ArticleNPJ digital medicine2020
Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review.PloS one · 2023Pooled it
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Wearable Assessment of Dynamic Trunk Sway Reveals Directional Balance Adaptations During Sling-Assisted Walking After Stroke.Journal of clinical medicine · 2026Article
- A wearable sensor-based kinematic dataset collected under standardized rehabilitation tasks from 120 post-stroke patients.Scientific data · 2026Article
- Towards routine biomechanical data collection in stroke rehabilitation: a usability comparison of IMU and markerless motion capture systems for functional upper-limb assessments.Journal of neuroengineering and rehabilitation · 2026Article
- Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.Brain sciences · 2026Review
- Transfer Learning With Simulated and Recorded Data Improves Predictions of Lateral Pinch Thumb-Tip Forces.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Advancing Wearable-Based Upper-Limb Stroke Recovery Assessment to the Clinic: A Comparison of Movement Segmentation Strategies.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Beyond the Wrist: Finger-Worn Accelerometers Enhance Assessment of Post-Stroke Motor Performance.Neurorehabilitation and neural repair · 2026Article
- Aggregation Periods Influence Step Count Error in Low-Power Wearables.Sensors (Basel, Switzerland) · 2025Article
- Estimation of Fugl-Meyer Assessment Upper-Extremity Sub-Scores Using a Mixup-Augmented LSTM Autoencoder and Wearable Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- Facilitated Effects of Closed-Loop Assessment and Training on Trans-Radial Prosthesis User Rehabilitation.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- An Open-Source Wearable System for Real-Time Human Biomechanical Analysis.Sensors (Basel, Switzerland) · 2025Article
- Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.Journal of neuroengineering and rehabilitation · 2025Article
- Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.Research (Washington, D.C.) · 2025Review
- Phase-specific multimodal biomarkers enable explainable assessment of upper limb dysfunction in chronic stroke.Frontiers in neuroscience · 2025Article
- High Technology-Assisted Rehabilitation Based on Neuropsychological Assessments in a Case of Severe Acquired Brain Injury.Case reports in neurological medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.