Sensing motion using spectral and spatial analysis of WLAN RSSI

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support

Inferring Stop-Locations from WiFi

Human mobility patterns are inherently complex. In terms of understanding these patterns, the process of converting raw data into series of stop-locations and transitions is an important first step which greatly reduces the volume of data, thus simplifying the subsequent analyses. Previous research into the mobility of individuals has focused on inferring 'stop locations' (places of stationarity) from GPS or CDR data, or on detection of state (static/active). In this paper we bridge the gap between the two approaches: we introduce methods for detecting both mobility state and stop-locations. In addition, our methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select the most important routers and one which uses a density-based clustering algorithm to detect router fingerprints. We validate our results using participants' GPS data as well as ground truth data collected during a two month period

Wi-Fi Sensing: Applications and Challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted

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