AI Approaches in Gait and Posture Analysis: A Review

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Notice

nThis 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.n

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Year : 2025 [if 2224 equals=””]26/09/2025 at 4:57 PM[/if 2224] | [if 1553 equals=””] Volume : 14 [else] Volume : [/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page :

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    Harshit, Sumit, Dr. Ashish Kumar,

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

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Abstract

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nThis 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 lifenn

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

n[if 424 equals=”Regular Issue”][This article belongs to Research and Reviews : Journal of Computational Biology ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Research and Reviews : Journal of Computational Biology (rrjocb)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nHarshit, Sumit, Dr. Ashish Kumar. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AI Approaches in Gait and Posture Analysis: A Review[/if 2584]. Research and Reviews : Journal of Computational Biology. 26/09/2025; 14(03):-.

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How to cite this URL:
nHarshit, Sumit, Dr. Ashish Kumar. [if 2584 equals=”][226 striphtml=1][else]AI Approaches in Gait and Posture Analysis: A Review[/if 2584]. Research and Reviews : Journal of Computational Biology. 26/09/2025; 14(03):-. Available from: https://journals.stmjournals.com/rrjocb/article=26/09/2025/view=0

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  1. Roggio F, Ravalli S, Maugeri G, et al. Technological advancements in the analysis of human motion and posture management through digital devices. World J Orthop 2021; 12(7): 467-484.
  2. Giannakopoulou KM, Roussaki I, Demestichas K. Internet of things technologies and machine learning methods for parkinson’s disease diagnosis, monitoring and management: a systematic review. Sensors (Basel) 2022; 22(5): 2
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  4. Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson’s disease. Front Aging Neurosci 2023; 15: 4

 

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Volume 14
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 29/05/2025
Accepted 11/09/2025
Published 26/09/2025
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Publication Time 120 Days

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