Journal of Information Processing Vol.29 1–12 (Jan. 2021)[DOI: 10.2197/ipsjjip.29.1]

Regular Paper

Context Recognition by Wireless Sensing:
A Comprehensive Survey
Akira Uchiyama1,2,a)Shunsuke Saruwatari1,b) Takuya Maekawa1,c)Teruo Higashino1,e)Kazuya Ohara3,d)Received: xx xx, xxxx, Accepted: xx xx, xxxx

Abstract: Context recognition is a topic that has garnered considerable interest in the ubiquitous and pervasive

computing research community. A wide variety of Internet-of-things devices with micro-electromechanical system(MEMS) sensors are used to obtain sensor data (e.g., acceleration, vibration, and sound) related to target contexts.
However, devices for context recognition also have limitations such as deployment cost, battery maintenance cost, andthe requirement for wearing/carrying the devices. To solve this problem, wireless sensing has attracted the attentionof many researchers because it enables device-free and/or maintenance-free context recognition. In this study, we willcomprehensively review studies on context recognition by wireless sensing, focusing on WiFi channel state information (CSI), radio-frequency identification (RFID), and backscatter. We will also discuss the design choices of wirelesssensing with their pros and cons through a review of the state-of-the-art.
Keywords: Context Recognition, Channel State Information, Wireless Sensing, Backscatter

1.

Introduction

Wireless technologies have become indispensable to our daily
lives. We are surrounded by various radio frequency (RF) signals
such as TV, FM/AM radio, cellular signals, WiFi, and Bluetooth.
They are used for data transmission. However, recent research
efforts have discovered a new aspect of wireless technologies—
wireless sensing. Wireless sensing enables us to recognize various contexts by leveraging RF signals change due to contexts
such as human motions and object movements. Typical wireless sensing methods involve transmitters (Tx) for lighting up targets and receivers (Rx) for capturing RF change due to context.
WiFi base stations, laptops, smartphones, and RFID (radio frequency identification) readers are often used as Tx and Rx. Thismeans that Tx and Rx in wireless sensing have plenty of energy
resources, such as large batteries and power outlets, comparedwith small Internet-of-things (IoT) devices such as wearables. In
this sense, wireless sensing is one of the key enablers for context
sensing without the maintenance of batteries.
This paper comprehensively reviews studies on context recognition by wireless sensing, focusing on WiFi CSI (channel state
information), RFID, and backscatter. Figure 1 illustrates theoverview of wireless sensing by WiFi CSI, RFID, and backscat1

2
3

a)
b)
c)
d)
e)

Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
JST, PRESTONTT Communication Science Laboratories, 2-4, Hikaridai, Seika-cho,”Keihanna Science City” Kyoto 619-0237, Japanuchiyama@ist.osaka-u.ac.jp
saru@ist.osaka-u.ac.jp
maekawa@ist.osaka-u.ac.jp
kazuya.ohara.hm@hco.ntt.co.jp
higashino@ist.osaka-u.ac.jp© 2021 Information Processing Society of Japanter. The original purpose of WiFi CSI is multiple-input andmultiple-output (MIMO) communication to estimate the states ofsignal propagation paths. Intuitively, WiFi CSI represents physical movements due to contexts because physical movements incur
state changes in signal propagation paths. RFID has also attracted
the attention of many researchers because RFID readers can directly recognize the precise movement of RFID tags attached totargets. The other key feature of RFID tags is battery-free operation, as it is powered by the RF signal from an RFID reader. By
observing RF signals reflected from RFID tags, the RFID reader
can recognize the movement of RFID tags. Lastly, backscatter isa technique for ultra-low power wireless communication wherein
communication is enabled by reflecting the carrier waves emitted from external RF signal sources. Its basic principle is widelyused for low-power communication of RFID systems. In Ref. [1],a novel concept of ambient backscatter was first proposed, whichleverages ambient RF signals such as TV as an external RF signal source for backscatter communication. The original purposeof backscatter is data transmission, similar to other wireless technologies. However, recent studies have revealed the feasibilityof wireless sensing by directly converting physical motions andphenomena (contexts) into changes in RF signals.
Many survey papers have already been published on wirelesssensing by WiFi CSI [2–5]. In this study, we leave a detailed
review of wireless sensing by WiFi CSI to these survey papers.
Instead, we focus on reviewing the differences between WiFi
CSI, RFID, and backscatter along with the state-of-the-art in eachstream.
The main challenge to exploit WiFi CSI is noise mitigation.
WiFi CSI suffers from noise due to clock offsets between Tx andRx (carrier frequency offsets (CFO), sampling frequency offsets

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Journal of Information Processing Vol.29 1–12 (Jan. 2021)

RFID reader

WiFi AP,
Smartphone,e,
Laptop

RFID tag(a) WiFi CSI(b) RFID

WiFi AP,
Smartphone,
Laptopcustom tag(c) Backscatter

Fig. 1 Wireless Sensing Overview(SFO), and packet detection delay (PDD)). Many studies have
employed sophisticated methods to mitigate such noises. Somemethods apply additional processing to alleviate multipath effects. After CSI preprocessing, context recognition is performedby signal propagation models or machine learning-based models
such as deep learning.
By contrast, RFID systems do not have clock offsets because
the RFID reader serves as both Tx and Rx. Instead, the effects
of multipath antenna orientation, and hardware imperfection are
the major sources of noise. Research efforts have been made to
deal with such effects. Because RFID phase information is morereliable than the CSI phase, many approaches are based on basic physics, which include the relation between wavelength, distance, and phase. In this sense, wireless sensing by RFID is moreaccurate and precise than WiFi-based wireless sensing. Anotheradvantage of RFID tags is their inherent ability to identify targets(i.e., attached parts, subjects, or objects) by responding to theirpresence with their identification.
Wireless sensing by backscatter is an emerging new classicaltechnique. Unlike other wireless sensing approaches, wirelesssensing by backscatter directly converts contexts (e.g., temperature, pressure, moisture, and audio) into changes in the backscattered wireless signal. Backscatter communication is an ultra-low
power and promising building block for future IoTs. Nevertheless, it still requires digital modulation, which necessitates processing by computation modules such as microcontroller units.
This leads to additional costs for additional components as wellas additional energy consumption, which can limit the application
design space. Wireless sensing by backscatter leverages analog
sensors or physical movements due to contexts (e.g., wind, water flow, and acoustic vibration). Its design is simple, yet worth
exploring with the application space, opening up new vistas ofwireless sensing.
The remainder of this paper is as follows. Section 2 describes
the basic principle of WiFi CSI, followed by a review of the stateof-the-art. Section 3 presents a review of RFID sensing with an
overview of RFID systems. Section 4 describes a review of wireless sensing by backscatter. In Section 5, we describe the design
choices of wireless sensing through a discussion on the pros andcons of WiFi CSI, RFID, and backscatter. Finally, Section 6 concludes the paper.

2.

Channel State Information

2.1 Basic Principle of WiFi CSI
In wireless LAN standards IEEE 802.11n and later, MIMO isadopted to improve the quality of communication. In addition,orthogonal frequency-division multiplexing (OFDM) is used asa modulation scheme that uses multiple orthogonal subcarriers.

© 2021 Information Processing Society of JapanMIMO leverages CSI for transmission signal control to improve
the quality of the signal at the receiver side. CSI provides the amplitude and phase differences for each subcarrier in this OFDM
modulation. Let X( f, t) and Y ( f, t) be the frequency domain
representations of the transmitted and received signals, respectively, with carrier frequency f at time t. The relationship between X( f, t) and Y ( f, t) are written as
Y ( f, t) = H( f, t) · X( f, t),(1)where H( f, t) is the complex-valued channel frequency response(CFR).
Assuming that the number of subcarriers is S for NT x antennasof the transmitter and NRx antennas of the receiver, we can obtain NT x · NRx · S pairs of CFR values, which is called CSI. Using
commercial WiFi devices such as Intel 5300 NIC with a modified driver, we can obtain CSI samples. Compared to RSSI, CSI
contains richer information on the conditions of the radio propagation paths. Because the channel states change due to dynamic
components such as human movement, CSI has been used to improve communication quality and context recognition.
As the signal travels from a transmitter to a receiver throughmultiple paths including the direct path, reflection from walls,
and human bodies, CSI is the superposition of components fromall the paths as follows.
X
H ( f, t) = e− j2π f t
a p ( f, t) e− j2π f d p (t)/c ,(2)∀pwhere a p ( f, t) and d p (t) are the amplitude attenuation factor andthe length of the p-th path at time t, respectively. c is the speed oflight.
To extract signals due to object/human movements, many approaches model the CSI as a composition of static and dynamic
components as follows.
H( f, t) = Hd ( f, t) + H s ( f, t),(3)where Hd ( f, t) and Hd ( f, t) are the CSI of the dynamic and static
components, respectively.
2.2 CSI Noise Factors
CSI is estimated by sending pilot signals from a transmitter toa receiver. In practice, the estimated CSI is distorted by various
noise factors owing to hardware imperfection. The following factors have been mentioned in many existing works [6–10].
• CFO: CFO is introduced because the oscillators of the transmitter and the receiver are not exactly synchronized. Thismeans that there is an offset between the central frequenciesof the transmitter and receiver.

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Preprocessing

Sanitization, CSI Ratio,(PCA, ICA, AoA)

Feature extraction

PCA, ICA, AoA, DFS,
CNN, TL

Recognition

Fig. 2

to estimate the phase delay yk of the k-th subcarrier due to SFO
and PDD as follows:
a =

S
X
S
S
)(θk − θ̄)/ (k − )2 ,
2
2
k=1

S
,
2
yk = a ∗ k + b,b = θ̄ − a ∗Typical CSI Processing Flow

• SFO: Because pilot signals are processed by the receiver one
by one for each subcarrier, delays are accumulated linearlywith the processing order of the subcarriers.
• PDD: PDD stems from the symbol synchronization modulein the receiver after detecting the frame start.
• Quantization Error: Analog-to-digital (A/D) conversion in
the receiver introduces quantization error, which amplifies
the noise originally included in the CSI. The quantization
error becomes larger as the signal amplitude decreases.
Many existing works have introduced sophisticated methods toovercome the aforementioned noise factors. Zhuo et al. [11] studied the CSI noise factors with other additional sources and proposed a calibration method to compensate for nonlinear and linear CSI phase errors. In the following sections, we review theprimary preprocessing and feature extraction methods. Figure 2
shows the typical flow of WiFi CSI-based wireless sensing. For adetailed review, interested readers may refer to surveys on WiFi
CSI sensing [2–5].
2.3 Preprocessing for CSI Noise Mitigation
Before extracting features, smoothing can be applied to reduce the random noise of CSI. For example, Refs. [12–14] employ Svitzky–Golay(SG)–filter for denoising. We note that somemethods [12, 15–19] perform denoising and feature extraction simultaneously by principal component analysis (PCA) and independent component analysis (ICA). Similarly, others [20–22] use
the MUSIC algorithm to extract signals of interests without denoising directly. Therefore, the design of preprocessing and feature extraction should be carefully considered depending on thetarget context.
2.3.1 Sanitization
A common noise mitigation method is phase sanitization [10,23–25], that alleviates SFO and PDD. Because SFO and PDD
are linear with sub-carrier indexes, the offset can be estimated bycomputing its gradient over all subcarriers, as shown in Eq. (4).
If the phase difference between adjacent subcarriers due to
SFO and PDD is δ, the CFR of the kth subcarrier is represented
as follows:(4)

where fk is the frequency of the k-th subcarrier and a( fk , t) is thecomplex attenuation of the k-th subcarrier. As can be seen from
Eq. (4), the phase part of the CSI is a function of time. Owing
to the linearity of the SFO and PDD phase error on the subcarriers, we can mitigate the error by subtracting the phase difference
due to δ(k − 1) for the k-th subcarrier. For simplicity, we define
θ = fk (t + δ(k − 1)). Then, we can apply the least square method© 2021 Information Processing Society of Japan

(k −

k=1

Model-based (e.g., FFZ),
ML, DNN, HMM, Kalman filter

H( fk , t) = a( fk , t)e−2 jπ fk (t+δ(k−1)) ,

S
X

(5)
(6)
(7)

where θ̄ is the average of θ of all the subcarriers.
2.3.2 CSI Ratio
In a real environment, a random offset θo f f set due to CFO is
added, which is represented as
H( f, t) = e− jθo f f set H( f, t)(8)

To remove the random offset due to CFO, the ratio of CSI between a pair of antennas on the transceiver is used, which cancels
out the phase offsets [8,9]. This is because the antennas of a wireless device (i.e., the transmitter or receiver) are connected to thesame oscillator, which results in the same CFO. The CSI ratio
Hn,m between antennas m and n is defined as follows:
Hn,m ( f, t) =Hn ( f, t)
e− jθo f f set Hn ( f, t)=
e− jθo f f set Hm ( f, t) Hm ( f, t)(9)

We note that the CSI ratio can be defined between the antenna
pairs of the receiver and transmitter. Therefore, the CSI ratio is(NT x ∗NRx C2 ∗ S )-dimensional data with respect to the number oftransmitter antennas NT x , the number of receiver antennas NRx ,and the number of subcarriers S . Note that NT x ∗NRx C2 is the number of T x and R x pairs. The CSI ratio still retains the other features, canceling out CFO.
2.4 Feature Extraction
2.4.1 Principal Component Analysis
Ref. [26] used PCA to decompose the mixed RF signals into
signal changes due to noises and target movements. As studied
in Ref. [26], the third component of PCA has the highest humanmovement signal-to-noise-ratio (SNR). In this sense, PCA is amethod for feature extraction as well as noise filtering.
2.4.2 Independent Component Analysis
Similar to PCA, ICA can be used to separate mixed signals
from multiple reflection sources and direct paths. Ref. [27] leverages ICA to extract the effect of signals reflected by objects withstate change (e.g., opening a door).
2.4.3 Angle-of-Arrival (AoA) Estimation
WiFi-based AoA estimation has been actively studied because
AoA is the vital information that provides a real-world context.
AoA estimation leverages multiple antennas on the receiver toextract features of interests depending on the targets. MUSIC
algorithm is widely used for this purpose [20–22]. Because theMUSIC algorithm outputs a spatial spectrum function representing the signal amplitude for each arrival angle, it works as a decomposer in terms of signal paths.
Interestingly, FreeSense [21] leverages the MUSIC algorithm
to detect human movement, which is observed as the CSI phasedifferences at the receiver antennas. Because the MUSIC algorithm has strong anti-interference ability against random noise,3

Journal of Information Processing Vol.29 1–12 (Jan. 2021)

FreeSense does not employ any denoising scheme. ArrayTrack [28] is a CSI-based indoor positioning system that utilizes
the MUSIC algorithm to estimate AoA with multiple antennas.
SpotFi [29] is also a CSI-based indoor positioning system; it calculates both AoA and time-of-flight (ToF) at the same time with
the MUSIC algorithm. ROArray [30] is a CSI-based indoor positioning system that employs sparse recovery to retrieve AoA
information even under a low SNR.
2.4.4 Doppler Frequency Shift
Doppler frequency shift (DFS) is one of the key features forhuman context recognition because it can infer the moving speedof the target. After signal selection (e.g., MUSIC algorithm), the
DFS of the signal is extracted based on its phase change. This
may work well for sensing contexts such as breathing and gestures of relatively static (e.g., sitting and sleeping) targets. WiPolar [31] proposed a simultaneous estimation of the direction of thetarget (i.e., signal selection) and DFS to overcome the challenge
when the target is moving.
2.4.5 Deep Neural Network
Deep neural networks (DNNs) such as convolutional neuralnetworks (CNNs) are another trend for feature extraction, similar
to other research domains [13, 27, 32–34]. Using a large amountof training data, a DNN has the capability to learn models that are
difficult for humans to explain. However, a major concern is thatlearned models are often environment- and subject-dependent,
which creates new challenges for practical applications.
2.4.6 Transfer Learning
To reduce the cost of collecting training data, several studies
used transfer learning for CSI-based context recognition. For example, Rao et al. [35] employed transfer learning for CSI-basedindoor positioning to learn feature representations such as fingerprints by minimizing the distribution differences between afingerprint database and test samples. Bu et al. [36] converted
CSI data into image data and pre-trained an activity recognition
model using a public image dataset for object recognition (ImageNet). Arshad et al. [37] also employed pre-trained image-basedneural networks for multiple human activity recognition. Jiang etal. [38] employed domain-adversarial training for activity recognition, whereas Wang et al. [39] employed domain-adversarial
training for in-car activity recognition.
2.5 Tools to Obtain CSI
The use of special customized hardware such as USRP [40]and WARP [41] enables the extraction of more detailed physicalspace information than CSI. However, the use of commerciallyavailable equipment such as IEEE 802.11n is advantageous for
deployment and the reproducibility of research results. In particular, the emergence of CSI tools [42–45] has been particularly
significant for the wireless sensing research community. Commercially available IEEE 802.11n devices not only produce various research results, but they have also opened up possibilities
for the deployment of wireless sensing. However, at present, research using IEEE 802.11n faces the problem that only one section of IEEE 802.11n devices, Intel 5300 NIC, Atheros AR9390,
AR9580, AR9590, AR9344, or QCA9558, can obtain CSI. Oneof the promising options is the IEEE 802.11ac [46, 47] com-

© 2021 Information Processing Society of Japanpressed CSI. The IEEE 802.11ac compressed CSI is standardized
to reduce the overhead of CSI feedback. Compressed CSI can beacquired from any device that supports IEEE 802.11ac or IEEE
802.11ax. Furthermore, the ESP32 CSI Toolkit [48] is anotheroption to obtain CSI directly from the ESP32 microcontroller,
enabling CSI data collection from a large number of tiny IoT devices.
2.6 Applications
In this section, we briefly describe the recent literature on wireless sensing by WiFi CSI. Table 1 summarizes our review.
2.6.1 Activity Recognition
In Ref. [26], the authors proposed two models for quantitatively correlating CSI dynamics and human activities: a CSIspeed model that correlates CSI dynamics with the movement
speed and a CSI-activity model that correlates the movement
speed of different body parts with a specific activity. Gao et
al. [59] converted CSI measurements from multiple channels
into an image and then recognized human activities by extracting color and texture features from the image. Chen et al. [60]recognized human activities by feeding CSI measurements intoa neural network with a bidirectional long short-term memory(LSTM) layer. WiStep [57] counted steps based on the CSI energy of the frequency components. For this purpose, WiStep converts CSI to time-domain channel impulse response by inverse
fast Fourier transform (IFFT) to remove non-relevant multipath
signals. CARIN [50] recognized driver activities using average
Doppler shift power with a hidden Markov model-based classification.
2.6.2 Fall Detection
Device-free fall detection for elder care support is another typical application of WiFi CSI. For example, the WiFall system
proposed in Ref. [61] employed the time variability and spatial
diversity of CSI to detect falls in residential settings, whereas
Anti-Fall [62] employed the CSI phase difference over two antennas and used amplitude information to distinguish the fall activity
from fall-like activities. FallDeFi [56] extracted the spectrogramof CSI by short-time Fourier transform (STFT) combined withnoise filtering by PCA and discrete wavelet transform (DWT) for
accurate fall detection.
2.6.3 Vital Sensing
In Ref. [63], the authors attempted to capture user sleep information such as respiration based on WiFi CSI by extracting
rhythmic patterns associated with respiration. MultiSense [15]achieved the respiration monitoring of multiple persons using
ICA to separate mixed signals. It also employed time-varying
phase offset cancellation, background static signal removal, andsubcarrier selection. FarSense [9] used the CSI ratio for noise
cancellation and achieves the respiration monitoring of the target. For robust respiration monitoring, FullBreathe [14] proposed complementarity of CSI amplitude and phase, which are
extracted as the conjugate multiplication of CSI between two antennas. Zhang et al. [54] proposed respiration sensing by phasechange based on a first Fresnel zone (FFZ) diffraction model.
2.6.4 Localization and Tracking
WiPolar [31] proposed multi-person tracking by simulta-

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Table 1 Summary of Wireless Sensing by WiFi CSI
MethodWidar3.0 [32]Features
Velocity profiles of gestures

WiBorder [49]MultiSense [15]

DCM-CSI
ICA

Algorithm
Model-based feature extraction and DNN for recognition
Model-based
Matching algorithm

WiPolar [31]AoA, ToF, and DFS

pSAGE algorithm

CARIN [50]Average Doppler shift power

HMM-based classification

LiquidSense [16]PCA and resonance frequencies

FingerDraw [51]CSI quotient

SVM classification for discrete
liquid level. Curvilinear regression for continuous liquid
level.
CSI-quotient model

FarSense [9]CSI ratio

Model-based

Respiration

WiDetect [52]

ACF of the CSI power response
Acceleration and CSI PCA(SG-filter for denoising)
CSI amplitude denoised by
SG-filter
CSI autocorrelation

Hypothesis testing

Motion detection coveringwhole house/office floor
Indoor odometer (traversed
distance)
Repetitive activities in FFZ

WIO [12]
Zhang et al [13]Guo et al [33]

WiVit [20]CSI phase change and pathlength change speed

WiID [17]Spectrogram of CSI denoisedby PCAFreeSense [21]Phase difference

FullBreathe [14]Conjugate multiplication of
CSI
Time of Arrival (ToA)

SiFi [53]SignFi [34]Sanitized CSI amplitude andphase

Zhang et al [54]QGesture [55]Rapid [58]Phase change
Sanitized CSI phase; PCI for
subcarrier selection; PCA forphase information recovery
CSI spectrogram; Discrete
Wavelet Transform (DWT) for
denoising
CSI energy of frequency components
CSI and acoustic information

Ohara et al [18]ICA and DNN

WiMu [19]PCA-based denoising; Frequency feature by STFT
Relative Time of Flight (ToF);
AoA estimation by MUSIC

FallDeFi [56]

WiStep [57]

Strobe [22]Fusion by Kalman filter
CNN; FFZ Diffraction model

Boundary crossing
Respiration of multiple persons
Multi-person tracking
Driver activities under interference of passengers (e.g. continuous head nodding)Liquid level

Sub-wavelength level finger
motion tracking

DNN models for individual
identification and exercise
recognition.
Spectrogrambased
workout
detection
algorithm
Model-based speed estimation

Device-free individual identification and workout assessment (repetition tempo ratio
and work-to-rest ratio)

Machine-learning based modelfor gesture and user classification
Peak detection

User authentication by gesture

FFT

Respiration

Hankel matrix decompositionand clustering
CNN

Localization

FFZ diffraction model
Model-based

Respiration
Gesture distance and direction

SVM classifier

Fall detection

Model-based

Step countMachine learning with handcrafted features
Hiden Markov Model

Person identification

Database matching
Model-based

neously estimating AoA, ToF, and DFS using the extended
space-alternating generalized expectation-maximization algorithm called pSAGE. WIO [12] estimated indoor odometry byfusion of acceleration and CSI. SiFi [53] estimated the ToA from
CSI by using Hankel matrix decomposition.

© 2021 Information Processing Society of Japan

Context
GestureTraining-free vitality sensing

Indoor human detection

276 gestures of sign languageStatechangesofindoorobjects(open/close
door/window/shade, etc.)
Multi-user gesture recognition
Soil moisture and electricalconductivity (EC)

Performance
82.6%-92.4%
forcrossdomain recognition
99.4% detection rate
Error rate of 0.73 bpm (breaths
per minute)Median tracking error of 56cm
(up to 5 people)
F1 score of 90.9%

97% accuracy

Median tracking accuracyof 1.27cm, 93% accuracy inrecognition of drawing ten
digitsMean absolute error of
0.34bpm in through-wall
respiration sensing
99.5% detection rate with
0.1% false alarm
6.87% relative odometer error
95+% precision and recall forpush-up, sit-up, and walkout
93% accuracy on workout
recognition and 97% accuracy
for individual detection (20subjects, 10 exercises)
98+% precision of activity detection and almost 100% ofarea detection accuracy
92.8% accuracy for 5 users

False positive rate of 0.53%,
false negative rate of 1.4%
100% detection rate if a subject
faces the transceivers
Median accuracy of 0.93m
94+% accuracy for 276 gestures by a single user; 86.66%
for 150 sign gestures by 5 users
98+% accuracy
3.7 cm moving distance error,
15 degrees moving direction
error
93% accuracy for pre-trained
environment, 80% accuracy
for different environment
87.59%-90.2% counting accuracies
92% to 82 % accuracy from agroup of 2 to 6 subjects
85% accuracy

90+% accuracy for 2-6 simultaneous gestures
Comparable accuracy with expensive soil sensors

2.6.5 Human Detection
Lv et al. [64] detected an intruder using WiFi CSI by extracting a robust feature with continuous wavelet transform. WiBorder [49] detected boundary crossing based on DCM-CSI: CSI
conjugate multiplication between two antennas. WiDetect [52]5

Journal of Information Processing Vol.29 1–12 (Jan. 2021)

succeeded in motion detection covering a whole house and office floor using the autocorrelation function of the power responseof CSI. WiVit [20] is a method for training-free vitality sensing(i.e., whether a target is still or not, moving speed, and its area).
It employed CSI phase change of dynamic path signals for activity detection and path-length change speed for area detection.
FreeSense [21] leveraged the MUSIC algorithm to estimate thephase difference due to human movement for indoor human detection. It performs peak detection for a spatial spectrum function
output using MUSIC.
2.6.6 Human Identification/Authentication
WiID [17] proposed user authentication by gestures using CSI
based on machine learning with a CSI spectrogram (i.e., frequency spectrum over time) obtained by STFT. Rapid [58] combines CSI and acoustic signals to achieve accurate person identification. IFFT is employed to remove the multipath effect.
2.6.7 Gesture Recognition
Widar3.0 [32] recognized gestures by extracting bodycoordinate velocity profiles based on estimated body orientationto achieve cross-domain recognition. FingerDraw [51] achieved
sub-wavelength-level finger motion tracking without attaching
any sensor to the finger. It leveraged the CSI quotient between
two antennas of a receiver to cancel out the noise and offsets. The
CSI-quotient model was used to describe the connection between
the motion displacement and CSI variations. SignFi [34] recognized 276 gestures of a sign language using sanitized CSI input
to the CNN. For recognition of gesture distance and direction,
QGesture [55] employed principal component identification for
subcarrier selection and PCA for phase information recovery.
WiMu [19] succeeded in multi-user gesture recognition using
frequency features extracted by STFT.
2.6.8 Fitness Monitoring
Zhang et al. [13] recognized repetitive activities such as
pushups by focusing on the model of the FFZ. The FFZ model
was used to guide system deployment. Guo et al. [33] achieved
individual identification and workout assessment using CSI autocorrelation and DNN.
2.6.9 Object Event Detection
Ohara et al. [18] employed WiFi CSI to recognize events of everyday objects, including door open/close events. A deep learning model was used to automatically extract efficient classification features. Xu et al. [65] employed WiFi CSI to recognize
door events based on features extracted from CFR and a classifier
using dynamic time warping.
2.6.10 Material/Moisture Sensing
LiquidSense [16] estimated the liquid level in a container using a transducer attached to the surface of a cup. It captured theliquid-level dependent vibration generated by a transducer on thesurface of a cup using CSI. WiFi CSI is also capable of soil moisture sensing as presented in Strobe [22], which exploits the relative ToF. The multipath signals were removed using the MUSIC algorithm. Because Strobe used multiple Rx antennas, PDD,
SFO, and CFO were canceled out.

d

RFID tag
RFID reader
Fig. 3

3.

RFID Sensing

3.1 Overview of Wireless Sensing by RFID
The RFID system is composed of a reader and battery-lesstags*1 . The RFID reader emits a continuous wave (CW) signal
to provide passive tags with energy. The tags send back data suchas their identification by backscatter communication. Backscatter
communication is ultra-low power because it leverages CW fromthe reader without generating a high-frequency active RF signal,
which requires a large amount of energy in many devices.
Commercial RFID systems such as the ImpinJ Speedway RFID
reader provide information on the received signal strength (RSS)
and phase of the received signal from tags. Although the APIs of
the commodity RFID systems provide RSS information, they areusually unstable and unreliable. For this reason, many approaches
rely mainly on phase measurement. However, some methods also
leverage RSS.
Based on basic physics, the following equation holds between
the observed phase θ and distance d between a reader antenna anda tag (see Fig. 3).
!
2d
+ θn
mod 2π,(10)θ = 2πλwhere θn is the noise. By observing the phase change over time,
we can infer contexts related to the movements of objects.
3.2 Noise Sources and Countermeasures
The primary sources of RFID system noise are tag hardware
imperfection, tag antenna orientation, and multipath effect. Furthermore, some countries’ regulations, including the U.S., require
frequency hopping, which affects the phase-angle measurements.
Tag hardware imperfection is mitigated by calibration, for example, measuring the distance between an antenna and a tag. We
note that some methods assume a constant noise for the hardware
imperfection without calibration, which can be mitigated by sampling over time and multiple measurements of tags and receiverantennas.
The phase difference between multiple tags is introduced in
RF-Kinect [66] to overcome the effect of the antenna orientation.
Similarly, the phase difference between multiple reader antennas(i.e., an antenna array) can be used.
Because the multipath effect is more like random noise, Yang
and Cao [67] employed a matched filter to separate a known signal template (e.g., repetitive pattern of respiration) from the multipath signals. Furthermore, similar to CSI, TagFree [68] performs AoA estimation by the MUSIC algorithm to identify thesignal reflected from the human body. Such signal path selection*1© 2021 Information Processing Society of Japan

Basic Principle of RFID Sensing

We focus on passive RFID tags owing to its unique nature of battery-free
operation, although other types of active RFID tags are also available.

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Journal of Information Processing Vol.29 1–12 (Jan. 2021)

methods are useful to focus on the signals of interests.
3.3 Applications
Wireless sensing by RFID systems is roughly classified into
tagged approaches and device-free approaches. In the tagged approaches, RFID tags are attached to the subjects/objects of interest to directly sense the movement of the tagged parts. This is
the major difference between RFID and WiFi CSI: RFID tags areattached to the targets, and the reader can distinguish the source
tag of the received signal from other tags. Some studies have also
proposed device-free approaches where tags are deployed in theproximity of the targets (e.g., doors and beds). The nature of theidentification capability of RFID systems provides a clear separation of the signal sources (i.e., tags), which enables us to capture
more precise movement than WiFi CSI-based wireless sensing.
Table 2 summarizes our review on the recent RFID-based wireless sensing. We note that a fusion of RFID with other sensors is
another option for enhancing its capability. One of such methods is RF-Focus [69], which combined RFID and a camera to
precisely estimate tag locations in the region of interest.
3.3.1 Tagged Approach
RF-Kinect [66] recognized three-dimensional (3D) body
movements by attaching multiple tags to the subject’s body. It
employed a phase difference between tags (PDT) to track the
body movement, which is robust to antenna orientation change.
RF-Wear [84] recognized body pose with tag arrays on each joint
by observing the phase difference between the signals from thetags.
To achieve real-time gesture recognition, EUIGR [70] proposed an LSTM-based sequence labeling classifier that predicts
gestures before its completion using two tags attached to eacharm. RFID Tattoo [75] proposed the design of stretchable customized tags; the tags were attached to the upper and lower jaws
and two sides of the mouth for speech recognition. The tag antenna impedance changes due to the stretch of tags related to the
movement of the mouth.
ShopMiner [73] tracked customer behavior such as turning
the item over and picking the item up by tags attached to each
item. AdaRF [79] used a CNN with transfer learning for localization of tagged targets, achieving cm-level positioning. Li
et al. [72] employed a CNN to recognize 11 medical and 6
lab activities (e.g., oxygen preparation, blood pressure measurement, and lab-meeting.) by tags attached to objects. RFCopybook [83] achieved millimeter-level antenna-tag distanceestimation for Chinese calligraphy monitoring by two tags on abrush. RF-Copybook used a Kalman filter to filter random noise.
In addition, the phase shift due to tag imperfection was calibrated
by measuring the distance between the antenna and tag.
Yang and Cao [67] proposed respiration monitoring with a
tag attached to the chest by finding continuous breathing patterns from a signal with a multipath effect by matched filtering.
ER-Rhythm [76] estimated the locomotor–respiratory coupling(LRC) ratio, which is the correlation between exercise locomotion and respiration rhythm. RF-ECG [82] estimated heart ratevariability with a tag array attached to the chest by separating
chest movement due to respiration and heartbeat. FEMO [74] rec-© 2021 Information Processing Society of Japanognized ten free-weight activities by two tags attached to dumbbells.
Interestingly, Tagtag [71] is a method for material sensing using two tags on a container, leveraging material-dependent phasechange (i.e., antenna impedance changes). In addition, soil moisture sensing is possible by attaching two tags on each pot basedon the signal change due to soil moisture [85].
3.3.2 Device-Free Approach
TagFree [68] attached multiple tags on furniture to recognize seven human activities using deep learning. It used AoAchange over time (AoA spectrum), representing the change ofbackscattered signal paths related to target activities. LungTrack [77] employed Fresnel diffraction and reflection models
for respiration monitoring with five tags deployed near the subject. TagSleep [78] is a device-free approach for the recognition of respiration and snoring, cough, and somniloquy. It also
employed a wavelet filter to remove high-frequency noise. AuId [80] achieved user identification and authentication with CNN
and LSTM using a 3×3 tag array on the door. TACT [81] recognized eight activities with four tags near the subject by extracting
various features such as moving speed, distance, activity duration,and phase waveform.

4.

Backscatter Sensing

Backscatter sensing is a novel yet classical concept of batteryfree or ultra-low-power sensing by direct conversion of contexts
into backscattered signal changes. The basic concept is similar tosome classic devices such as the Great Seal Bug [86] and a laser
microphone [87]—backscattering signals from an external source
in a passive manner.
Printed WiFi [88] is one of the emerging concepts of recent
backscatter sensing. It directly converts contexts such as windspeed, liquid flow, the moving distance of a slider bar, and theamount of knob rotation into the variation of backscattered WiFi
signals without any digital modulation. LiveTag [89] is a printed
tag composed of two antennas and resonators. The resonator absorbs the WiFi signal of a specific frequency, which is used forthe identification of the resonators. Based on this principle, LiveTag [89] leveraged the cancellation of the resonator effect by finger touch and liquid to enable battery-less touchpads and liquidlevel sensing.
The aforementioned two approaches are entirely battery-free,
without any silicon chips. On the contrary, a tiny amount ofenergy (e.g., harvested from ambient light) broadens the capability of backscatter sensing. BARNET [90] proposed backscatter channel state information between backscatter tags to obtain
activity-related signal change information similar to WiFi CSI.
Because backscatter tags can be deployed anywhere without thelimitation of batteries, the number of backscatter tag-to-tag links
is expected to be much higher than that of WiFi CSI. Therefore,
we can expect wide area coverage and more robust context recognition. RF Bandaid [91] proposed an RF sensing platform thatconsists of an energy harvester, an antenna, an oscillator, an RF
switch, and a resistive or capacitive sensor. The resistive or capacitive sensor changes its resistance or capacitance according
to its sensing target. For example, the capabilities of tempera-

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Journal of Information Processing Vol.29 1–12 (Jan. 2021)

Table 2 Summary of Wireless Sensing by RFID
MethodRF-Kinect [66]FeaturesPhase Difference
BetweenTags(PDT)
AoA change overtime (AoA spectrum)

Algorithm
Model-based

Reader
1 reader with 2antennas

Tag
Multiple Tags on
bodyContext
3D body movement

Deeplearning(CNN+LSTM)

1 reader with 4antennas

6 tags on furniture

EUIGR [70]Phase and RSS

Deeplearning(CNN+LSTM)

1 reader with 1
antenna

2 tags attachedon each arm

7
activities:
stand, sit, wave,
bow, walk, run,work
8 traffic command gestures

Yang and Cao [67]Phase

Matched filtering

1 reader with 1
antenna

1 tag on chest

Respiration monitoring

Tagtag [71]

Dynamic
Warping

1 reader with 1
antenna

2 tags on container

Material Sensing

Li et al [72]Materialdependent
phase change
RSS

CNN

2 readers, 8 antennas

12 tags on 12 objects

ShopMiner [73]Phase

Model-based

FEMO [74]Phase (Doppler
shifts)

One tag on eachitem
2 tags on dumbbells

RFID Tattoo [75]1 reader with 1
antenna on user’s waist4
stretchable
customized tags
around mouth

Speech recognition

86% accuracy in reconstructing the top-100 words
in English

ER-Rhythm [76]

RSS and phase(impedancechange due tostretch of tags)
Phase

Fingerprint
matchingby
Dynamic Time
Warping
Classification bymachine learning(Random Forest)

1 reader with 4antennas
1 reader with1
directional
antenna

11 Medical and6 lab activity
recognition
Turn item over,pick item up
10 free-weightactivities

Model-based

1 reader with 1 or
2 antennas

LRC ratio

LungTrack [77]

RSS and phase

Fresnel diffraction and reflection models

1 reader with 1
antennatags on the limbs
and front andback chest
5 tags near the
subject

TagSleep [78]Phase;
timedomain,frequencydomain
andsample entropy
featuresPhase(simulation
andexperiment)
Phase and RSSIClassification bymachine learning

1 reader with 1
antenna

3 tags near the
subject

Respiration andsnore,
cough,and somniloquy

Accurate estimation up to
92%-95% of the exercise
duration
98% accuracy for a single
target, 93% accuracy fortwo subjects separated byat least 10cm
96.58+% accuracy in recognizing snore, cough, andsomniloquy

CNN with transfer learning

1 reader with 1
moving antenna

1 tag on each object

Localization

cm-level positioning

CNN and LSTM

1 reader with 1
antenna

3x3 tag array on
the door

User
identification
andauthentication

RF-Focus [69]Phase, RSSI, andimages1 reader with 2antennas

1 tag on each object

TACT [81]Moving speed,
moving distance,
activity duration
estimatedbyphase, and phase
waveform
Chest movement
estimationbyphase change
Phase; random
noise filtering by
Kalman filter
Phase

Model-based distance estimationand matching
Machinelearning basedclassifier

1 reader with 1
antenna

4
tags
near
the
subject(Reader-tag
distance=2m4m)

Tag locations inRegion of Interest (ROI)
8 human activities (stand, sit,raisehand, drophand, walk, fall,rotation, get-up)

94.2% identification accuracy (15 users), 96.11%
authentication
accuracy(8 legitimate users and 7
spoofers)
True positive rates of
91.6% and false positive
rates of 10%
93.5% precision

Model-based;
DWT-based
denoising
Model-based distance estimation

1 reader with 1
antenna in frontof the subject
1 reader with 3
antennas

tag array on thechestHeart rate variability

Median error of 3% ofInter-Beat Interval (IBI)

2 tags on a brush

Chinese calligraphy monitoring

Model-based

1 reader with
1 antenna in a
pocket1 reader with 1
antenna over pots

tag arrays (matrixes) on eachjoint
2 tags on eachpot

Body pose

4.8mm-7.5mm distance estimation errors depending
on multipath environment
Mean error of 8-21 degrees
in tracking angles at joints

TagFree [68]AdaRF [79]Au-Id [80]RF-ECG [82]RF-Copybook [83]RF-Wear [84]Wang et al [85]Differential Minimum Response
Threshold(DMRT)© 2021 Information Processing Society of JapanTime

Model-based

Respiration monitoring

Soil moisture

Performance
8.7 degree limb angle error
and 4.4cm relative joint position error
91%-97% average accuracy, depends on activity
speed and multipath environment
96% precision and recallwith unseen users; 88.6%
precision and 86.7% recall
in untrained positions
0.5 bpm error for respiration rate; 5.3% error for apnea detection
90+% accuracy even forsimilar materials like Pepsi
and Coke
80.4% accuracy for medical activities, 90.8% for lab
activities
87+% accuracy
90% precision and 91% recall

90-percentile moisture estimation errors of 5%

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Journal of Information Processing Vol.29 1–12 (Jan. 2021)

ture, force, and stress measurements have been demonstrated inRef. [91]. RF Bandaid employed a micropower precision programmable oscillator from Linear Technology LTC6906. Thisoscillator converts the resistance or capacitance of the sensor toa specific frequency. The RF switch changes its state according to the oscillator frequency, resulting in a frequency shift inthe backscattered signal. The concept of a touchpad using FM
backscatter was presented in UbiquiTouch [92], which modulates
a touch point on a surface to its corresponding time-series pattern of the frequency shift. OFDMA backscatter localization withultra-low power was also proposed in Ref. [93] using an extended
MUSIC algorithm.

5.

Design Choice of Wireless Sensing

As we reviewed, wireless sensing by WiFi CSI is the mainstream of the research because of its wide availability. RFID sensing has also been attracting the attention of many researchers owing to its nature of identification and ubiquitous tags. Backscatter
sensing is similar to RFID sensing; however, customized tags areused to more directly recognize contexts.
The design choice of wireless sensing depends on various requirements such as deployment cost, target context, and required
performance. WiFi sensing has a great advantage in deployment
cost, whereas the target environment and performance may belimited. By contrast, RFID sensing can typically achieve higher
accuracy than WiFi CSI because it provides signals from manytags that are even attachable to the targets. Backscatter sensing
further enhances the capability of wireless sensing by directly
converting context into ambient RF signal change such as WiFi
and BLE; however it requires careful design of customized tags.

6.

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Conclusion

In this study, we comprehensively reviewed wireless sensing
by WiFi CSI, RFID, and backscatter. Wireless sensing has a wide
variety of applications owing to its pervasive and ubiquitous nature. We also provided the design choice of wireless sensing, depending on the requirements. We hope that this review will help
researchers open up new research directions for wireless sensing.

[18]

[19]

[20]

Acknowledgement
This work was partly supported by JST, PRESTO Grant
Number JPMJPR1932, and JSPS KAKENHI 19K11941,
JP20K20398, and JP19H05665, Japan.

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Akira Uchiyama received his M.E. and
Ph.D. degrees in Information and Computer Science from the Osaka University
in 2005 and 2008, respectively. He is an
Assistant Professor at Graduate School ofInformation Science and Technology, Osaka University. He was a visiting scholar
in the University of Illinois at UrbanaChampaign in 2008 and a research fellow of the Japan Society
for the Promotion of Science from 2007 to 2009. His current research interests include mobile sensing and applications in pervasive and ubiquitous computing. He is a member of IEEE, ACM,
IEICE and IPSJ.

Shusuke Saruwatari received the Dr.
Sci. degree from the University of Tokyoin 2007. From 2007 to 2008, he was avisiting researcher at the Illinois Genetic
Algorithm Laboratory, University of Illinois at Urbana-Champaign. From 2008
to 2012, he was a research associate at the
RCAST, the University of Tokyo. From2012 to 2016, he was an assistant professor at Shizuoka University. He has been an associate professor at Osaka University
Since 2016. His research interests are in the areas of wirelessnetworks, sensor networks, and system software.

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Journal of Information Processing Vol.29 1–12 (Jan. 2021)

Takuya Maekawa is an Associate Professor in the Graduate School of Information Science and Technology, Osaka University. He received his bachelor degreefrom School of Engineering, Osaka University in 2003. In 2004, he received his
master degree from Graduate School ofInformation Science and Technology, Osaka University. In 2006, he received his doctor degree (Information Science and Technology) from Graduate School of Information Science and Technology, Osaka University. After that
he worked for NTT Communication Science Laboratory for six
years. He was awarded the IPSJ/IEEE Computer Society Young
Computer Researcher Award on the topic of zero-shot and fewshot unobtrusive context recognition for pervasive computing in
2019. His research interest includes sensor-based context recognition techniques for pervasive/ubiquitous computing.

Kazuya Ohara is a Researcher at NTT
Communication Science Laboratories in
Kyoto, Japan. He received the B.E. degrees, the Master of information scienceand technology degrees, and the Ph.D.
in information science and technology
from Osaka University in 2015, 2016, and2019, respectively. He joined NTT Communication Science Laboratories in 2019. His research interests include ubiquitous computing and machine learning. He isa member of IEEE and IPSJ.

Teruo Higashino received his BS, MS
and Ph.D. degrees in Information andComputer Sciences from Osaka University, Japan in 1979, 1981 and 1984, respectively. He joined the faculty of Osaka University in 1984. Since 2002, hehas been a Professor in Graduate Schoolof Information Science and Technology at
Osaka University. His current research interests include design
and analysis of distributed systems, communication protocol andmobile computing. He is a senior member of IEEE, a fellow ofInformation Processing Society of Japan (IPSJ), and a member of
ACM and IEICE of Japan.

© 2021 Information Processing Society of Japan

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