Leveraging Transfer Learning in Multiple Human Activity Recognition Using WiFi Signal

Existing works on human activity recognition predominantly consider single-person scenarios, which deviates significantly from real world where multiple people exist simultaneously. In this work, we leverage transfer learning, a deep learning technique, to present a framework (TL-HAR) that accurately detects multiple human activities; exploiting CSI of WiFi extracted from 802.11n. Specifically, for the first time we employ packet-level classification and image transformation together with transfer learning to classify complex scenario of multiple human activities. We design an algorithm that extracts activity based CSI using the variance of MIMO subcarriers. Subsequently, TL-HAR transforms CSI to images to capture correlation among subcarriers and use a deep Convolutional Neural Network (d-CNN) to extract representative features for the classification. We further reduce training complexity through transfer learning, that infers knowledge from a pre-trained model. Experimental results confirm the significance of our approach. We show that using transfer learning TL-HAR improves recognition accuracy to 96.7% and 99.1 % for single and multiple MIMO links.