A multi-sensor fusion model based on Bidirectional Long Short-Term Memory (BiLSTM) and gated attention mechanism is proposed to address the challenges of heterogeneous data fusion and insufficient real-time performance in multi-sensor human activity recognition. This method captures bidirectional long-term dependencies through BiLSTM and employs gated attention to achieve adaptive weighting of multi-source features, effectively enhancing feature representation capability and fusion efficiency. Experimental results demonstrate that the proposed approach achieves a highest accuracy of 95.3%,in addition, under a comparable lightweight setting, the proposed model exhibits lower parameter count and faster inference latency than the Transformer baseline, thereby achieving a more favorable balance between accuracy and efficiency.Ablation studies further confirm the critical role of BiLSTM and the attention mechanism in improving performance. This research provides a solution that balances recognition accuracy and inference efficiency for behavior recognition in complex scenarios, showing strong potential for practical applications.
Key words
multi-sensor fusion /
attention mechanism /
BiLSTM /
human activity recognition (HAR)
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