RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915704482037760 |
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| author | Xue, Junxiao Smirnov, Pavel Li, Ziao Shi, Yunyun Chen, Shi Yin, Xinyi Yue, Xiaohan Wang, Lei Wang, Yiduo Lin, Feng Chen, Yijia Ma, Xiao Yan, Xiaoran Zhang, Qing Xue, Fengjian Wu, Xuecheng |
| author_facet | Xue, Junxiao Smirnov, Pavel Li, Ziao Shi, Yunyun Chen, Shi Yin, Xinyi Yue, Xiaohan Wang, Lei Wang, Yiduo Lin, Feng Chen, Yijia Ma, Xiao Yan, Xiaoran Zhang, Qing Xue, Fengjian Wu, Xuecheng |
| contents | We propose a real-time 3D human pose estimation and motion analysis method termed RePose for rehabilitation training. It is capable of real-time monitoring and evaluation of patients'motion during rehabilitation, providing immediate feedback and guidance to assist patients in executing rehabilitation exercises correctly. Firstly, we introduce a unified pipeline for end-to-end real-time human pose estimation and motion analysis using RGB video input from multiple cameras which can be applied to the field of rehabilitation training. The pipeline can help to monitor and correct patients'actions, thus aiding them in regaining muscle strength and motor functions. Secondly, we propose a fast tracking method for medical rehabilitation scenarios with multiple-person interference, which requires less than 1ms for tracking for a single frame. Additionally, we modify SmoothNet for real-time posture estimation, effectively reducing pose estimation errors and restoring the patient's true motion state, making it visually smoother. Finally, we use Unity platform for real-time monitoring and evaluation of patients' motion during rehabilitation, and to display the muscle stress conditions to assist patients with their rehabilitation training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00625 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation Xue, Junxiao Smirnov, Pavel Li, Ziao Shi, Yunyun Chen, Shi Yin, Xinyi Yue, Xiaohan Wang, Lei Wang, Yiduo Lin, Feng Chen, Yijia Ma, Xiao Yan, Xiaoran Zhang, Qing Xue, Fengjian Wu, Xuecheng Computer Vision and Pattern Recognition We propose a real-time 3D human pose estimation and motion analysis method termed RePose for rehabilitation training. It is capable of real-time monitoring and evaluation of patients'motion during rehabilitation, providing immediate feedback and guidance to assist patients in executing rehabilitation exercises correctly. Firstly, we introduce a unified pipeline for end-to-end real-time human pose estimation and motion analysis using RGB video input from multiple cameras which can be applied to the field of rehabilitation training. The pipeline can help to monitor and correct patients'actions, thus aiding them in regaining muscle strength and motor functions. Secondly, we propose a fast tracking method for medical rehabilitation scenarios with multiple-person interference, which requires less than 1ms for tracking for a single frame. Additionally, we modify SmoothNet for real-time posture estimation, effectively reducing pose estimation errors and restoring the patient's true motion state, making it visually smoother. Finally, we use Unity platform for real-time monitoring and evaluation of patients' motion during rehabilitation, and to display the muscle stress conditions to assist patients with their rehabilitation training. |
| title | RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.00625 |