Evaluating 3D Human Motion Capture on Mobile Devices
Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/4806 |
| _version_ | 1827670604417859584 |
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| author | Lara Marie Reimer Maximilian Kapsecker Takashi Fukushima Stephan M. Jonas |
| author_facet | Lara Marie Reimer Maximilian Kapsecker Takashi Fukushima Stephan M. Jonas |
| author_sort | Lara Marie Reimer |
| collection | DOAJ |
| description | Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area. |
| first_indexed | 2024-03-10T03:25:23Z |
| format | Article |
| id | doaj.art-6d61b23a9283450b845f1a3e77c1f072 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:25:23Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6d61b23a9283450b845f1a3e77c1f0722023-11-23T09:53:27ZengMDPI AGApplied Sciences2076-34172022-05-011210480610.3390/app12104806Evaluating 3D Human Motion Capture on Mobile DevicesLara Marie Reimer0Maximilian Kapsecker1Takashi Fukushima2Stephan M. Jonas3Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, GermanyDepartment of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, GermanyDepartment of Sports and Health Sciences, Technical University of Munich, Georg-Brauchle-Ring 60/62, 80992 München, GermanyInstitute for Digital Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, GermanyComputer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.https://www.mdpi.com/2076-3417/12/10/4806human motion capturemobile motion captureoptical motion captureconsumer electronicsmHealthdHealth |
| spellingShingle | Lara Marie Reimer Maximilian Kapsecker Takashi Fukushima Stephan M. Jonas Evaluating 3D Human Motion Capture on Mobile Devices Applied Sciences human motion capture mobile motion capture optical motion capture consumer electronics mHealth dHealth |
| title | Evaluating 3D Human Motion Capture on Mobile Devices |
| title_full | Evaluating 3D Human Motion Capture on Mobile Devices |
| title_fullStr | Evaluating 3D Human Motion Capture on Mobile Devices |
| title_full_unstemmed | Evaluating 3D Human Motion Capture on Mobile Devices |
| title_short | Evaluating 3D Human Motion Capture on Mobile Devices |
| title_sort | evaluating 3d human motion capture on mobile devices |
| topic | human motion capture mobile motion capture optical motion capture consumer electronics mHealth dHealth |
| url | https://www.mdpi.com/2076-3417/12/10/4806 |
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