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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.04721 |
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| _version_ | 1866909027353493504 |
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| author | Kumar, Shashwat Rahman, Arafat Gutierrez, Robert Livermon, Sarah McCrady, Allison N. Blemker, Silvia Scharf, Rebecca Srivastava, Anuj Barnes, Laura E. |
| author_facet | Kumar, Shashwat Rahman, Arafat Gutierrez, Robert Livermon, Sarah McCrady, Allison N. Blemker, Silvia Scharf, Rebecca Srivastava, Anuj Barnes, Laura E. |
| contents | Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Shape-Based Functional Index for Objective Assessment of Pediatric Motor Function Kumar, Shashwat Rahman, Arafat Gutierrez, Robert Livermon, Sarah McCrady, Allison N. Blemker, Silvia Scharf, Rebecca Srivastava, Anuj Barnes, Laura E. Applications Machine Learning Medical Physics Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders. |
| title | A Shape-Based Functional Index for Objective Assessment of Pediatric Motor Function |
| topic | Applications Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2501.04721 |