WiFi-based contactless activity recognition on smartphones

Activity recognition is necessary for the interaction between information sensing devices and users. Currently, vision-based and sensor-based methods are widely used. However, most existing systems based on sensors can only work in some fixed locations where sensors have been installed; visionbased systems have requirement on the scope of vision and devices. In this paper, we propose a system that utilizes WiFi Received Signal Strength Indicator(RSSI) in a smartphone for the recognition of activity. It has two advantages: one is that it is contactless and the other is that the hardware facilities are more universal. The system uses the classifiers and Convolutional Neural Networks (CNNs) to train the relation model between the WiFi signals and the activities. We took about one month to obtain continuous data of WiFi device in the same conditions and analyzed the challenge and lessons of the case studies of building a model that can address this nonlinear relationship problem. We made groups experiments to analyze RSSI and made comparison tests base on Channel State Information(CSI). The result of experiments shows the potential of utilizing WiFi signals of the environment for ambient stimuli by human via smartphones. As a preliminary exploration for activity classification, the system utilizing RSSI achieved a mean average precision of 95% based on four Machine Learning methods and up to 97.7% based on CNNs.