CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914306988179456 |
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| author | Yu, Ying Li, Siyao Jiang, Yixuan Xiao, Hang Long, Jingxi Tang, Haotian Liu, Hanyu Li, Chao |
| author_facet | Yu, Ying Li, Siyao Jiang, Yixuan Xiao, Hang Long, Jingxi Tang, Haotian Liu, Hanyu Li, Chao |
| contents | Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate feature extraction, issues such as multimodal data mixing, activity heterogeneity, and complex model deployment remain largely unresolved. The aim of this paper is to address issues such as multimodal data mixing, activity heterogeneity, and complex model deployment in sensor-based human activity recognition. We propose a spatiotemporal attention modal decomposition alignment fusion strategy to tackle the problem of the mixed distribution of sensor data. Key discriminative features of activities are captured through cross-modal spatio-temporal disentangled representation, and gradient modulation is combined to alleviate data heterogeneity. In addition, a wearable deployment simulation system is constructed. We conducted experiments on a large number of public datasets, demonstrating the effectiveness of the model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition Yu, Ying Li, Siyao Jiang, Yixuan Xiao, Hang Long, Jingxi Tang, Haotian Liu, Hanyu Li, Chao Computer Vision and Pattern Recognition Artificial Intelligence Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate feature extraction, issues such as multimodal data mixing, activity heterogeneity, and complex model deployment remain largely unresolved. The aim of this paper is to address issues such as multimodal data mixing, activity heterogeneity, and complex model deployment in sensor-based human activity recognition. We propose a spatiotemporal attention modal decomposition alignment fusion strategy to tackle the problem of the mixed distribution of sensor data. Key discriminative features of activities are captured through cross-modal spatio-temporal disentangled representation, and gradient modulation is combined to alleviate data heterogeneity. In addition, a wearable deployment simulation system is constructed. We conducted experiments on a large number of public datasets, demonstrating the effectiveness of the model. |
| title | CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.21843 |