SynthesisJournal of neuroengineering and rehabilitation2017
Markerless motion capture systems as training device in neurological rehabilitation: a systematic review of their use, application, target population and efficacy.
Synthesis in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06667115 (The Effectiveness of Proprioceptive Neuromuscular Facilitation Exercise With Virtual Reality Motion Capture Gaming System and Concurrent Feedback on Early Shoulder Muscle Activation in Healthy Individuals), which is not on this map. Cited by 61 papers, 7 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.
The Effectiveness of Proprioceptive Neuromuscular Facilitation Exercise With Virtual Reality Motion Capture Gaming System and Concurrent Feedback on Early Shoulder Muscle Activation in Healthy Individuals
Who cites it
61 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review.JMIR aging · 2024Pooled it
- Clinical assessment of upper limb impairments and functional capacity in Parkinson's disease: a systematic review.Arquivos de neuro-psiquiatria · 2023Pooled it
- Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.Journal of neuroengineering and rehabilitation · 2023Pooled it
- The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis.Sensors (Basel, Switzerland) · 2021Pooled it
- Recent advances in rehabilitation for Parkinson's Disease with Exergames: A Systematic Review.Journal of neuroengineering and rehabilitation · 2019Pooled it
- Feasibility and Efficacy of a Virtual Reality Game-Based Upper Extremity Motor Function Rehabilitation Therapy in Patients with Chronic Stroke: A Pilot Study.International journal of environmental research and public health · 2022Trial
- Efficacy of a technology-based client-centred training system in neurological rehabilitation: a randomised controlled trial.Journal of neuroengineering and rehabilitation · 2021Trial
- Effects of virtual reality associated with serious games for upper limb rehabilitation inpatients with multiple sclerosis: randomized controlled trial.Journal of neuroengineering and rehabilitation · 2020Trial
- Feasibility, Acceptability, and Clinical Effectiveness of a Technology-Enabled Cardiac Rehabilitation Platform (Physical Activity Toward Health-I): Randomized Controlled Trial.Journal of medical Internet research · 2020Trial
- Kinect-based assessment of proximal arm non-use after a stroke.Journal of neuroengineering and rehabilitation · 2018Trial
- Neural Network-Driven Finite Element Modeling for Estimating Knee Joint Cartilage Mechanical Responses.Annals of biomedical engineering · 2026Article
- From Optical to AI-Driven Markerless Motion Capture in Motor Learning and Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Review
- Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review.Pediatric reports · 2026Review
- The Concurrent Validity and Test-Retest Reliability of a Smartphone-Based Markerless System.Sensors (Basel, Switzerland) · 2026Article
- The Clinician's Guide to Computer Vision in the Motion Capture Galaxy.Restorative neurology and neuroscience · 2026Article
- Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises.Journal of neuroengineering and rehabilitation · 2025Article
- Combined hamstrings lengthening and patellar tendon retensioning for neurogenic knee flexion in spastic diplegia.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025Article
- Effectiveness of intelligent robotic-assisted training system combined with repetitive facilitative exercise on upper limb motor function after stroke: a randomized controlled trial.BMC sports science, medicine & rehabilitation · 2025Article
1 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClient-centred task-oriented training is important in neurological rehabilitation but is time consuming and costly in clinical practice. The use of technology, especially motion capture systems (MCS) which are low cost and easy to apply in clinical practice, may be used to support this kind of training, but knowledge and evidence of their use for training is scarce. The present review aims to investigate 1) which motion capture systems are used as training devices in neurological rehabilitation, 2) how they are applied, 3) in which target population, 4) what the content of the training and 5) efficacy of training with MCS is.
methodsA computerised systematic literature review was conducted in four databases (PubMed, Cinahl, Cochrane Database and IEEE). The following MeSH terms and key words were used: Motion, Movement, Detection, Capture, Kinect, Rehabilitation, Nervous System Diseases, Multiple Sclerosis, Stroke, Spinal Cord, Parkinson Disease, Cerebral Palsy and Traumatic Brain Injury. The Van Tulder's Quality assessment was used to score the methodological quality of the selected studies. The descriptive analysis is reported by MCS, target population, training parameters and training efficacy.
resultsEighteen studies were selected (mean Van Tulder score = 8.06 ± 3.67). Based on methodological quality, six studies were selected for analysis of training efficacy. Most commonly used MCS was Microsoft Kinect, training was mostly conducted in upper limb stroke rehabilitation. Training programs varied in intensity, frequency and content. None of the studies reported an individualised training program based on client-centred approach.
conclusionMotion capture systems are training devices with potential in neurological rehabilitation to increase the motivation during training and may assist improvement on one or more International Classification of Functioning, Disability and Health (ICF) levels. Although client-centred task-oriented training is important in neurological rehabilitation, the client-centred approach was not included. Future technological developments should take up the challenge to combine MCS with the principles of a client-centred task-oriented approach and prove efficacy using randomised controlled trials with long-term follow-up.
trial registrationProspero registration number 42016035582 .
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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.