Design of an Artificial Vision System for Exercise Monitoring in Healthy Aging

This study presents the design and pilot evaluation of a computer vision–based system for monitoring exercise performance in older adults, aiming to reduce frailty-related risks without the need for wearable sensors. Using MediaPipe and OpenCV, the system tracks posture and movement in real time and provides feedback on exercise execution. A pilot test was conducted with 14 volunteers performing seven exercises from the Vivifrail Spanish program (Wheel A). Performance was evaluated using perfomance analysis, yielding recognition rates between 91.06\% and 100\% across exercises. While the system showed high accuracy in detecting posture and repetitions, challenges such as camera positioning, clothing variability, and the absence of validation in the target population remain. These findings demonstrate the feasibility of computer vision for exercise monitoring and support its potential as an accessible tool for fall prevention and functional assessment in older adults. Future work will focus on clinical validation and integration into mobile platforms for home based use. This approach will allow older population to adequately perform exercises from the Vivifrail program, while professionals, as physiotherapists and geriatricians, can monitor their progress remotely, adjusting the program when needed.