 To learn or not to learn? Model-based estimation of motor learning in a large, “in the wild”, home rehabilitation databaseYan Wen, Dongze Ye, Rukshana Poudel, Dan Zondervan, David Reinkensmeyer and Nicolas Schweighofer PLOS Digital Health, 2026, vol. 5, issue 8, 1-22 Abstract: Motor learning, defined as practice-related processes leading to relatively permanent changes in response capability, is critical for regaining motor function following brain injury. However, quantifying learning in real-world, unsupervised settings remains challenging. Here, we present a model-based framework for estimating the presence and extent of motor learning across multiple tasks and users from a large “in-the-wild” rehabilitation dataset collected via the FitMi sensor system. We analyzed 4,661 episodes from 398 users practicing 20 upper-limb tasks for at least 50 sessions. For each episode, we modeled the effect of daily dose (repetitions) on performance (repetitions per second) using a discrete-time first-order state-space model, in which a latent motor memory evolves through cumulative practice. The model employed a learning rate for memory updates and a forgetting time constant for decay. Simulation-based recovery confirmed the robustness of this procedure despite realistic noise and irregular practice schedules. We utilized a likelihood ratio test (LRT) to compare this learning model against a non-learning null model. Overall, 38.3% of episodes were classified as learning. Among these, 44.6%, which correspond to 17.1% of all episodes, exhibited a time constant exceeding 30 days, consistent with durable motor memory. In contrast, non-learning episodes exhibited short time constants (under 2 days), reflecting transient fluctuations. Task-level analysis revealed no clear dichotomy between learnable and non-learnable tasks. Among users practicing at least five tasks, 18% were non-learners and only 5% exhibited learning on all tasks, suggesting that generalizable improvements were rare. Our findings demonstrate that real-world rehabilitation induces detectable motor learning in specific tasks and users. Future research will incorporate user-level covariates, characterize higher-order learning dynamics, and validate findings against clinical outcomes to establish a link with functional recovery. These results offer a path toward optimizing post-stroke recovery through individualized task selection.Author summary: Stroke survivors often require long periods of arm and hand therapy to regain movement. Home rehabilitation with devices like the FitMi enables high doses of practice at a reduced cost. However, it has remained unclear whether such unsupervised practice leads to long-lasting motor skill learning or simply transient performance changes. We analyzed thousands of long-term home-exercise records from nearly 400 FitMi users. By applying a computational model that separates lasting motor memory from temporary improvements, we found that only about one-third of user-task episodes showed clear evidence of motor learning. Furthermore, fewer than one in five users exhibited learning that persisted for more than a month. These findings suggest that sustained motor memory formation during home-based rehabilitation may be less common than previously assumed. Our approach provides a framework for therapists to design personalized motor therapy programs and can guide future adaptive digital health tools that tailor exercises to each individual’s unique learning potential. Date: 2026 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001598 DOI: 10.1371/journal.pdig.0001598 Access Statistics for this article More articles in PLOS Digital Health from Public Library of Science |
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