Differential Channel-State-Information-Based Human Activity Recognition in IoT Networks

Abstract

In this article, we recognize multiple human activities in an Internet-of-Things (IoT) network using differential channel state information (CSI) of the available wireless fidelity (Wi-Fi) signals. Different human activities in the Wi-Fi environment lead to multipath fading, resulting in a change of CSI for each activity. This CSI is sensed by smart IoT devices, such as smartphones, tablets, and laptops for activity recognition. The use of differential CSI mitigates the offset and background noise. Another advantage of the proposed method is that it eliminates the requirement of traditional wearable activity recognition sensors, such as gyroscope, pedometers, and accelerometers. A long short-term memory (LSTM) model is used for automatic feature extraction and classification of human activities from the differential CSI. Training the LSTM model with the phase of differential denoised CSI significantly improves the classification accuracy. The results show a good tradeoff between model complexity and classification accuracy, thereby ensuring better performance as compared to the previous state-of-the-art methods.