=Paper=
{{Paper
|id=Vol-2996/paper1
|storemode=property
|title=Low-Cost Millimeter-Wave Interactive Sensing through Origami Reflectors
|pdfUrl=https://ceur-ws.org/Vol-2996/paper1.pdf
|volume=Vol-2996
|authors=Riku Arakawa,Yang Zhang
|dblpUrl=https://dblp.org/rec/conf/ewsn/ArakawaZ21
}}
==Low-Cost Millimeter-Wave Interactive Sensing through Origami Reflectors==
https://ceur-ws.org/Vol-2996/paper1.pdf
Low-Cost Millimeter-Wave Interactive Sensing
through Origami Reflectors
Riku Arakawa Yang Zhang
The University of Tokyo University of California, Los Angeles
riku.arakawa1996@gmail.com yang.zhang@cs.cmu.edu
mm-wave sensor
order controller kit fold origami calibrate controllers use controllers
Figure 1: We envision a future scenario where users can (a) buy cheap materials (e.g., origami), (b) assemble to make controllers
by themselves, (c) calibrate the controllers in their own environment guided by a dedicated app, and (d) deploy them as low-cost
controllers for achieving ubiquitous interactivity. Please also see our Video Figure1 for more details.
ABSTRACT 1 INTRODUCTION
Millimeter-Wave (mm-wave) sensing provides an increasingly vi- Millimeter-Wave (mm-wave) sensing poses an inviting opportunity
able sensing solution for smart environments for its compact and for ubiquitous sensing for being low-cost, compact, and privacy-
solid-state form factor, non-intrusiveness, and low cost. While prior sensitive – key properties that make commercial integrations possi-
work in this domain has mostly focused on sensing humans – e.g., ble. Recent years have seen an increasing trend of mm-wave sensing
location, motion, and posture, we propose a new approach that techniques featured on consumer products such as smartwatches,
leverage mm-wave sensing to enable tangible ubiquitous controllers phones, autonomous vehicles, as well as home devices such as
such as buttons and switches. By encoding the controller state with occupancy sensors, smart lightbulbs, and thermometers. In these
the Radar Cross Section (RCS) of origami structures, our component- systems, mm-wave sensors emit structured RF waves into user
free controllers cost less than 40 cents per unit and require virtually environments and decode the reflectance signals to infer user infor-
zero maintenance effort, while achieving long-range wireless sens- mation such as presence, proximity, hand gestures, body postures,
ing with sufficient accuracies. and beyond.
Meanwhile, conventional sensing techniques for ubiquitous in-
teractivity have several constraints that have prevented smart en-
CCS CONCEPTS vironments from being widely adopted across society. Existing
• Human-centered computing → Ubiquitous and mobile com- controllers such as switches and buttons are often wired, elimi-
puting systems and tools. nating flexible deployments. Though, it is possible to enable flex-
ible deployments with batteries and wireless transceivers, these
components inevitably increase the maintenance and monetary
KEYWORDS costs. In response, prior work proposed interactive sensing tech-
Millimeter Wave, Ubiquitous Computing, Battery-free, DIY niques around computer vision [27], capacitive sensing [32], and RF
backscatter [14]. Researchers have also leveraged user interactions
as sources of power to eliminate the need for batteries [3, 24].
In this work, we leverage the increasingly popular mm-wave
sensing technique to build wireless interactive controllers that are
designed around origami-inspired structures. While most prior
work on mm-wave sensing focused on sensing direct signals from
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). users, our system senses objects – controllers that encode user
interactions into their radar cross section (RCS), which we can
detect to infer user interactions wirelessly at a room-scale. More-
over, our controllers consist of only common everyday materials
1 https://youtu.be/BaLlQNVRVrI
CHIIoT 1, February 17, 2021, Delft, The Netherlands Riku Arakawa and Yang Zhang
(e.g., cardboard papers, aluminum foil), which are low-cost and several commercial products such as QIACHIP switches [21] and
component-free, enabling users to deploy them across their envi- EnOcean switches [4].
ronments with negligible cost or maintenance effort. In this paper, we propose an approach for achieving battery-free
Specifically, we designed a series of origami-inspired controllers wireless controllers that consist of only ultra-low-cost everyday
to implement conventional controls, including ones with discrete materials in origami forms, working in conjunction with mm-wave
states such as buttons and switches, as well as ones with continuous sensing. The proposed approach enables users to deploy controllers
states such as knobs and sliders. We built a signal processing and de- with negligible cost and effort to maintain. Furthermore, in the
tection pipeline around a Frequency-Modulated Continuous Wave future, we expect our work to facilitate users making and replicating
(FMCW) radar and conducted a series of evaluations to demonstrate these paper-based controllers easily on their own, enabling DIY
our system’s feasibility. Overall, we believe our system proposes a smart environment experience.
promising approach to achieving ubiquitous interactivity.
3.2 RF Sensing in HCI
2 INSPIRATIONS On the technology front, our approach is closely related to systems
that leverage RF sensing. RF sensing has been actively integrated
This work took much inspiration from prior literature, including
into applications in our lives. One of its representative uses is
recent effort in HCI that leveraged paper as interaction medium,
in autonomous vehicles where mm-wave radar is used primarily
where researchers empowered paper with sensing [14, 30] and
for detecting objects around the vehicle (e.g., other vehicles and
power generation [2, 11]. We were also inspired by a vast amount
pedestrians) [8, 26, 35]. Recently, Prabhakara et al. [20] showed
of online resources on origami structures in the design of our con-
that a wireless approach to sensing tire wearing is also possible by
trollers. Finally, our project chose to follow the same low-cost and
using mm-wave.
do-it-yourself spirit as Nintendo Labo [17], a phenomenal inter-
Closer to our work are prior systems that focus on interactivity
action design concept for console games. For these reasons, we
in HCI. For example, Soli [15] is a ubiquitous gesture sensing tech-
chose paper, one of the most accessible and tangible materials to
nology based on FMCW mm-wave sensing, which allows precise
build our controllers. We envision a future where these paper-based
finger tracking at close range [25]. The same mm-wave sensing
controllers can be easily accessed and assembled by average home-
has also been applied for classifying proximate material and object
owners to facilitate smart environment interactivity (Figure 1).
[28, 29]. Moreover, similar FMCW-based sensing using RF signals
are proposed for capturing human pose and motion even when they
3 RELATED WORK are occluded from the device or in a different room [1, 33, 34]. These
To situate our work, we first review previous approaches proposed approaches have been expanded to be capable of identifying users
for room-scale interactions. We also review how RF sensing has by analyzing the signal reflections and been utilized for collecting
been utilized to power interactions in HCI. behavioral data in homes [6].
Ultra-wideband (UWB) radar has been another common RF sens-
ing technique used for various purposes such as localizing sur-
3.1 Room-Scale Interactions rounding IoT devices and enhancing interactions with them [7, 10].
Many previous works have aimed to achieve room-scale interac- MechanoBeat [18] is a low-cost mechanical tag that can work with
tivity by deploying ubiquitous sensing modules based on various UWB radar arrays for unobtrusively monitoring user interactions.
principles. Vision-based sensing approaches have been widely pro- Additionally, the RF Doppler effect can also be utilized for detecting
posed [13, 27]. For example, WorldKit [27] is a system that uses movements of targets. For example, Goel et al. [5] leveraged the
a depth camera and a projector to make ordinary surfaces (e.g., effect to detect facial gestures by monitoring user’s tongue, cheeks,
walls) interactive. SurfaceSight [13] is a LiDAR-based sensing sys- and jaw movements.
tem that enriches IoT experiences by enabling sensing context on As shown in these works, while most previous works have been
table surfaces. On the other hand, sensing approaches without re- focusing on sensing direct signals from users, we aim to sense the
lying on vision sensors have also been investigated. Wall++ [32] states of objects as controllers that encode user interaction. In detail,
is a capacitive sensing approach for allowing walls to become a we propose an approach to utilizing origami-inspired structures as
smart infrastructure that senses users’ touch and gestures. More- ultra-low-cost reflectors that can change their shapes upon user
over, lasers have been utilized for sensing room-scale interactions interaction. We expect that this shape change can result in unique
from a distance [19, 31]. RCS values which can be sensed remotely with a mm-wave radar.
Meanwhile, there are also wireless sensors which allow flex- In Section 5, we explain how our origami-based controllers were
ible installation and portable use (e.g., TV remotes). Still, these designed.
controllers are often battery-powered, which requires user main-
tenance (e.g., battery replacement). In response, there have been 4 SENSING HARDWARE
developed battery-free wireless controllers. For example, The Pep-
We used the Infineon Position2Go 2 , a 24GHz radar sensor devel-
permill [24] utilizes human operation as a source of power, and
opment kit utilizing BGT24MTR12 RF transceiver and XMC4700
PaperID [14] uses RF backscatter to sense how a user is manipulat-
32-bit ARM® Cortex®-M4 MCU series, which costs approximately
ing RFID-instrumented paper. In addition, Iyer et al. [9] embedded
$300. The sensor has one transmitter (Tx) and two receivers (Rxs)
backscatter structures into 3d-printed objects to make wireless sen-
sors such as buttons, knobs, and sliders. Finally, there have been 2 https://www.infineon.com/cms/en/product/evaluation-boards/demo-position2go/
Low-Cost Millimeter-Wave Interactive Sensing through Origami Reflectors CHIIoT 1, February 17, 2021, Delft, The Netherlands
and its board size is 50 mm × 45 mm. The horizontal and vertical Interaction Off State On State
field of view are 76° and 19°, and the minimum and maximum dis-
tance for sensing are 1 m and 25 m. The sensor streams raw data
to a PC via USB, with 2.5 W power consumption. We utilized the Button
officially provided Matlab APIs to receive the streamed data and
developed our detection algorithm, which will be described later 0.13 dB 0.83 dB
in Section 7. Note in the equation below that the received power
(𝑃𝑟𝑥 ), which is calculated through FFT computation on raw radar
measurements, is linearly proportional to RCS of a target object (𝜎)
Toggle
at a fixed distance to the radar (𝑅), with a constant scale factor of
Switch
transmitted power level (𝑃𝑡𝑥 ), transmitter gain (𝐺𝑡𝑥 ), receiver gain
-0.45 dB 3.23 dB
(𝐺𝑟𝑥 ), and signal wavelength (𝜆):
Figure 2: Two types of discrete controllers (top: button, bot-
𝑃𝑡𝑥 𝐺𝑡𝑥 𝐺𝑟𝑥 𝜎𝜆 2 tom: toggle switch). These controllers have two discrete
𝑃𝑟𝑥 =
(4𝜋) 2 𝑅 4 states (i.e., on and off).
As a result, we treated the received power as an indicator of RCS
in the rest of this paper. • Knob (zigzag-fold): This controller works as a knob and
has continuous states. It consists of a zigzag-fold and a 3d-
5 REFLECTOR DESIGN printed handle attached to the origami. The zigzag-fold part
Our design rationale is to create shape-changing reflector struc- gradually opens as the handle is rotated in one direction and
tures that can 1) be actuated by force at a single point, and 2) result closes as it is rotated in the opposite direction.
in distinctive RCS at different shapes. To explore potential origami • Slider (Miura-fold): This controller works as a slider and has
models suitable for our purpose, we first looked into a variety of continuous states. It consists of a Miura-fold [16] origami and
existing works available on the Internet. We anticipated that struc- a 3d-printed handle. The Miura-fold part gradually expands
tures consisting of mutually perpendicular surfaces would have and shrinks as the handle is manipulated by a user.
high RCS values. This is inspired by the fact that corner reflectors, We measured RCS changes of each controller when they mor-
which have three mutually perpendicular intersecting flat surfaces, phed. To do this, we first recorded signals without a controller as
reflect waves directly towards the source, resulting in high RCS a base signal. Then, we placed a controller perpendicularly, 1 m
values [12, 22]. away from the sensor. We recorded the signals with a controller
We found four designs that could be used for our controllers. in each state and calculated the ratio of their amplitude to that of
In general, all form-changing origami designs change their RCS the base signal, measured in dB. Overall, We found significant RCS
when morphing. However, as we anticipated, the four selected changes between different states of the controllers. We elaborate an
origami designs feature mutually perpendicular surfaces, which algorithm as to how these values are utilized for detection later in
get distorted during the folding and unfolding process, resulting in Section 7 and document the evaluation of our system in Section 8.
significant RCS changes. For example, we incorporated Miura-fold
[16] into one controller that forms such perpendicular surfaces 6 USER INTERACTION
when its structure is gradually expanded. Herein, we show the Before explaining the sensing algorithm, we describe our envisioned
current controller designs: Button, Toggle Switch, Knob, and Slider future scenario of how users will set up and use our proposed
(see Figure 2 and Figure 3). We used conventional silver origami, origami controllers (Figure 1). First, a user purchases controller tool
paper with aluminum films coated on the surface, to further improve kits of interest from a distributor. These tool kits allow the user to
the RF reflectance of surfaces. Our total material cost is less than easily make controllers from pieces on their own. Then, the user
40 cents per controller unit. opens a dedicated smartphone or smart speaker app to initiate the
• Button (umbrella-fold): This controller works as a push-and- setup. The app will prompt the user to identify mm-wave sensors
pull button and has discrete states (i.e., on and off). It consists in their room and origami controllers within the sensing range,
of an umbrella-fold and a 3d-printed handle attached to the and start the calibration process. For calibration, he user collects
origami. The umbrella part opens when the handle is pushed a small amount of sample data corresponding to each state of the
and closes when it is pulled. controller, and the our system is ready to use.
• Toggle Switch (corner reflector): This controller works as a
toggle switch and has discrete states (i.e., on and off). It is a 7 SENSING ALGORITHM
corner reflector structure made of cardboard papers coated Our detection approach is based on FMCW sensing [23]. At each
with silver origami. The three surfaces get mutually per- frame, the Tx transmits 𝑁𝑐ℎ𝑖𝑟𝑝 chirps and there are 𝑁𝑠𝑎𝑚𝑝𝑙𝑒 sam-
pendicular in the on state. This structure is disrupted when ples in each chirp, resulting in a reflected signal matrix: 𝑋𝑟𝑎𝑤 ∈
the switch is turned off. Users interact with the attached C𝑁𝑠𝑎𝑚𝑝𝑙𝑒 ×𝑁𝑐ℎ𝑖𝑟 𝑝 . When we apply fast Fourier transform (FFT) with
3d-printed handle to switch between the two states. the size of 𝑁 𝑓 𝑓 𝑡 , we get 𝑋 𝑓 𝑓 𝑡 ∈ C𝑁 𝑓 𝑓 𝑡 ×𝑁𝑐ℎ𝑖𝑟 𝑝 = 𝐹 𝐹𝑇 (𝑋𝑟𝑎𝑤 ). Then,
CHIIoT 1, February 17, 2021, Delft, The Netherlands Riku Arakawa and Yang Zhang
Interaction 0% 25 % 50 % 75 % 100 %
Knob
-0.22 dB 0.52 dB 0.89 dB 1.28 dB 1.42 dB
Slider
-0.49 dB 0.55 dB 0.83 dB 1.57 dB 2.29 dB
Figure 3: Two types of continuous controllers (top: knob, bottom: slider). We expanded each controller from 0% to 100% with
25% interval.
the largest value in each row (i.e., over 𝑁𝑐ℎ𝑖𝑟𝑝𝑠 chirps) was taken
azimuth angle
out after calculating the amplitude, resulting in a frame vector angle of incidence
{0°, 15°, 30°, 45°}
𝑥 ∈ R𝑁 𝑓 𝑓 𝑡 ×1 = max |𝑋 𝑓 𝑓 𝑡 |. Each of the values in the 𝑁 𝑓 𝑓 𝑡 bins {0°, 15°, 30°, 45°}
corresponds to the power of the reflected signal within specific
𝑐 , where mm-wave direction
ranges from the sensor. The range resolution is given by 2𝐵 1m
𝑐 is the light speed and 𝐵 represents the used bandwidth.
Our current implementation ignores frames that contain non-
reflector norm
negligible human body movements that interfere with our RCS
room for testing test for angle of incidence
sensing. To do this, we applied a simple threshold-based algorithm.
In detail, we calculated the difference of two consecutive frames,
say 𝑥𝑡 and 𝑥𝑡 +1 , and classified the frame 𝑥𝑡 +1 as containing human Figure 4: Setting of the pilot test for discrete detection. We
body movements if the norm of the difference (i.e., |𝑥𝑡 +1 − 𝑥𝑡 |) is placed a controller to the gray positions and tested the detec-
larger than a predefined threshold. Note that the current detection tion accuracy by changing its distance, azimuth angle, and
algorithm requires users to exit the controller proximity after in- angle of incidence.
teraction. This means our system only works in an asynchronous
manner, where there could be a lag between user interaction and
its detection. and linear, and thus our straightforward regression models worked
Once a frame is detected as not containing human body move- sufficiently well in practice.
ments, the frame is processed for detecting the controller’s state.
Here, we assume that the system knows the bins of the feature 8 PILOT TEST
vector 𝑥 that correspond to the controller position in terms of its We examined the accuracy of the proposed algorithm. In this pilot
distance from the sensor. This information is provided during the test, we set the sensor parameters as followings: 𝑁𝑐ℎ𝑖𝑟𝑝 = 4 chirps
user calibration process. By extracting the values in these bins of per frame, 𝑁𝑠𝑎𝑚𝑝𝑙𝑒 = 256 samples per chirp, 𝑁 𝑓 𝑓 𝑡 = 256, and
𝑥, we can focus on the data relevant to the controller’s state. Thus, 𝐵 = 200 MHz bandwidth. We averaged the measurements across
we denote the values in these bins as a sub-vector 𝑦 and use it for the two Rxs for calculating 𝑥 and 𝑦 in this pilot test. We conducted
the subsequent processing. the test in an open indoor space of approximately 10 𝑚 2 .
For detecting discrete states of the controllers, users first collect
data for on and off states of the controllers in the calibration, as we 8.1 Discrete Detection
described in Section 6. Then, we calculated the mean values of data
We first tested the accuracy of detecting discrete states of the con-
collected from these two states as the thresholds to classify new
trollers – button and toggle switch.
frames of data after calibration.
Similarly, for detecting continuous states of the controllers, users 8.1.1 Setting. We examined the sensing accuracy in a variety of set-
provide sample data in the calibration for some of the data points in tings in terms of the controllers’ distance from the sensor, azimuth
the detection range. We trained regression models (i.e., linear) with angle, and angle of incidence. Figure 4 (left) shows the locations
the data collected during the user calibration process. Note that we we tested and Figure 4 (right) illustrates the angles of incidence
found the RCS change as the controllers morph to be monotonous we tested. For each controller, we first placed it in the mm-wave
Low-Cost Millimeter-Wave Interactive Sensing through Origami Reflectors CHIIoT 1, February 17, 2021, Delft, The Netherlands
RF beam direction with 1 m interval up to 5 m. Then, we fixed the
distance to 3 m and changed the azimuth angle with 15 degrees
interval up to 45 degrees. Lastly, we fixed the distance to 3 m and
the azimuth angle to 0 degrees, and changed the angle of incidence
with 15 degrees interval up to 45 degrees.
For each placement pattern, we first placed our controller and
conducted calibration, as described in Section 7. After the cali-
bration, we changed the state of the controller to be on and off
repeatedly, five times each, while we recording the output of the
algorithm each time.
8.1.2 Result. Figure 5 shows the accuracy, each corresponding
to when we changed distance (top), azimuth angle (center), and
angle of incidence (bottom). The toggle switch showed a stable
detection accuracy over the conditions. On the other hand, the
accuracy for detecting the button’s state gradually decreased as
it was placed far from the sensor or its perpendicularity lost (i.e.,
as we changed the azimuth angle or angle of incidence). Overall,
the high detection accuracy confirmed the validity of using our
origami-based reflectors as discrete controllers.
8.2 Continuous Detection
Next, we tested the performance of detecting continuous states of
the controllers – knob and slider.
8.2.1 Setting. We first recorded signals without a controller placed
in the environment and obtained 𝑦𝑏𝑎𝑠𝑒 . Then, we placed a controller
1 m away from the sensor board perpendicularly so that both the
azimuth angle and angle of incidence were 0°. We then changed the
expansion level of the controller from 0% to 100% with 25% interval,
while we recording the corresponding signals 𝑦. We calculated
and plotted the ratio of the amplitude of 𝑦 to 𝑦𝑏𝑎𝑠𝑒 in dB. We also
trained a linear regression model and calculated the mean absolute
percentage error (MAPE).
8.2.2 Result. Figure 6 shows the measured ratio of the amplitude
of 𝑦 to 𝑦𝑏𝑎𝑠𝑒 in each of the controller’s expansion level. As expected,
the values gradually increased as the controllers were expanded.
The MAPE for each of the controllers are 33.2% (knob) and 28.6%
(slider), respectively. The results clearly showed the correlation
between the expansion level and RCS, with which our regression
models can be easily trained for using origami-based reflectors as Figure 5: Accuracy of button and toggle switch when we
continuous controllers. However, our proof-of-concept regressor changed their distance (top), azimuth angle (center), and an-
implementation did not yield high accuracies due to large deviations gle of incidence (bottom) from the mm-wave sensor board.
when controllers were expanded to certain levels (e.g., 75%). We
suspect this issue was caused by fabrication defects, which we plan
to further investigate and make improvements in our future work. DIY smart environment experience across a wide spectrum of ap-
plications.
9 EXAMPLE APPLICATIONS
As we discussed in Section 1, our low-cost controllers can be a 10 DISCUSSION AND FUTURE WORK
promising approach for ubiquitous interactivity. For example, av- There are some directions to further refine our approach. First,
erage homeowners can easily deploy the controllers into their en- the accuracy and robustness can be improved by adopting better
vironments and connect them with various IoT applications, such fabrication process. For example, adding linings on top of basic
as light, music player, TV, air conditioner, etc. Moreover, the con- origami structures could yield more programmable shape changing
trollers can be installed in public places such as museums, hospitals, of the continuous controllers. We could also add protective coatings
restaurants, and buses, replacing exiting controllers that are mostly to mitigate degradation of origami structures over time.
wired and powered. Overall, we believe the advantages of our ap- Secondly, we will expand the origami design set. In this pa-
proach being ultra-low-cost, wireless, and battery-free facilitate per, we demonstrated four designs, but considering the abundant
CHIIoT 1, February 17, 2021, Delft, The Netherlands Riku Arakawa and Yang Zhang
the same time, we would like to further investigate the interac-
tion space where users can easily assemble controllers from basic
material primitives, as we discussed in Section 2 and Section 6.
11 CONCLUSION
In this paper, we proposed a novel approach to achieving ubiq-
uitous interactivity: ultra-low-cost (less than 40 cents), wireless,
battery-free controllers made of origami in concert with mm-wave
sensing. We demonstrated four controller designs (i.e., button, tog-
gle switch, knob, and slider). These controllers change their RCS
significantly upon user interaction, which can be detected remotely
by mm-wave sensing (e.g., FMCW). Our pilot test demonstrated
the feasibility of the proposed approach. We believe that our work
demonstrates a novel technique for ubiquitous interactivity, and
will greatly facilitate users’ DIY of future smart environments.
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