ReviewCurrent neurology and neuroscience reports2020
Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation.
Review in Current neurology and neuroscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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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.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies.Journal of neurology · 2026Article
- A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults.Scientific data · 2025Article
- The effect of depth data and upper limb impairment on lightweight monocular RGB human pose estimation models.Biomedical engineering online · 2025Article
- Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.Biomedicines · 2024Review
- Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.Thoracic cancer · 2024Article
- Predicting Tissue Loads in Running from Inertial Measurement Units.Sensors (Basel, Switzerland) · 2023Article
- 3D Analysis of Human Movement, Sport, and Health Promotion.Journal of functional morphology and kinesiology · 2023Article
- Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data.Bioengineering (Basel, Switzerland) · 2023Article
- The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review.Disability and rehabilitation · 2022Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- 2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings.Sensors (Basel, Switzerland) · 2022Article
- Applications of Pose Estimation in Human Health and Performance across the Lifespan.Sensors (Basel, Switzerland) · 2021Review
- Editorial: Machine Learning Approaches to Human Movement Analysis.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
purpose of reviewRecent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in the motor rehabilitation field, they have great potential. In this review, I describe how the current state of machine learning can be applied to 3D kinematic analysis, and how this will have an impact on neurorehabilitation. RECENT
findingsApplications of deep learning methods, in the form of convolutional neural networks, have been revolutionary for image analysis such as face recognition and object detection in images, exceeding human level performance. Recent studies have shown applicability of these deep learning approaches to human posture and movement classification. It is to be expected that portable stereo-camera systems will bring 3D pose estimation into the clinical setting and allow the assessment of movement quality in response to interventions. Advances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement.
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Registered trials
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