AI Approaches in Gait and Posture Analysis: A Review

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 14 | 03 | Page :
    By

    Harshit,

  • Sumit,,

  • Dr. Ashish Kumar,

  1. Undergraduate Student, Guru Kashi University, Talwandi Sabo, Bathinda,, Punjab, India
  2. Undergraduate Student, Guru Kashi University, Talwandi Sabo, Bathinda,, Punjab, India
  3. Assistant Professor,, Guru Kashi University, Talwandi Sabo, Bathinda,, Punjab, India

Abstract

This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensor-derived data, offering objective and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with quantitative data from wearable sensors, motion capture systems, or depth cameras. The review highlights the strengths of AI, including its high accuracy and capacity to process large datasets, while acknowledging limitations such as data heterogeneity, interpretability challenges, and generalization issues. Results indicate that ML algorithms like SVM, RF, and KNN, and DL models like CNN and RNN, are effectively employed for gait and posture classification, parameter estimation, and clinical applications such as Parkinson’s disease monitoring, scoliosis screening, and fall risk assessment. Future research directions emphasize addressing data heterogeneity, developing real-time analysis capabilities, enhancing explainable AI, and integrating personalized analysis into clinical workflows. The integration of AI into clinical practice holds the potential to revolutionize the diagnosis and management of musculoskeletal and neurological disorders, improving patient outcomes and quality of life.

Keywords: Gait, Posture, AI, Machine Learning, Deep Learning

How to cite this article:
Harshit, Sumit,, Dr. Ashish Kumar. AI Approaches in Gait and Posture Analysis: A Review. Research and Reviews : Journal of Computational Biology. 2025; 14(03):-.
How to cite this URL:
Harshit, Sumit,, Dr. Ashish Kumar. AI Approaches in Gait and Posture Analysis: A Review. Research and Reviews : Journal of Computational Biology. 2025; 14(03):-. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=228135


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Ahead of Print Subscription Review Article
Volume 14
03
Received 29/05/2025
Accepted 11/09/2025
Published 26/09/2025
Publication Time 120 Days
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