Design and Evaluation of an Augmented Reality Posture Guidance System for Marching Exercises in Older Adults: A Pilot Study
Abstract
Incorrect movements during marching exercises among older adults can result in acute injuries and chronic complications, highlighting the need for precise and real-time feedback from exercise specialists to mitigate further risks. This study proposes an automated detection system designed to address these challenges by leveraging a convolutional neural network (CNN)-based pre-trained model integrated with a cosine-based formula algorithm derived from the dot product. The system extracts anatomical movement data from key joints—specifically the hip, knee, and ankle—to calculate joint angles θ, thereby emulating the evaluative capabilities of exercise specialists. Furthermore, augmented reality (AR)-based techniques are incorporated to visualize the results, offering a practical solution to the scarcity of physiotherapists. The proposed approach is experimentally validated through a study involving 90 elderly participants, 30 in controlled laboratory settings and 60 in real-world environments. The system demonstrates robust performance in detecting marching postures across diverse body weight statuses and genders, achieving F-measure scores exceeding 98%. These findings suggest that the system is highly suitable for real-world applications, providing a self-assessment tool that enables older adults to exercise safely and effectively by simulating continuous professional supervision.