Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning

• URL: http://arxiv.org/abs/2504.03687v1
• Date: Sat, 22 Mar 2025 16:48:16 GMT
• Title: Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning
• Authors: Hanyu Liu, Ying Yu, Hang Xiao, Siyao Li, Xuze Li, Jiarui Li, Haotian Tang,
• Abstract summary: We propose a comprehensive optimization process approach centered on multi-attention interaction.<n>We conduct extensive testing on three public datasets, including ablation studies, comparisons of related work and embedded deployments.
• Score: 9.445469731895505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensor-based human activity recognition is a key technology for many human-centered intelligent applications. However, this research is still in its infancy and faces many unresolved challenges. To address these, we propose a comprehensive optimization process approach centered on multi-attention interaction. We first utilize unsupervised statistical feature-guided diffusion models for highly adaptive data enhancement, and introduce a novel network architecture-Multi-branch Spatiotemporal Interaction Network, which uses multi-branch features at different levels to effectively Sequential ), which uses multi-branch features at different levels to effectively Sequential spatio-temporal interaction to enhance the ability to mine advanced latent features. In addition, we adopt a multi-loss function fusion strategy in the training phase to dynamically adjust the fusion weights between batches to optimize the training results. Finally, we also conducted actual deployment on embedded devices to extensively test the practical feasibility of the proposed method in existing work. We conduct extensive testing on three public datasets, including ablation studies, comparisons of related work, and embedded deployments.

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