Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916131934044160 |
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| author | Smyrnakis, Nikolaos Karakostas, Tasos Cotton, R. James |
| author_facet | Smyrnakis, Nikolaos Karakostas, Tasos Cotton, R. James |
| contents | Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for estimating gait parameters from videos can produce physically implausible results. To overcome this, we train a policy using reinforcement learning to control a physics simulation of human movement to replicate the movement seen in video. This forces the inferred movements to be physically plausible, while improving the accuracy of the inferred step length and walking velocity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation Smyrnakis, Nikolaos Karakostas, Tasos Cotton, R. James Computer Vision and Pattern Recognition Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for estimating gait parameters from videos can produce physically implausible results. To overcome this, we train a policy using reinforcement learning to control a physics simulation of human movement to replicate the movement seen in video. This forces the inferred movements to be physically plausible, while improving the accuracy of the inferred step length and walking velocity. |
| title | Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.12676 |