Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912257297874944 |
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| author | Ye, Xiaozhou Sakurai, Kouichi Nair, Nirmal Wang, Kevin I-Kai |
| author_facet | Ye, Xiaozhou Sakurai, Kouichi Nair, Nirmal Wang, Kevin I-Kai |
| contents | Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_15422 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review Ye, Xiaozhou Sakurai, Kouichi Nair, Nirmal Wang, Kevin I-Kai Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges. |
| title | Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review |
| topic | Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.15422 |