44). For each participant, we measured the peak andRMS voltages of the open (OC) and closed (L) circuit usinga Tektronix MSO58 oscilloscope on the back of the hand andthe palm. The bio-impedance is calculated using the measured voltage and the source impedance:
Zbio = Zs (VOC/VL -1), where Zs = 50 Ohm.
We measured an average RMS bio-impedance of 420 Ohm(SD=266). Thus, the maximum contact current should be~10 mA when the output voltage is configured at 9 Vpp. Despite an extensive search, we did not find any research linking this frequency range and current to negative health effects, though we note that long-term studies are ongoing.
IMPLEMENTATION

Our proof-of-concept implementation of ActiTouch (Figure7) required custom electrodes, PCBs, software, and wristand head-worn hardware. We now describe each of thesecomponents in detail.
Electrodes

As previously discussed, we found a silver-based conductive
fabric [1] to offer the strongest skin-electrode coupling
among the electrode materials we tested. We cut this fabricinto small patches that we integrated into two worn components – a wristband emitter and a headset receiver (Figure 7).
To electrically connect the fabric to our sensor boards, weused SMA cables soldered to fabric snap fasteners.

Table 1. Average on-touch signal delta for our
various electrode configurations (unit mVpp).

K20P64M72SF1 microcontroller [7] running at 96 MHz
with Teensy firmware [27]. The AD5930 is configured to
output a 10.5 MHz signal at 200 mVpp. We remove the DC
component and amplify this signal to 9 Vpp. The board ispowered by a 3.7 V lithium ion polymer battery. For electrodes we use two 2×2 cm silver-fabric electrodes with a
2 cm spacing. All of these components are affixed to an elastic Velcro strap worn on the wrist (Figure 7, left two images).
Receiver Headset

Our receiver board features a two-stage differential signal
amplification analog frontend with a gain of 10, built arounda LT1806 opamp [20]. The amplified signal is then DC biased to 1.5 V with a voltage reference chip [31] and sampled
at 2 MHz by our microcontroller’s built-in ADC. Due to under-sampling, we actually measure an alias of our 10.5 MHz
emitted signal. Raw measurements are sent to a laptop overBluetooth at 50 FPS for additional processing (describednext). The receiver board is powered by a 3.7 V lithium ion
polymer battery. For electrodes, we found the soft region below the eyes to offer the most reliable skin-to-electrode contact (see Figure 7, headset receiver, side view).
Touch Tracking Pipeline

Our touch tracking software runs on a 4-core Intel i7 laptop.
After our software receives measurements from our receiverboard over Bluetooth, it computes an FFT (non-overlapping
window of size 128). We use the FFT bin that contains 10.5
MHz as the RF signal strength indicator for touch segmentation. Due to changes in user posture and varying proximityof the hands to the head, it is not possible to use the raw amplitude of the received signal for touch detection. Instead, we
use the first derivative – a sudden and significant increase insignal amplitude indicates a Finger Touch Down event,whereas a sudden decrease indicates a Finger Touch Up
event. This signal is sufficiently clean and characteristic that
we can use fixed thresholds with basic hysteresis.
To track a user’s fingers and arms in 3D, we used a Leap
Motion [19] camera (Orion SDK) attached to the front of ourheadset. Specifically, we track the index finger and its distance to the opposing palm and arm planes. If the finger gets

Emitter Wristband

Our emitter board is built around an AD5930 signal generator chip [2]. To interface with this chip, we used a Freescale

Figure 7. ActiTouch wristband (left two images)
and headset (right two images).

Figure 8. Overview of touch segmentation algorithm. Dfs isthe Leap-Motion-reported distance between the fingertip
and palm/arm surface; DeltaRF is the first derivative of thereceived amplitude; Vt is the transition threshold.

closer than 3 cm to one of these interactive planes, the detection pipeline changes from No Touch to a Finger Hover state.
While in this state, if the first derivative of the received signal
is above a threshold, the system moves to a Finger Touchstate. If the first derivative exceeds a second, negative threshold, the system moves back to a Finger Hover state. If thefinger moves further than 3 cm away from either the palm orarm, the state returns to No Touch. Figure 8 offers an overview of this algorithm.
Latency

To measure the touch segmentation latency of ActiTouch,
we took repeated measurements with a high-speed camera.
End-to-end latency – from the moment the finger touches theskin to the instant the computer displays the touch event –was 141 ms on average. In the future, this could be improvedby forgoing Bluetooth transmission and laptop processing,and instead performing all compute on processors found innewer AR/VR headsets.
USER STUDY

To evaluate ActiTouch tracking performance, we ran a user
study with 10 participants (2 female, mean age 26, all righthanded). This took approximately one hour and participantsreceived $10 for their time.
We considered several options for ground-truthing touch
events, including a Vicon optical tracking setup and a sidemounted depth camera. However, we found in piloting there
were significant challenges due to finger curl and angle ofattack, as well as complex geometry at the point of contactthat necessitated sub-millimeter tracking. Fortunately, we
found that human observers were surprisingly accurate, as
they could observe the kinematics of finger impacts and thereaction of the limb being touched (e.g., skin deformation,
deflection of the limb). For these reasons, we used humanobservers to confirm touch events in the study.
Procedure

We evaluated ActiTouch at three body locations: palm, backof the hand, and inside of the forearm. These areas are relatively flat and sufficiently large for touch interactions. Aftera brief participant orientation, we drew 30mm-interval crosshairs on the three body locations – 3×3 for the palm and backof the hand, and 2×4 for the forearm (Figure 9). We thenasked participants to wear our headset and wristband. The
headset was hollowed-out (i.e., no screen; Figure 7) so thatparticipants could see their arms. This was done to remove
confounding experimental effects, such as VR latency andtracking offsets.

Figure 9. Requested touch locations (dashed crossings) and thetracked touch points (blue dots) from all participants. Also
shown are 2σ confidence ellipses for each crosshair.

We tested the three body locations sequentially, in a random
order. For each location, we collected three rounds of data,and in each round, we collected 10 data points for each crosshair. Crosshairs were automatically requested on an adjacent
laptop screen. After the experimenter confirmed the participant performed a touch (note the detected touch state washidden from both the participant and experimenter), the experimenter advanced the study software to the next trial. The
experimenter only intervened if a trial was not performed as
requested (e.g., wrong crosshair was touched).
After Touch data was collected, we attached a small 5mmtall transparent acrylic (i.e., not conductive) cylinder to theparticipant’s fingertip and repeated the above procedure(note this cylinder was fully occluded by the finger and not
visible from the perspective of the Leap Motion). This session collected Hover data so that we could investigate false
positive instances when the finger was very close, but nottouching the skin.
In total, this procedure (10 participants × 26 crosshairs × 3
rounds × 10 data points) yielded 7800 Touch and 7800 Hover
trials. Only live touch state output by ActiTouch was recorded (i.e., no post hoc calibration or processing).
Touch Segmentation Accuracy

On average, ActiTouch achieved a touch segmentation accuracy of 93.8% (SD=2.0). Figure 10 shows the accuracy broken out by body location. Of the errors, 24.2% were false
negatives (i.e., Touch recognized as Hover) and 75.8% werefalse positives (i.e., Hover recognized as Touch).

Figure 10. Touch segmentation accuracies across our threebody input locations. Error bars are standard deviation.

As one point of comparison, we ran a post hoc touch segmentation analysis using only the 3D data reported by the
Leap Motion (i.e., a CV-only approach). We varied thetouch/hover threshold from 1 to 50 mm (in 1 mm increments)
and re-ran our logged data. The best average touch segmentation accuracy achieved (i.e., assuming the ideal threshold
was known a priori) was 55.5% (SD=1.6).
Touch Tracking Accuracy

We also evaluated the touch tracking spatial precision of our
combined ActiTouch plus Leap Motion pipeline. To calibrate the Leap Motion, we used the first touch trial on eachcrosshair from each participant to compute a calibration matrix for each body location, which we applied to that participant’s study data. We then calculated the Euclidian distanceerror between the reported touch locations and the actual requested touch locations. We included false positives trials(i.e., hover trials detected as touches) to better reflect realworld performance. We found that ActiTouch achieved amean distance error of 5.3 mm (SD=1.1). Figure 9 plots all
touch trials from our 10 participants, along with 97.8%(mean + 2σ) confidence ellipses.
Supplemental Study: Continuous Drawing

To test the continuous touch tracking capability of ActiTouch, we asked the same group of participants to draw fivedifferent shapes on their palms (Figure 11, top left), repeatedthree times each, as naturally as they would on a conventional touchscreen. The crosshairs drawn on the skin fromthe previous study were used as guides to provide a unified
scale. Figure 11 shows the raw drawn paths superimposedfrom all participants, with no post-hoc corrections or peruser calibration.

receiver, though this arrangement requires instrumentationof both wrists, which as noted earlier, is less desirable.
EXAMPLE APPLICATIONS

To illustrate the utility of our system, we created three example applications: a dial pad, a music player, and a drawing
app. Similar on-skin interfaces have been demonstrated inprior work, but our versions of these applications featuredenser interactors, taking advantage of ActiTouch’s hightouch segmentation accuracy. Please also see Video Figure.
Our dial pad app, seen in Figure 1, is automatically placedon the palm and features the standard 3×4 grid of buttons. On
the longer forearm region, we place a scrollable list of contacts. A scroll bar for fast alphabetical navigation is locatedon the middle finger. Figure 12 (top) shows a screenshot of
our music player application, which uses contemporary
scrolling and swiping gestures to navigate between songs, aswell as buttons for pause/play and volume control. Finally,
we created a drawing app (Figure 12, bottom) to highlightfine grained continuous tracking (beyond directionalswipes). In addition to the main canvas area on the palm, thefingers are used as buttons (save, pause/play, erase/paint toggle) and sliders (controls for brush thickness, brush color).
DISCUSSION

Despite respectable touch segmentation accuracy, ActiTouch has several notable limitations. First, good skin-electrode contact is key to achieving strong and reliable RF signal; if users prefer looser fitting of the wristband or headset,
there will be a reduction in performance. Second, althoughmulti-finger touches result in stronger received signal thansingle-finger touches, our current system cannot reliably distinguish these two events. One potential workaround is toSupplemental Study: Wristband Receiver

Finally, we ran a variant of our study that used a wrist-wornreceiver (worn on the opposite arm to the wrist-worn emitter), instead of the receiver integrated into a headset. This
study used the same procedure as our main study, as well as
the same set of participants. In this arrangement, ActiTouchachieved a mean touch segmentation accuracy of 95.8%(SD=2.3), with a mean tracking distance error of 4.3 mm(SD=0.7). This is slightly more accurate than our headset

Figure 11. Requested shapes superimposed (top left) onto thecrosshairs (green) that were drawn on participants’ palms.
Remainder of figure are paths drawn by all participants withno post-hoc or per-user calibration.

Figure 12. Example applications: a music player (top),
and a drawing app (bottom). See Figure 1 for dial pad app.

leverage computer vision, which could predict the number oftouching fingers based on hand pose.

Video (EuroITV '12). ACM, New York, NY, USA, 2734. DOI: https://doi.org/10.1145/2325616.2325623

As our headset receiver is sensitive to airborne radiationfrom the emitter, some poses (especially those that bring thearms closer to the head) cause interference. For this reason,
we used the first derivative of the received signal, as poses
tend to change less rapidly than the instantaneous touchingof a finger to the skin. Nonetheless, we still encountered false
positives, and this remains an obstacle for future work. Likewise, the amplitude of the first derivative was impacted byuser pose and distance between emitter and receiver, and thus
a dynamic threshold should be employed in future systems.
Finally, we found that touching large conductive surfaces(e.g., laptops, magnetic whiteboards, appliances with metal
enclosures) amplified the received signal. In the future, thiseffect might be used to support on-world touch interactions.

[5] Paul Dietz and Darren Leigh. 2001. DiamondTouch: amulti-user touch technology. In Proceedings of the
14th annual ACM symposium on User interface software and technology (UIST '01). ACM, New York,NY, USA, 219-226. DOI:
http://dx.doi.org/10.1145/502348.502389

CONCLUSION

Skin is a convenient and comfortable touch input surface for
AR/VR interactions, where contemporary input approaches
are generally performed in free space, limiting precision. To
advance this vision, we developed ActiTouch, a novel touchsegmentation technique that uses the human body as an RF
waveguide. Users need only wear one wristband emitter(which could be integrated into future smartwatches) and ahead-worn receiver (which can be easily incorporated into
AR/VR headsets). We conducted a series of investigations tocharacterize RF signal propagation in the body, as well as
optimize electrode material and placement. We also ran auser study to evaluate the performance of ActiTouch, usinga Leap Motion as an exemplar computer vision system toprovide finger tracking data. Our results show a mean touchsegmentation accuracy of 93.8% and a mean tracking errorof 5.3 mm. While future work remains, we believe ActiTouch illuminates a promising new way to enable robust
touch sensing in a practical form factor.
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