TransTM: A device-free method based on time-streaming multiscale transformer for human activity recognition

RFID-based human activity recognition (HAR) attracts attention due to its convenience, non-invasiveness, and privacy protection. Existing RFID-based HAR methods use modeling, CNN, or LSTM to extract features effectively. Still, they have shortcomings: 1) requiring complex hand-crafted data cleaning processes and 2) only addressing single-person activity recognition based on specific RF signals. To solve these problems, this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM. This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing. Concretely, we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes single-human activities and human-to-human interactions. Compared with existing CNN- and LSTM-based methods, the Transformer-based method has more data fitting power, generalization, and scalability. Furthermore, using RF signals, our method achieves an excellent classification effect on human behavior-based classification tasks. Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy (99.1%). The dataset we collected for detecting RFID-based indoor human activities will be published.