Validation of a Low-cost Inertial Exercise Tracker

This work validates the application of a low-cost inertial tracking suit, for strength exercise monitoring. The procedure includes an offline processing for body-IMU calibration and an online tracking and identification of lower body motion. We proposed an optimal movement pattern for the body-IMU calibration method from our previous work. Here, in order to reproduce real extreme situations, the focus is on the movements with high acceleration. For such movements, an optimal orientation tracking approach is introduced, which requires no accelerometer measurements and it thus minimizes error due to outliers. The online tracking algorithm is based on an extended Kalman filter(EKF), which estimates the position of upper and lower legs, along with hip and knee joint angles. This method applies the estimated values in the calibration process i.e. joint axes and positions, as well as biomechanical constraints of lower body. Therefore it requires no aiding sensors such as magnetometer. The algorithm is evaluated using optical tracker for two types of exercises: squat and hip abd/adduction which resulted average root mean square error(RMSE) of 9cm. Additionally, this work presents a personalized exercise identification approach, where an online template matching algorithm is applied and optimised using zero velocity crossing(ZVC) for feature extraction. This results reducing the execution time to 93% and improving the accuracy up to 33%.