Using Foot and Knee Movement and Posture Information to Mitigate the Probability of Injuries in Functional Training

摘要

Foot and Knee pain have been associated with numerous orthopedic pathologies and injuries of the lower limbs. From street running to CrossFit functional training, these common injuries correlate highly with unevenly distributed plantar pressure and knee positioning during long-term physical practice and can lead to severe orthopedic injuries if the movement pattern is not amended. Therefore, the monitoring of foot plantar pressure distribution and the spatial and temporal characteristics of foot and knee positioning abnormalities is of utmost importance for injury prevention. This work proposes a wearable platform to provide real-time feedback of functional exercises, aiming to help users and physical educators to mitigate the probability of injuries during training. We conducted an experiment with 12 diverse volunteers to build a Human Activity Recognition (HAR) classifier that achieved about 87% overall classification accuracy, and a second experiment to validate our physical evaluation model. Finally, we performed a semi-structured interview to evaluate usability and user experience issues regarding the proposed platform.