Analysis and Prediction of Movement Patterns Based on Artificial Intelligence with a Health Behavior Perspective: Analysis and prediction of movement patterns

Maedeh Ahmadpour; Mahdi Najafian-Razavi; Hediye Yıldırım Ogan; Armin Farokhi; Elnaz Ebrahimi-Khayat; Maryam Sadeghi

doi:10.22122/ijbmc.v11i5.829

Authors

Maedeh Ahmadpour
maedeh.ahmadpour@ut.ac.ir
PhD Student, Department of Behavioral and Cognitive Sciences in Sport, University of Tehran, Tehran, Iran, Iran, Islamic Republic of
Mahdi Najafian-Razavi Assistant Professor, Department of Physical Education and Sport Sciences, Mashhad Branch, Islamic Azad University, Mashhad, Iran, Iran, Islamic Republic of
Hediye Yıldırım Ogan Department of Physical Education and Sports, Health Sciences Institute, Malatya, Turkey, Turkey
Armin Farokhi PhD Student of Sport Psychology, Arak Branch, Islamic Azad University, Arak, Iran, Iran, Islamic Republic of
Elnaz Ebrahimi-Khayat MSc of Motor Learning, Mashhad Branch, Islamic Azad University, Mashhad, Iran, Iran, Islamic Republic of
Maryam Sadeghi Assistant Professor, Department of Psychology, Mashhad Branch, Islamic Azad University, Mashhad, Iran, Iran, Islamic Republic of

Vol 11, No 5 (2024)

Review Article(s)

Accepted October 23, 2024

Published October 23, 2024

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Background: Movement pattern analysis is fundamental in fields such as sports, healthcare, robotics, and surveillance, providing critical insights into human and robotic motion. Traditional methods often struggle with the complexity and volume of movement data, limiting their effectiveness. Recently, integrating health behavior analysis into AI-driven movement analysis has further enhanced its application, particularly in preventive healthcare and rehabilitation. This study aims to review AI techniques employed in analyzing and predicting movement patterns, with an added focus on health behavior perspectives.

Methods: A comprehensive narrative review was conducted, focusing on literature published between 2000 and 2024. Electronic databases including PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar were searched using keywords related to AI, movement pattern analysis, and health behaviors. Studies were included if they discussed the application of AI techniques in movement and health behavior analysis across domains such as sports, healthcare, robotics, and surveillance. Data extraction centered on the AI methods used, application areas, findings, and identified challenges.

Results: AI techniques, particularly machine learning and deep learning models such as convolutional neural networks and recurrent neural networks, have significantly advanced movement pattern analysis. In sports analytics, AI enhances athlete performance and injury prevention by analyzing complex movement data. In healthcare, AI contributes to rehabilitation, prosthetic development, chronic disease management, and patient monitoring through precise movement and health behavior interpretation. AI also aids in predicting health behaviors, enabling personalized interventions to improve physical activity, adherence to rehabilitation protocols, and chronic disease management.

Conclusion: Artificial intelligence has revolutionized both movement pattern analysis and
health behavior prediction, offering transformative capabilities across various fields. Addressing technical and ethical challenges is essential for future advancements. Emerging technologies like hybrid models, transfer learning, and personalized AI systems offer promising directions for enhancing AI applications. Interdisciplinary collaboration will further shape the future landscape of movement and health behavior analysis, improving outcomes in healthcare, sports, robotics, and beyond.

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Ahmadpour M, Najafian-Razavi M, Ogan HY, Farokhi A, Ebrahimi-Khayat E, Sadeghi M. Analysis and Prediction of Movement Patterns Based on Artificial Intelligence with a Health Behavior Perspective: Analysis and prediction of movement patterns. Int J Body Mind Cult. 11(5):489–513.

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Analysis and Prediction of Movement Patterns Based on Artificial Intelligence with a Health Behavior Perspective