A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors
<b>Background</b>: In the fields of rehabilitation, public health, military training and other domains, the accurate and effective monitoring of exercise intensity during exercise can control the occurrence of sports injuries, which is of great significance for people’s healthy lives. &l...
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MDPI AG
2025-05-01
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| author | Lei Pang Yi Li Ming-Xia Liao Jia-Gang Qiu Hui Li Zhen Wang Gang Sun |
| author_facet | Lei Pang Yi Li Ming-Xia Liao Jia-Gang Qiu Hui Li Zhen Wang Gang Sun |
| author_sort | Lei Pang |
| collection | DOAJ |
| description | <b>Background</b>: In the fields of rehabilitation, public health, military training and other domains, the accurate and effective monitoring of exercise intensity during exercise can control the occurrence of sports injuries, which is of great significance for people’s healthy lives. <b>Objective</b>: This study combined easily collectable multi-dimensional sensor data and various algorithm models to achieve cross-individual recognition of low, middle and high levels of exercise intensity. <b>Methods</b>: This study compared the recognition performance of different algorithm models using acceleration and angular velocity sensors worn on seven body parts through individualised body data characteristics. <b>Results</b>: The recognition performances of two classical machine learning algorithms were the worst, with a recognition rate of only 82.97% and 80.31%. The performances of two ensemble learning algorithms were slightly better, with a recognition rate of 88.86% and 87.35%. The deep sub-domain adaptation network algorithm proposed in this study exhibited the best performance, with a recognition rate of 92.87%. This study utilised multi-dimensional sensors to construct a cross-individual exercise intensity recognition model for different parts of the body, and the overall recognition rate of the left part was higher than that of the right part. Moreover, the recognition effect upon wearing sensors on the left side of the body is better than the right in running events. <b>Conclusions</b>: The results of this study have demonstrated the effectiveness of combining domain adaptation methods and multi-dimensional sensors for cross-individual exercise intensity recognition, laying a solid theoretical foundation for broader-scale cross-individual exercise intensity recognition in future research. |
| format | Article |
| id | doaj-art-2b67a71479424f3994e6eaa089214bec |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2b67a71479424f3994e6eaa089214bec2025-08-20T03:46:45ZengMDPI AGSensors1424-82202025-05-012511343710.3390/s25113437A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable SensorsLei Pang0Yi Li1Ming-Xia Liao2Jia-Gang Qiu3Hui Li4Zhen Wang5Gang Sun6Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, ChinaSchool of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100091, China<b>Background</b>: In the fields of rehabilitation, public health, military training and other domains, the accurate and effective monitoring of exercise intensity during exercise can control the occurrence of sports injuries, which is of great significance for people’s healthy lives. <b>Objective</b>: This study combined easily collectable multi-dimensional sensor data and various algorithm models to achieve cross-individual recognition of low, middle and high levels of exercise intensity. <b>Methods</b>: This study compared the recognition performance of different algorithm models using acceleration and angular velocity sensors worn on seven body parts through individualised body data characteristics. <b>Results</b>: The recognition performances of two classical machine learning algorithms were the worst, with a recognition rate of only 82.97% and 80.31%. The performances of two ensemble learning algorithms were slightly better, with a recognition rate of 88.86% and 87.35%. The deep sub-domain adaptation network algorithm proposed in this study exhibited the best performance, with a recognition rate of 92.87%. This study utilised multi-dimensional sensors to construct a cross-individual exercise intensity recognition model for different parts of the body, and the overall recognition rate of the left part was higher than that of the right part. Moreover, the recognition effect upon wearing sensors on the left side of the body is better than the right in running events. <b>Conclusions</b>: The results of this study have demonstrated the effectiveness of combining domain adaptation methods and multi-dimensional sensors for cross-individual exercise intensity recognition, laying a solid theoretical foundation for broader-scale cross-individual exercise intensity recognition in future research.https://www.mdpi.com/1424-8220/25/11/3437cross-individualexercise intensityensemble learningdomain adaptationdeep subdomain adaptation network |
| spellingShingle | Lei Pang Yi Li Ming-Xia Liao Jia-Gang Qiu Hui Li Zhen Wang Gang Sun A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors Sensors cross-individual exercise intensity ensemble learning domain adaptation deep subdomain adaptation network |
| title | A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors |
| title_full | A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors |
| title_fullStr | A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors |
| title_full_unstemmed | A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors |
| title_short | A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors |
| title_sort | feasibility study of domain adaptation for exercise intensity recognition based on wearable sensors |
| topic | cross-individual exercise intensity ensemble learning domain adaptation deep subdomain adaptation network |
| url | https://www.mdpi.com/1424-8220/25/11/3437 |
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