ArticleJournal of neuroengineering and rehabilitation2022
A low-cost virtual coach for 2D video-based compensation assessment of upper extremity rehabilitation exercises.
Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy.Sensors (Basel, Switzerland) · 2024Pooled it
- An Accessible AI-Assisted Rehabilitation System for Guided Upper Limb Therapy.Sensors (Basel, Switzerland) · 2025Article
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Characterization of bilateral reaching development using augmented reality games.Human movement science · 2024Article
- MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
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Abstract
backgroundThe increasing demands concerning stroke rehabilitation and in-home exercise promotion grew the need for affordable and accessible assistive systems to promote patients' compliance in therapy. These assistive systems require quantitative methods to assess patients' quality of movement and provide feedback on their performance. However, state-of-the-art quantitative assessment approaches require expensive motion-capture devices, which might be a barrier to the development of low-cost systems.
methodsIn this work, we develop a low-cost virtual coach (VC) that requires only a laptop with a webcam to monitor three upper extremity rehabilitation exercises and provide real-time visual and audio feedback on compensatory motion patterns exclusively from image 2D positional data analysis. To assess compensation patterns quantitatively, we propose a Rule-based (RB) and a Neural Network (NN) based approaches. Using the dataset of 15 post-stroke patients, we evaluated these methods with Leave-One-Subject-Out (LOSO) and Leave-One-Exercise-Out (LOEO) cross-validation and the [Formula: see text] score that measures the accuracy (geometric mean of precision and recall) of a model to assess compensation motions. In addition, we conducted a pilot study with seven volunteers to evaluate system performance and usability.
resultsFor exercise 1, the RB approach assessed four compensation patterns with a [Formula: see text] score of [Formula: see text]. For exercises 2 and 3, the NN-based approach achieved a [Formula: see text] score of [Formula: see text] and [Formula: see text], respectively. Concerning the user study, they found that the system is enjoyable (hedonic value of 4.54/5) and relevant (utilitarian value of 4.86/5) for rehabilitation administration. Additionally, volunteers' enjoyment and interest (Hedonic value perception) were correlated with their perceived VC performance ([Formula: see text]).
conclusionsThe VC performs analysis on 2D videos from a built-in webcam of a laptop and accurately identifies compensatory movement patterns to provide corrective feedback. In addition, we discuss some findings concerning system performance and usability.
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