Bibliographic Details
| Title: |
TransTM: A device-free method based on time-streaming multiscale transformer for human activity recognition |
| Authors: |
Yi Liu, Weiqing Huang, Shang Jiang, Bobai Zhao, Shuai Wang, Siye Wang, Yanfang Zhang |
| Source: |
Defence Technology, Vol 32, Iss , Pp 619-628 (2024) |
| Publisher Information: |
KeAi Communications Co., Ltd., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Military Science |
| Subject Terms: |
Human activity recognition, RFID, Transformer, Military Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2214-9147 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2214914723000508; https://doaj.org/toc/2214-9147 |
| DOI: |
10.1016/j.dt.2023.02.021 |
| Access URL: |
https://doaj.org/article/ceebf1857d584300b7e69a8408c9948e |
| Accession Number: |
edsdoj.bf1857d584300b7e69a8408c9948e |
| Database: |
Directory of Open Access Journals |
