With the aging population, there is an increasing demand for rehabilitation services for people with chronic diseases. However, limitations such as medical resources, geographic barriers, and cost make home rehabilitation an option for more patients. Existing wearable devices and vision methods are effective but face problems with portability, cost, and privacy concerns. As for existing wireless sensing methods, they can only extract coarse features for activity recognition. Therefore, we present EchoHealth, which utilizes a smart speaker for rehabilitation exercise detection and assessment. We upgrade the smart speaker into an active sonar system without hardware modification to generate acoustic micro-distance images with motion information. Then, time-domain motion detection and distance-domain feature extraction are utilized to filter out the effects of non-motion time and distance to extract patient motion features for motion recognition. We further assess the patient's rehabilitation exercises from five aspects, based on which EchoHealth provides rehabilitation guidance. Extensive experiments with 15 participants performing 12 rehabilitation motions confirmed that EchoHealth can achieve 97.4% average accuracy in recognition of rehabilitation motion and provide accurate rehabilitation indicators in various environments.

Password, fingerprint and face recognition are the most popular authentication schemes on smartphones. However, these user authentication schemes are threatened by shoulder surfing attacks and spoof attacks. In response to these challenges, eye movements have been utilized to secure user authentication since their concealment and dynamics can reduce the risk of suffering those attacks. However, existing approaches based on eye movements often rely on additional hardware (such as high-resolution eye trackers) or involve a time-consuming authentication process, limiting their practicality for smartphones. This paper presents DEyeAuth, a novel dual-authentication system that overcomes these limitations by integrating eyelid patterns with eye gestures for secure and convenient user authentication on smartphones. DEyeAuth first leverages the unique characteristics of eyelid patterns extracted from the upper eyelid margins or creases to distinguish different users and then utilizes four eye gestures (i.e., looking up, down, left, and right) whose dynamism and randomness can counter threats from image and video spoofing to enhance system security. To the best of our knowledge, we are among the first to discover and prove that the upper eyelid margins and creases can be used as potential biometrics for user authentication. We have implemented the prototype of DEyeAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results indicate that DEyeAuth achieves a high authentication accuracy of 99.38% with a relatively short authentication time of 6.2 seconds, and is effective in resisting image presentation, video replaying, and mimic attacks.

To balance venue safety with public photography rights, this article presents CamShield—a novel system for selective defense against unauthorized photography. Amid dense electromagnetic environments, CamShield reliably identifies cameras by analyzing their unintended electromagnetic emissions. By tracing frequency drift patterns and harmonic spectral movements unique to each device, CamShield can accurately detect cameras despite environmental noise or model similarities. An integrated antenna amplitude ratio module and Kalman filter further localize threats through resilient positioning. Directional ultrasonic beams then focus tuned acoustic interference toward devices, temporarily disrupting visualization in restricted locations while preserving ambient imaging freedoms. Comprehensive evaluations across three state-of-the-art object detectors quantify real-world reliability. With 30 intruding cameras, CamShield exhibited obstruction latencies below 346 ms. Furthermore, CamShield achieves three times the coverage using the same power as traditional Omnidirectional transmission. Together, the breakthroughs in pervasive camera sensing and context-aware actuation contribute toward advancing policy-centric access controls at the edge of cyber-physical convergence. CamShield sets an important precedent on enforcing venue custom protections in bounded secure zones without undermining positive public photography assumptions elsewhere.

Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).

The increasing misuse of commercial drones for illicit activities poses significant challenges in their detection and identification. Existing methods, such as acoustic-based, radio frequency-based, and computer vision approaches, face limitations due to factors like miniaturization, stealth, and background noise. In this paper, we propose E-Argus, a system that leverages the electromagnetic radiation (EMR) emitted by the memory of drones. It is a basic fact that, with all types of drones, the implementation of arbitrary behavior must be digested in the built-in memory, and electromagnetic radiation is thus generated. Specifically, the memory clock drives the switching regulator causing current fluctuations that generate EMR signals at the clock frequency. E-Argus combines the relationship between the flight pattern of the drone and the memory EMR signal, analyzes the unique side-channel signatures, and utilizes advanced neural network-based identification; E-Argus can accurately detect and identify various types of illegal drones. We designed a system prototype based on USRP B210 and conducted experiments in a wide range of scenarios. The evaluation shows that E-Argus has low latency, high accuracy, and robustness in real environments.

Accurate perception of intelligent vehicles is critical for the safe operation of autonomous vehicles. However, current perception methods often struggle to effectively detect intelligent vehicles when obstacles block their field of view. Collaborative perception, although attracting considerable attention, presents challenges in terms of privacy and data trust. In this study, we present a novel design for Enhancing Intelligent Vehicle (), a cost-effective and comprehensive perception system for intelligent vehicles. We discovered that during the process of memory caching raw sensing data in the intelligent vehicle’s system-on-chip (SOC), continuous fluctuating currents inside the memory result in the emission of Electromagnetic Radiation (EMR). As a result, intelligent vehicles actively expose themselves on the electromagnetic spectrum. is based on a set of specially designed antenna arrays that scan the spectrum and utilize a joint Kalman filtering algorithm to enhance EMR signals. The micro-Doppler signature of each EMR signal is then analyzed to identify signals from intelligent vehicles and construct a vehicle database. A multi-antenna joint estimation algorithm is also designed to further estimate the position, distance, and direction of the target vehicle. Our experiments demonstrate that offers advantages in terms of timeliness, robustness, and accuracy.

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, respectively. To address these issues, we propose IA-Track, a novel head motion tracking system that combines inertial measurement units (IMU) and acoustic sensing. Our wireless earphone-based method balances flexibility, computational complexity, and tracking accuracy, requiring only an earphone with an IMU and a smartphone. However, we still face two challenges. First, wireless earphones have limited hardware resources, making acoustic Doppler effect-based method unsuitable for acoustic tracking. Second, traditional Kalman filter-based trajectory restoration methods may introduce significant cumulative errors. To tackle these challenges, we rely on IMU sensor data to recover the trajectory and use smartphones to emit ”inaudible” acoustic signals that the earphone receives to adjust the IMU drift track. We conducted extensive experiments involving 50 volunteers in various potential IA-Track usage scenarios, demonstrating that our well-designed system achieves satisfactory head motion tracking performance.

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection systems (cameras, acoustics, etc.) have made significant progress, but various issues have prevented their widespread adoption, including privacy concerns, line-of-sight requirements, and cost issues. Therefore, there is a critical need for a simple and robust system that can detect eye blinks using common commercial equipment. In this paper, we propose BlinkRadar, which uses a low-cost customized impulse-radio ultra- wideband (IR-UWB) radar for non-contact and fine-grained blink detection. BlinkRadar can reliably detect driver blinks in driving conditions, making it possible to infer drowsy driving. To effectively extract the eye blink signal, we analyzed real experimental data to study the characteristics of the eye blink pattern and successfully used the multi-sequence variational mode decomposition (MS-VMD) algorithm to separate the blink signal from the noise signal. We conducted extensive experiments in two different environments (a quiet room and moving vehicles) and found that BlinkRadar had an average blink detection accuracy of over 96.2%. Our results demonstrate the feasibility of using UWB radar for non-contact eye blink detection.