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- Title
Human Action Recognition Based on Hierarchical Multi-Scale Adaptive Conv-Long Short-Term Memory Network.
- Authors
Huang, Qian; Xie, Weiliang; Li, Chang; Wang, Yanfang; Liu, Yanwei
- Abstract
Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches.
- Subjects
UNITED States Code; HUMAN activity recognition; HUMAN-robot interaction
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 19, p10560
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app131910560