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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.08268 |
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| _version_ | 1866911334146244608 |
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| author | Peiffer, J. D. Shah, Kunal Djuraskovic, Irina Anarwala, Shawana Abdou, Kayan Patel, Rujvee Jayabalan, Prakash Pennicooke, Brenton Cotton, R. James |
| author_facet | Peiffer, J. D. Shah, Kunal Djuraskovic, Irina Anarwala, Shawana Abdou, Kayan Patel, Rujvee Jayabalan, Prakash Pennicooke, Brenton Cotton, R. James |
| contents | Movement directly reflects neurological and musculoskeletal health, yet objective biomechanical assessment is rarely available in routine care. We introduce Portable Biomechanics Laboratory (PBL), a secure platform for fitting biomechanical models to video collected with a handheld, moving, smartphone. We validate this approach on over 15 hours of data synchronized to ground truth motion capture, finding mean joint-angle errors < 3$°$ and pelvis-translation errors of a few centimeters across patients with neurological-injury, lower-limb prosthesis users, pediatric in-patients, and controls. In > 5 hours of prospective deployments to neurosurgery and sports-medicine clinics, PBL was easy to setup, yielded highly reliable gait metrics (ICC > 0.9), and detected clinically relevant differences. For cervical-myelopathy patients, its measurement of gait quality correlated with modified Japanese Orthopedic Association (mJOA) scores and were responsive to clinical intervention. Handheld smartphone video can therefore deliver accurate, scalable, and low-burden biomechanical measurement, enabling greatly increased monitoring of movement impairments. We release the first clinically-validated method for measuring whole-body kinematics from handheld smartphone video at https://IntelligentSensingAndRehabilitation.github.io/MonocularBiomechanics/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone Peiffer, J. D. Shah, Kunal Djuraskovic, Irina Anarwala, Shawana Abdou, Kayan Patel, Rujvee Jayabalan, Prakash Pennicooke, Brenton Cotton, R. James Computer Vision and Pattern Recognition Movement directly reflects neurological and musculoskeletal health, yet objective biomechanical assessment is rarely available in routine care. We introduce Portable Biomechanics Laboratory (PBL), a secure platform for fitting biomechanical models to video collected with a handheld, moving, smartphone. We validate this approach on over 15 hours of data synchronized to ground truth motion capture, finding mean joint-angle errors < 3$°$ and pelvis-translation errors of a few centimeters across patients with neurological-injury, lower-limb prosthesis users, pediatric in-patients, and controls. In > 5 hours of prospective deployments to neurosurgery and sports-medicine clinics, PBL was easy to setup, yielded highly reliable gait metrics (ICC > 0.9), and detected clinically relevant differences. For cervical-myelopathy patients, its measurement of gait quality correlated with modified Japanese Orthopedic Association (mJOA) scores and were responsive to clinical intervention. Handheld smartphone video can therefore deliver accurate, scalable, and low-burden biomechanical measurement, enabling greatly increased monitoring of movement impairments. We release the first clinically-validated method for measuring whole-body kinematics from handheld smartphone video at https://IntelligentSensingAndRehabilitation.github.io/MonocularBiomechanics/. |
| title | Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.08268 |