A Wi-Fi-based Approach for Recognizing Human-Human Interactions

This paper presents a new approach for recognizing human activities that involve two humans, referred to as human-human interactions, using Wi-Fi signals. The proposed approach utilizes the Channel State Information (CSI) metric of the Wi-Fi signals to characterize the performed interactions in indoor environment. Specifically, the proposed approach analyzes the CSI data and extracts a set of time-domain and frequency-domain features that comprise salient information to distinguish between the performed interactions. The extracted features are used to construct a multi-class support vector machine classifier that can recognize the classes of the interactions comprised within the CSI data. The performance of the proposed approach was evaluated using our publicly available human-human interaction CSI dataset that contains the CSI data recorded for 40 pairs of participants while performing 13 interactions. The experimental results indicate that our proposed approach achieved an average recognition accuracy of 69.78% computed overall the 13 interactions. The reported results for each pair of participants demonstrate the feasibility of our proposed approach to recognize human-human interactions using the CSI metric of the Wi-Fi signals.