Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology
Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/7/2037 |
| _version_ | 1797212027810217984 |
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| author | Maria Sideridou Evangelia Kouidi Vassilia Hatzitaki Ioanna Chouvarda |
| author_facet | Maria Sideridou Evangelia Kouidi Vassilia Hatzitaki Ioanna Chouvarda |
| author_sort | Maria Sideridou |
| collection | DOAJ |
| description | Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely. |
| first_indexed | 2024-04-24T10:35:52Z |
| format | Article |
| id | doaj.art-aa11913be0154fa8adafadeb90c48b76 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:35:52Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa11913be0154fa8adafadeb90c48b762024-04-12T13:26:03ZengMDPI AGSensors1424-82202024-03-01247203710.3390/s24072037Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile TechnologyMaria Sideridou0Evangelia Kouidi1Vassilia Hatzitaki2Ioanna Chouvarda3Lab of Computing, Medical Informatics, and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Physical Education and Sport Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Physical Education and Sport Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLab of Computing, Medical Informatics, and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreecePhysical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely.https://www.mdpi.com/1424-8220/24/7/2037pose estimation modelsBlazePosereal-time biomechanical feedback-(BMF)kinematicsmachine learningsignal processing |
| spellingShingle | Maria Sideridou Evangelia Kouidi Vassilia Hatzitaki Ioanna Chouvarda Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology Sensors pose estimation models BlazePose real-time biomechanical feedback-(BMF) kinematics machine learning signal processing |
| title | Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology |
| title_full | Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology |
| title_fullStr | Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology |
| title_full_unstemmed | Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology |
| title_short | Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology |
| title_sort | towards automating personal exercise assessment and guidance with affordable mobile technology |
| topic | pose estimation models BlazePose real-time biomechanical feedback-(BMF) kinematics machine learning signal processing |
| url | https://www.mdpi.com/1424-8220/24/7/2037 |
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