ArticlePLOS digital health2022
PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- The promise and peril of AI for disability health equity: A justice-centered narrative review.PLOS digital health · 2026Review
- Vision-language models for human motion understanding: Lessons from stroke rehabilitation.PLOS digital health · 2026Article
- Article
- Quantifying impairment and disease severity using AI models trained on healthy subjects.NPJ digital medicine · 2024Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- StrokeRehab: A Benchmark Dataset for Sub-second Action Identification.Advances in neural information processing systems · 2022Article
- Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides.Sensors (Basel, Switzerland) · 2022Article
- Comparing algorithms for assessing upper limb use with inertial measurement units.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.
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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.