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A Time Series Data Classification Method for Gesture Recognition Based on LSTM and Attention Mechanism
- 1 Institute of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, People’s Republic of China
* Author to whom correspondence should be addressed.
Abstract
This paper addresses the issues of weak model transferability and poor environmental adaptability in cross-domain gesture recognition within wireless sensing technology. A time-series data classification model integrating Long Short-Term Memory (LSTM) and attention mechanism (W-LSTM+A) is proposed. By introducing a feature selection weight matrix to reconstruct the LSTM gating mechanism and combining a dynamic attention allocation strategy, the model’s ability to capture key spatiotemporal features in channel state information is significantly enhanced. Experiments based on a WiFi signal dataset collected in a real office environment compared the performance of CNN, LSTM, and LSTM+A models. The results show that the LSTM+A model achieved a test accuracy of 87.3% after 200 training epochs, significantly outperforming CNN’s 81.9%. Although the LSTM model had a higher final accuracy, its convergence speed was significantly slower than that of the LSTM+A model. Further analysis indicates that the attention mechanism, by strengthening key time-step features, enables the model to quickly capture effective patterns in the early stages of training. However, due to limited sample size, its potential has not been fully realized. This study provides new solutions for the cross-scene adaptability of wireless sensing technology and has application value in smart homes, health monitoring, and other fields.
Keywords
Wireless Sensing, LSTM, Attention Mechanism, Time-Series Data Analysis
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Cite this article
Mao,X. (2025). A Time Series Data Classification Method for Gesture Recognition Based on LSTM and Attention Mechanism. Applied and Computational Engineering,146,51-59.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of SEML 2025 Symposium: Machine Learning Theory and Applications
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