WiFi‐based passive sensing system for human presence and activity event classification

Abstract

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the timefrequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

“…Wi-Fi based HGR systems capture signal reflections due to human movements between the transmitter and receiver pair as raw CSI traces. It has a wide range of application in the field of surveillance [5], physical analytics [6], healthcare [7] and have become a potential study in the smart home environment [8,9]. It is evident that the following factors, namely the number of users [10] and access point (AP) [11], orientation and distance between the users, as well as the transmitter and receiver pair [12,13], environmental factors [14], interferences, and multipath fading effect in the sensing environment influence the recognition accuracy [15].…”

Physical Activity Sensing via Stand-Alone WiFi Device

“…A higher level of Doppler information can be obtained with the enhanced mode when additional information is required, for example activity recognition or contextual awareness [15]. With the higher density of probe response signal, the enhanced mode has a significant improvement on the Doppler signatures as shown in Fig 6(c), (d) and (e).…”

Detecção de Risco em Ambiente Hospitalar a partir da Informação do Estado do Canal Sem-Fio

“…Outro exemplo, é o Widar2.0 [15] que visa a localização interna através da CSI. Em [16], visa-se detectar a presença humana e classificá-la extraindo o espalhamento Doppler do sinal refletido, o que exige um processamento bastante mais sofisticado, característico das técnicas de CPL (Coherent Passive Localization). Em [17], é apre-sentado o uso dessa ferramenta para reconhecimento de atividades, detecção de respiração e contagem de pessoas.…”