# Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface:
Opportunities and Challenges

Xinyu Li, , Jian Wei You, , Ze Gu, Qian Ma, ,
Long Chen, Jingyuan Zhang, Shi Jin, , and Tie Jun Cui Xinyu Li, Jian Wei You,
Ze Gu, Qian Ma, Long Chen, Jingyuan Zhang and Tie Jun Cui are with the State
Key Laboratory of Millimeter Wave, Southeast University, Nanjing 210096, China
(E-mail: {xinyuli, guze, maqian, 220220703,zhangjingyuan, jvyou,
tjcui}@seu.edu.cn).Shi Jin is with the National Mobile Communications Research
Laboratory, Southeast University, Nanjing 210096, China (E-mail:
jinshi@seu.edu.cn).
(Corresponding author: Jian Wei You; Tie Jun Cui).

###### Abstract

Reconfigurable intelligent surfaces (RISs) have flexible and exceptional
performance in manipulating electromagnetic waves and customizing wireless
channels. These capabilities enable them to provide a plethora of valuable
activity-related information for promoting wireless human sensing. In this
article, we present a comprehensive review of passive human sensing using
radio frequency signals with the assistance of RISs. Specifically, we first
introduce fundamental principles and physical platform of RISs. Subsequently,
based on the specific applications, we categorize the state-of-the-art human
sensing techniques into three types, including human imaging, localization,
and activity recognition. Meanwhile, we would also investigate the benefits
that RISs bring to these applications. Furthermore, we explore the application
of RISs in human micro-motion sensing, and propose a vital signs monitoring
system enhanced by RISs. Experimental results are presented to demonstrate the
promising potential of RISs in sensing vital signs for manipulating
individuals. Finally, we discuss the technical challenges and opportunities in
this field.

###### Index Terms:

Human activity sensing, passive radio-frequency (RF) sensing, reconfigurable
intelligent surface (RIS), multi-person passive recognition.

##  I Introduction

With the advancement of wireless technologies, passive human sensing based on
wireless signals has achieved remarkable success and found applications in
various areas, including health care, surveillance and ambient assisted
living. Such a passive human sensing technique is realized by capturing the
variations in reflected signals (e.g., radio absorption, scattering, and
polarization) caused by the presence and movements of human targets [1]. To
quantify the impact of human bodies on wireless channels for sensing purposes,
off-the-shelf signal measurements, including the received signal strength
indicator (RSSI) and channel state information (CSI), are commonly utilized
[2]. Due to the non-intrusive, non-cooperative and privacy-protecting
superiorities, radio frequency (RF) based sensing has attract significant
attention over the past decade. Moreover, with the widespread deployment of
mobile communication infrastructure, RF-based human sensing could become more
ubiquitous and precise, particularly in indoor environments where high
coverage could be achieved.

However, current wireless human sensing systems face certain fundamental
challenges, limiting their applicability to the real world [2]. For instance,
multipath effects often lead to heavy attenuation of echo signals reflected by
human bodies, making them sensitive to environmental noises. As a result, the
sensing range and accuracy are compromised. Meanwhile, the non-line-of-sight
(Non-LOS) human sensing is challenging and even unfeasible due to the absence
of a direct wireless link between the transmitter (Tx) and the receiver (Rx).
Furthermore, existing wireless sensing systems (e.g., WiFi and portable radar)
have limited degrees of freedom and small aperture, resulting in low
time/spatial resolution. This limitation leads to the mixing of reflected
signals from different components at the receiver, further degrading the
overall sensing performance. To fix these issues, we contemplate the following
question: can we dynamically control signal propagation and elaborate
customized radio channels for human sensing purposes?

Figure 1: Schematic of RIS-enhanced passive human sensing, which is composed
of the physical layer, the digital layer and the sensing layer.

The reconfigurable intelligent surface (RIS) [3], composed of a two-
dimensional array of meta-atoms and renowned for its exceptional
electromagnetic wavefront manipulation capabilities, holds great promise in
addressing the aforementioned challenges. By individually programming the
phase and amplitude of each reconfigurable meta-atom, the RIS can flexibly
adjust unwanted propagation channels into desirable ones to facilitate high-
quality passive human sensing. Compared with the conventional RF-based passive
human sensing systems, the RIS-aided system incorporates a unique trainable
physical layer composed of the reconfigurable metasurface, as shown in Fig. 1.
Such an alteration enables customization of both the physical (the RIS) and
the digital (the sensing algorithm) layers, thereby promoting the performance
of passive human sensing at the physical and digital levels [4].

In this paper, we present a comprehensive review of passive human sensing
enhanced by RISs. We first provide a brief introduction to the background and
implementation of RIS for RF-based human sensing. Subsequently, we place our
main focus on three sensing tasks, i.e., imaging and segmentation,
localization and tracking, and human posture recognition. Additionally, we
provide an overview of existing RIS-aided passive human sensing systems.
Furthermore, beyond the three typical tasks, some preliminary experimental
results are provided to demonstrate the potential applications of RISs in
multi-person micro-motion sensing. Finally, we discuss various challenges and
opportunities for future investigations in this field before presenting
concluding remarks.

##  II Background and Implementation of RISs

###  II-A Background of RISs

RIS technology is enabled by metasurfaces, which are two-dimensional (2D)
artificial surfaces composed of periodic or quasi-periodic meta-atoms in
subwavelength scale [3].To date, the overwhelming majority of RISs used in RF-
based human sensing have employed the space-domain-coding (SDC) pattern. For
SDC RISs, the coding sequences are generally optimized in the spatial domain
and fixed in time, switching with the command of the control system [5]. The
advantages of employing SDC RISs for sensing can be summarized as follows.
Firstly, the SDC RISs enable the customization of propagation channels to
enhance sensing capabilities, and make Non-LOS sensing feasible. Secondly, SDC
RISs aid in focusing sensing signals on specific areas of interest,
significantly reducing interferences from irrelevant regions and improving
signal-to-noise ratio (SNR) for long-range sensing. Additionally, SDC RIS is
able to break through the resolution limitation of the existing wireless
sensing systems with a simple hardware structure, empowering wireless systems
with greater sensing capabilities. Thanks to these distinctive advantages, SDC
RISs have been effectively applied to a wide range of human sensing tasks,
leading to significant achievements in this field.

Furthermore, by jointly encoding the parameters (e.g., reflection amplitudes
and phases) of RISs in time and space, the EM waves could be manipulated in
both the spatial and spectral domains [5]. By this means, one can
simultaneously manipulate the harmonic distribution (frequency domain) and the
propagation direction (spatial domain) of EM waves. Although spatial-time-
coding (STC) RISs have made significant progress and found applications in
various fields such as wireless communications, mobile user localization, and
tracking, they are seldom utilized for passive human sensing tasks.
Nevertheless, there is promising potential om employing STC RISs to overcome
the current challenges in passive human sensing and achieve performance
improvements, which will be introduced in Section IV.

Figure 2: The 1-bit RIS implementation, and the corresponding magnitude-
frequency and phase-frequency response of the designed meta-atom.

###  II-B RIS Implementation

A 1-bit RIS operating at $\sim$3.5 GHz is designed as shown in Fig. 2. CST
Microwave Studio is used to design the meta-atom. The digital meta-atom
includes two PIN diodes, each of which is integrated to electrically and
dynamically control the reflected EM response. Furthermore, from the
magnitude-frequency and phase-frequency response of the designed meta-atom, it
could be found that there is nearly no amplitude difference in the reflected
EM waves between the ”0” and ”1” states at around 3.5 GHz, while the
corresponding phase difference is approximately 180 degrees. As a result, this
meta-atom can be effectively utilized in the design of a phase-based digital
RIS.

##  III RIS-aided Passive Human Sensing

In this section, we discuss three typical passive human sensing applications
with the aid of RISs: imaging and segmentation, localization and tracking, and
human activity recognition (HAR). Additionally, we provide an overview of
existing algorithms for passive human sensing enhanced by RISs in Table I.

TABLE I: RIS-aided Passive Human Sensing

| Ref.
---
year
Applications | RIS coding | Signal frequency | Sensing algorithm | Multi-person | Real-time | Performance summary
| [6]
---
2019
Imaging | SDC | 3 GHz |  | PCA
---
No | Yes |  | Measured results: $\sim$5 dB SNR of
---
the reconstructed images with 300
measurements.
| [7]
---
2019
Imaging &HAR | SDC | 2.4 GHz | DNN | Yes | Yes |  | Measured results: an accuracy of
---
0.94 for recognizing ten hand
signs with WiFi signals, and 0.75
SSIM for imaging with 30 coding
patterns.
| [8]
---
2020
Imaging | SDC | 2.4 GHz | ANNs | No | Yes |  | Measured results: $\sim$0.9 SSIM for
---
imaging with 15 coding patterns.
| [9]
---
2022
Segmentation | SDC | 3.2 GHz | MLPs | No | N/A |  | Measured results: an average error
---
rate of <=1% for image segmentation.
| [10]
---
2021
3D Localization | SDC | N/A | DRL | No | N/A |  | Simulated results: CE loss of 0.9
---
with 16 space grids in 2D space;
CE loss of 1.5 with 32 space grids
in 3D space.
| [11]
---
2022
Localization |  | SDC with
---
sensors
N/A | MUSIC | Yes | N/A |  | Simulated results: RMSE of $10^{-2}$
---
degree with 15 dBm transmit power.
| [12]
---
2022
Localization | SDC | 3 GHz | WPSO | Yes | N/A |  | Simulated results: localization proba
---
-bility of approximately 0.99 with 20
detection cycles.
| [13]
---
2020
| Static posture
---
recognition
SDC | 3.2 GHz | FCAO & DNN | No | N/A |  | Measured results: an accuracy of
---
0.97 in recognizing four human
postures.
| [14]
---
2021
| Writing motion
---
recognition
SDC | 19.4 GHz | AEMML | No | N/A |  | Measured results: an accuracy of
---
over 0.93 for recognizing ten writing
motions.
| [15]
---
2022
Activity recognition | SDC | 2.4 GHz | DNN | No | Yes |  | Measured results: Imaging with appro
---
-ximately 50% reconstructed images
having a normalized PSNR of 75. The
normalized PSNR value ranges from 1
to 100.

  * •

SSIM: structure similarity index metric; RMSE: root mean square error; CE:
cross-entropy; PSNR: peak signal-to-noise ratio.

###  III-A Imaging and Segmentation

RISs are capable of compensating for the limited spatial resolution in current
wireless sensing systems, and have made significant advancements in human
target imaging. Compared with the commonly used imaging schemes (i.e., real-
aperture, synthetic aperture, and coding aperture imaging) that often face
challenges in balancing hardware complexity and sensing algorithms, RIS-
empowered imaging techniques provide a more efficient and simplified approach
to achieve accurate target imaging. For instance, L. Li et al. [6] proposed a
machine-learning reprogrammable imager with RISs. In this work, a linear
relationship between the measured reflected signal y and the scene x to be
imaged was established, i.e., y=Hx. Then, principal component analysis (PCA)
was employed to extract principle scene components serving as illumination
patterns H. On top of that, a deep-learning (DL) based imaging algorithm was
developed for data-driven intelligent imaging [8]. The RIS configuration was
treated as trainable physical weights and trained with the digital weights of
an artificial neural network (ANN) alternatively. Meanwhile, a RIS-empowered
imaging and scene segmentation approach was presented [9]. By optimizing
coding sequence of the RIS, the measurements of the customized radio channel H
could be utilized to estimate the reflection coefficients of objects.
Subsequently, a symmetric multilayer perceptron (MLP) model was proposed to
segment different objects using the estimated reflection coefficients.

RIS-aided human imaging techniques can significantly promote the development
of passive human sensing. In other words, the reconstructed images could
intuitively capture the physical characteristic of the person, and further
infer his body posture, activity and even intention. Meanwhile, the presence
of multiple persons could be detected with RIS-aided imaging, which is
expected to promote multi-person passive sensing. Furthermore, scene imaging
enables a comprehensive understanding of the environment so as to generate
desirable signal beams for further fine-grained sensing.

###  III-B Localization and Tracking

In RF-based human localization, the target location is generally determined by
forming triangles from known points to the target person. In this
circumstance, multiple transmitting/receiving antennas are required to improve
the localization accuracy, which inevitably increases the system’s hardware
complexity and deployment difficulty. By contrast, RISs with dense arrays of
unit cells could increase the number of antennas to promote localization in a
more efficient manner. In addition to the direct link between the target and
the Tx, the RISs could provide extra LoS reflected links to perceive the
target from other orientations, thus potentially enhancing localization
performance. The benefits above have spurred active research on designing
various RIS-aided human localization systems. For instance, a semi-passive
RIS-empowered localization system, where the RIS is equipped with dedicated
sensors to receive echoes for target localization, has been proposed [11]. The
multiple signal classification (MUSIC) algorithm was applied to estimate the
direction-of-angle (DOA) of the person with the received echo signals.

To provide desirable signal propagation properties and achieve high sensing
accuracy, it is critical to optimize the coding patterns of the RIS.
Conventional RIS-assisted localization algorithms generally treat coding
sequence optimization and human imaging as separate issues, and the
interaction between them has not been considered. To address this limitation,
a deep reinforcement learning (DRL) algorithm was proposed to jointly compute
the optimal coding patterns and the mapping of the received signals [10].
Through the joint optimization of hardware and software, the proposed approach
was able to sense the presence and locations of 3D objects with a remarkable
performance. Meanwhile, H. Zhang et al. [12] proposed a waveform and phase
shift optimization (WPSO) algorithm for optimizing the radar waveforms and the
RIS.

###  III-C Human Activity Recognition

RF-based HAR aims to automatically recognize different human activities by
analyzing the impacts of body movements on wireless signal propagation.
Conventional RF sensing techniques need to passively adapt to and are
generally constrained by the radio environment, which limits the diversity of
wireless channels for carrying abundant information about human activities.
Furthermore, due to the unpredictable nature of wireless environments, the
feasibility and accuracy of activity recognition are greatly affected by the
undesirable multi-path fading. To deal with this challenge, RIS-aided HAR
techniques have been increasingly investigated. In HAR tasks, the RIS is able
to continuously customize wireless propagation properties and generate a high
dimension of independent channel measurements to enhance activity recognition
performance.

J. Hu et al. [13] proposed a RIS-assisted RF sensing system for recognizing
daily human activities. A frame configuration alternating optimization (FCAO)
algorithm was first proposed for finding a set of optimal coding sequences of
the RIS. Subsequently, a supervised deep neural network (DNN) was proposed to
extract features from the received channel information and inference human
activities. Similarly, a dynamic metasurface antenna (DMA) was presented for
writing motion recognition [14]. The concept ”normalized entropy” was proposed
to assess the sensing quality of different DMA pattern configurations and
select the optimal patterns. Furthermore, a quality-aware auxiliary-assisted
ensemble multimask learning (AEMML) algorithm was presented to dynamically
aggregate the heterogeneous DMA measurements for better sensing performance.
Additionally, to explore the time-varying characteristics of human behaviors,
a RIS-assisted “camera” [15] was proposed to produce microwave videos of
moving persons in real-time. Experimental results showed that the EM camera
successfully recognized human activities behind a 60 cm-thickness reinforced
concrete wall with desirable performance in terms of image quality and frame
rate.

##  IV Over-the-air Experiments and Results

While significant advancements have been achieved in intelligent RIS-aided
human macroscopic activity sensing, there has been limited research on micro-
motion (e.g., vital signs) recognition. In this section, we study multi-person
respiration monitoring enhanced by SDC and STC RISs, and present some
preliminary experimental results.

###  IV-A Experimental Setup

As shown in Fig. 3, the RIS-enhanced RF sensing system consists of a
transceiver module and a RIS module. In the transceiver module, a software-
defined radio (SDR) device is adopted and connected with two antennas for
signal transmission and reception. Two low-noise amplifiers (LNAs) are
connected with the Tx and Rx antennas to amplify the transmitted and received
signals of the SDR, respectively. Meanwhile, a host computer is integrated
into the system to control the SDR based on the GNU packet and also serves as
a data processor for vital signs sensing.

Figure 3: Experimental setup of the RIS-enhanced RF sensing system, which
consists of a transceiver module and a RIS module. When the RIS is deployed in
this system, the signal gain at the receiving end has been enhanced by over 10
dBm.

To achieve contactless physiological signal sensing, a 1 kHz baseband tone
signal is generated using the GNU packet, and transmitted on the 3.5 GHz
carrier frequency through the SDR. The transmitted tone signal can be
represented as $Ae^{j(2\pi f_{c}t)}$, where $A$ is the signal amplitude, and
$f_{c}$ is the carrier frequency, i.e., (3.5+1e-6) GHz. Subsequently, the RIS
manipulates the illuminated signals, redirecting them towards the human body.
The movements of the human thorax during respiration modify the magnitude of
the transmitted signal, leading to amplitude modulation. In other words, the
signal received by the SDR could be expressed as $m(t)Ae^{j(2\pi f_{c}t)}$,
where $m(t)$ represents the respiration signal that modulates the transmitted
tone signal. To extract $m(t)$ from the demodulated baseband signal, the
signal amplitude is first reconstructed with the in-phase (I) and quadrature
(Q) components of the received signal and then, the envelope of the amplitude
could be a desirable approximation of $m(t)$. Subsequently, as the normal
respiration rate of a person is 12 to 20 beats per minute, the envelope data
is downsampled to 200 Hz. Then, a low pass filter is employed to filtered out
the high-frequency noise. Finally, a peak detection algorithm is proposed to
estimate human respiration rate.

###  IV-B Experimental Results and Analysis

First, we utilize an SDC RIS to manipulate EM waves and perform single-person
respiration sensing. With the experimental deployment in Fig. 3, the signal
gain at the receiving end has been enhanced by over 10 dBm, indicating the SDC
RIS is capable of significantly improving the signal quality and potentially
enhancing sensing performance. Furthermore, when there is a person in the
reflection area, the extracted signal $m(t)_{D1}$ after low-pass filtering is
shown by the red line in Fig. 4. Compared with the extracted signal
$m(t)_{D2}$ (the green line in Fig. 4) when the SDC RIS is not integrated,
$m(t)_{D1}$ has more distinct periodic properties, leading to more accurate
estimation of the respiration rate. The experimental results indicate that by
redirecting signal beams towards the human body with the SDC RIS, other
objects in the open space will reflect less signal, thus reducing the
interference of the human echo signals at the physical layer and improving the
SNR of sensing signals.

Figure 4: The extracted respiration signals with and w/o the SDC RIS. Compared
with the reflected signal when the SDC RIS is not integrated, the signal from
the RIS-enhanced system has more distinct periodic properties.

On the other hand, when there are multiple persons in the reflection area of
the SDC RIS-assisted respiration monitoring system, the received echoes of
these persons become intertwined in the time, frequency and spatial domains.
Consequently, it becomes challenging to disentangle these mixed echoes at the
receiving end and simultaneously monitor the respiration of multiple
individuals using the SDC RIS. To achieve multi-person respiration monitoring,
an STC RIS could be incorporated into the RF sensing system instead of the SDC
RIS. As the STC RIS is able to modulate the incident wave into a set of
harmonic components with different reflection directions, the harmonic waves
could redirect the persons at different locations, respectively. Specifically,
we assign the harmonic components with different frequencies to different
human targets, and align the main beam of each harmonic to the corresponding
individual. Then, the reflected echoes from different persons could be
separated effectively in the frequency domain.

Figure 5: The extracted respiration signals of two persons in the time and the
frequency domains.

To demonstrate the feasibility and effectiveness of STC RIS-assisted multi-
person respiration estimation, an STC RIS with an operating frequency of
approximately 3.5 GHz is integrated into the RF sensing system. In the
experiment, two persons were positioned respectively at 45 degrees on both
sides of the RIS normal. By using a well-designed STC sequences, the reflected
beams at the +1st ($f_{c}$ \+ 455 Hz) and -1st ($f_{c}$ \- 455 Hz) harmonic
frequencies are transmitted along the two directions and illuminate the two
persons for sensing. By using only one Rx, the reflected echo signals
$m(t)_{T1}$ and $m(t)_{T2}$ from the two persons are received and then
separated in the frequency domain by using band-pass filters. The envelope of
$m(t)_{T1}$ and $m(t)_{T2}$ are shown in Fig. 5. Meanwhile, the short-time
Fourier Transform (STFT) is conducted on $m(t)_{T1}$ and $m(t)_{T2}$ to obtain
the time-frequency map, which could also indicate the periodic movements of
the chest due to breathing. Experimental results show that the extracted
signals have the same periodicity as human breathing, demonstrating the
potential of using STC RISs for multi-person respiration monitoring.

##  V Challenges and Opportunities

There are a number of open problems in the field of passive human sensing with
the aid of RISs. In this section, we briefly discuss several research
challenges and future opportunities.

###  V-A Multi-person Activity Sensing

State-of-the-art RF-based human sensing techniques have achieved excellent
performance in single-person activity sensing. However, when it comes to
sensing multiple persons, the reflected signals from different persons are
generally coupled together, degrading the performance of these approaches. To
address this limitation, RISs could be used to separate the mixed reflected
echoes.

For instance, the SDC RIS could manipulate EM beams through time-division
multiplexing so that the signal can irradiate different persons in turn to
achieve multi-person sensing. However, such a time-division multiplexing
sensing scheme exhibits low efficiency, which limits real-time performance.
Additionally, the potential of STC RISs for multi-person respiration
monitoring has been preliminary verified in Section IV. These STC RISs possess
echo separation capabilities at the physical layer level, which could be
further applied for multi-person sensing. Furthermore, in the field of active
RF sensing, RISs have been successfully employed to localize multiple mobile
users , which could provide some inspiration for designing passive multi-
person sensing algorithms.

###  V-B Vital Signs Monitoring of Moving Persons

Current passive RF sensing systems predominantly focus on vital signs
monitoring of static persons. When these approaches are utilized for vital
signs sensing of moving persons, the performance may degrade significantly. It
is because the signals reflected by the human torso and limbs are mixed with
physiological signals at the receiving end, and the physiological signals may
even be overwhelmed by the reflected echoes from macroscopic body movements.
In this case, directing the EM signals towards a specific body part could be a
potential solution to eliminate the effect of macroscopic human movements [7].

More specifically, taking the respiration sensing task as an example, since
the moving speed of the thorax is determined by both human macro- and micro-
movements, it is essential to compensate for the effect of macro-motions on
the sensing measurements, and estimate the distance/speed variations of the
thorax caused by respiration alone for the sensing purpose. In future
research, we will elaborate on the algorithms for micro-movement estimation
and realize vital signs sensing of moving persons.

###  V-C Model-driven Human Sensing with RISs

Model-driven RF-based human sensing is achieved by quantitatively estimating
the effects of the human body on wireless signal propagation (e.g.,
reflection, diffraction and scattering). Compared with the data-driven human
sensing technique, the model-driven alternative does not rely on large-scale
data and has strong interpretability, which hence is suitable for sensing
scenarios that require low latency and high reliability.

However, when a RIS is integrated into the RF system, the amplitude and phase
of channel indicators are simultaneously impacted by the RIS and human
movements. In this circumstance, it is vital to decouple the influence of the
two factors and individually extract the channel variations caused by human
bodies for passive human sensing. Furthermore, though the changes of channel
properties caused by RISs could be theoretically derived, accurately
qualifying the combined influence of multiple meta-atoms in a real RF system
poses practical challenges. Hence it is nontrivial to focus more on the RIS
design and the RF system deployment.

###  V-D Joint Optimization of RIS Configuration and Sensing Algorithm

Jointly optimizing the variables in both the physical and digital layers
enables the sensing pipeline to be task-aware and could select more effective
information relevant to the task from the received RF signals. To this end,
the DL technique is expected to create an end-to-end pipeline for jointly
optimizing the physical and digital variables. However, the digitalization
characteristic of the meta-atoms introduces discontinuous physical weights,
leading to an incompatibility with the existing error backpropagation
algorithms. Therefore, it is crucial to develop tailored DL models for RIS
configuration optimization.

Additionally, exploiting information from previous measurements can be a
promising approach to acquire optimal system weights and extract more
efficient information from the received echoes for sensing tasks. This
approach draws inspiration from the recurrent visual attention of humans. In
other words, the update of RIS configuration could be impacted based on both
the current information and the previous sensing results provided by the
digital layer, thereby enabling adaptive customization of more desirable
wireless environment for the current sensing task.

###  V-E Privacy Security of Passive Human Sensing

Due to the broadcast nature of wireless signal propagation, it is likely that
the privacy leakage cannot be prevented in passive human sensing. In this
case, the sensitive information such as the human locations, activities and
vital signs could be exposed to the air interface, where the malicious users
can intercept such sensitive information and further violate user privacy.

To deal with this issue, an integrated human sensing and security design can
be performed, where the RIS-aided sensing and the privacy security can
mutually benefit by sharing spectrum, power and hardware resources, etc. For
example, as per the illegal user sensing, the RIS can be equipped with active
reflection elements, i.e., active RIS, to authenticate the user identity via
the radio-frequency fingerprint (RFF) or the physical unclonable function
(PUF), and then control the electromagnetic waves to confuse the unauthorized
sensing. The challenges mainly include the unified framework design of
integrated human sensing and security, especially for the open wireless
environment, and the efficient acquisition of RFF or PUF with a low overhead.

##  VI Conclusion

A comprehensive review of RF-based human sensing with the assistance of RISs
was presented here. We outlined the background of intelligent metasurfaces and
gave an in-depth analysis on the sensing characteristics of the SDC and STC
RISs. Subsequently, we categorized the existing RIS-aided passive human
sensing systems into three typical applications, followed by a detailed
discussion on their deployments and implementations for the sensing purpose.
Furthermore, RF-based human respiration monitoring assisted by SDC and STC
RISs was investigated. The experimental setup was introduced briefly, and some
experimental results were then provided to demonstrate the potential of using
STC RISs for multi-person micro-motion sensing. Finally, we presented four key
challenges and open research problems to facilitate a transition of the
techniques into real-world applications.

## Acknowledgements

This work was supported in part by the Postdoctoral Innovation Talents Support
Program under Grant BX20230066, in part by the Jiangsu Planned Projects for
Postdoctoral Research Fund under Grant 2023ZB318, in part by the National
Natural Science Foundation of China under Grant 62371131, 62288101, 62371131,
and 62371132, in part by the Natural Science Foundation of Jiangsu Province
under Grant BK20230820, and in part by Fundamental Research Funds for the
Central Universities under Grant 2242023K5002, and in part by the China
Postdoctoral Science Foundation under Grant 2021M700761 and 2022T150112.

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