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, noninvasiveness, 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 singlehuman 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 behaviorbased 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. (c) 2023 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).