ArticleJournal of rehabilitation and assistive technologies engineering
Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms.Sensors (Basel, Switzerland) · 2025Article
- Normative isokinetic knee strength values and prediction models in non-athletic Chinese adults.Frontiers in bioengineering and biotechnology · 2025Article
- Intervention Effect of Rehabilitation Robotic Bed Under Machine Learning Combined With Intensive Motor Training on Stroke Patients With Hemiplegia.Frontiers in neurorobotics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionStroke is the leading cause of disability worldwide. It has been well-documented that rehabilitation (rehab) therapy can aid in regaining health and function for individuals with stroke. Yet, tracking in-home rehab continues to be a challenge because of a lack of resources and population-scale demands. In order to address this gap, we implemented a methodology to classify and track rehab interventions in individuals with stroke.
methodsWe developed personalized classification algorithms, including neural network-based algorithms, to classify four rehab exercises performed by two individuals with stroke who were part of a week-long therapy camp in Jamaica, a low- and middle-income country. Accelerometry-based wearable sensors were placed on each upper and lower limb to collect movement data during therapy.
resultsThe classification accuracy for traditional and neural network-based algorithms utilizing feature data (e.g., number of peaks) from the sensors ranged from 64 to 94%, respectively. In addition, the study proposes a new method to assess change in bilateral mobility over the camp duration.
conclusionThe results of this pilot study indicate that personalized supervised learning algorithms can be used to classify and track rehab activities and functional outcomes in resource limited settings such as LMICs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.