WiHAR: From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition

A robust and unobtrusive human activity recognition system is essential to a multitude of applications, such as health care, active assisted living, robotics, sports, and tele-immersion. Existing well-performing activity recognition methods are either vision- or wearable sensor-based. However, they are not fully passive. In this paper, we develop WiHAR—an unobtrusive Wi-Fi-based activity recognition system. WiHAR uses the Wi-Fi network interface card to capture the channel state information (CSI) data. These CSI data are effectively processed, and then amplitude and phase information is used to obtain the spectrogram. In the subsequent step, the time-variant mean Doppler shift (MDS) caused by the human body movements in the radio signals before their arrival at the receiver is estimated. The MDS is used to extract time and frequency domain features that are needed to train the supervised learning algorithms (i.e., decision tree, linear discriminant analysis, and support vector machines (SVM)) to assess the performance of the WiHAR. Our results show that WiHAR combined with SVM achieves 96.2% recognition accuracy on the data set consisting of 9 participants where each participant performed four activities including: walking, falling, picking up an object from the ground, and sitting on a chair.