Artificial Intelligence in Gait Analysis and Rehabilitation
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 155
Special Issue Editors
Interests: cognitive aging and Alzheimer’s disease and related dementias (ADRD); geriatric oral health and oral–systemic links to dementia; social epidemiology of late-life depression, social isolation, and loneliness; dementia caregiving in Asian American families; health disparities in cardiometabolic, mental, and cognitive health; data science and advanced quantitative methods (machine learning, causal inference, large-scale population health data)
Special Issues, Collections and Topics in MDPI journals
Interests: sports biomechanics; gait analysis; rehabilitation biomechanics; artificial intelligence; machine learning; human movement analysis; wearable sensor technology; surface electromyography; lower-limb biomechanics; musculoskeletal modeling; injury prevention
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is rapidly transforming gait analysis and gait-related rehabilitation by enabling automated movement assessment, individualized risk prediction, and data-driven clinical decision-making. This Special Issue, “Artificial Intelligence in Gait Analysis and Rehabilitation”, focuses on AI applications in walking, locomotion, mobility assessment, and lower-limb functional recovery. It aims to bring together interdisciplinary research at the intersection of biomechanics, biomedical engineering, rehabilitation science, sports medicine, and computational modeling. We welcome original research articles, reviews, technical notes, and methodological studies that develop, validate, or apply AI-based approaches for gait assessment, abnormal gait detection, movement disorder classification, fall-risk prediction, rehabilitation monitoring, prosthetics and orthotics optimization, wearable-sensor analytics, computer vision, digital biomarkers, and personalized gait rehabilitation. Contributions may include machine learning, deep learning, explainable AI, multimodal data fusion, musculoskeletal modeling, virtual/augmented reality, robotics-assisted gait training, and clinical decision-support systems. Particular emphasis will be placed on studies demonstrating methodological rigor, clinical interpretability, external validation, and translational relevance for improving mobility and rehabilitation outcomes across healthy, aging, athletic, and clinical populations.
Dr. Xiang Qi
Guest Editor
Dr. Wanli Zang
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- artificial intelligence
- gait analysis
- gait rehabilitation
- mobility assessment
- lower-limb rehabilitation
- biomechanics
- machine learning
- deep learning
- explainable AI
- wearable sensors
- motion capture
- computer vision
- digital biomarkers
- musculoskeletal modeling
- abnormal gait detection
- movement disorders
- fall-risk prediction
- prosthetics and orthotics
- robotic gait training
- clinical decision support
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