Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

The Evolution of Wi-Fi Technology in Human Motion Recognition: Concepts, Techniques and Future Works

. Human motion recognition is an important topic in computer vision as well as security. It is used in scientific research, surveillance cameras industry and robotics technology as well. The human interaction with the objects creates a complex stance. Multiple artefacts such as clutter, occlusions, and backdrop diversity contribute to the complexity of this technology. Wi-Fi signals with the usage of their features could help solve some of these issues, with the help of other wearable sensors, such as: RGB-D camera, IR sensor (thermal camera), inertial sensor etc. This paper reviews various approaches for Wi-Fi human motion recognition systems, their analytical methodologies, challenges and proposed techniques along with the aspects to this paper: (a) applications; (b) single and multi-modality sensing; (c) Wi-Fi-based techniques; d) challenges and future works. More research related to Wi-Fi human related activity recognition can be encouraged and improved

MIMOCrypt: Multi-User Privacy-Preserving Wi-Fi Sensing via MIMO Encryption

Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractical for multi-user scenarios. Moreover, these proposals all fail to exploit Wi-Fi's multiple-in multiple-out (MIMO) capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi sensing framework to support realistic multi-user scenarios. To thwart unauthorized eavesdropping while retaining the sensing and communication capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to physically encrypt Wi-Fi channels, treating the sensed human activities as physical plaintexts. The encryption scheme is further enhanced via an optimization framework, aiming to strike a balance among i) risk of eavesdropping, ii) sensing accuracy, and iii) communication quality, upon securely conveying decryption keys to legitimate users. We implement a prototype of MIMOCrypt on an SDR platform and perform extensive experiments to evaluate its effectiveness in common application scenarios, especially privacy-sensitive human gesture recognition.Comment: IEEE S&P 2024, 19 pages, 22 figures, including meta reviews and response