Hamstring Muscle Activity Characterization Using IMU and sEMG Sensors During Leg Squats

The hamstring muscle is frequently susceptible to injury among athletes across various sports. This paper investigates the use of multidimensional scaling (MDS) to project data points derived from IMU and sEMG data into a lower dimensional space to characterize hamstring muscle activation during leg squats. The research aims to assemble a wearable sensor system for hamstring data acquisition and to characterize the extracted features, including the knee angle, thigh Euler angles, and sEMG signals, analyzing trends and behaviors in the manifold representation. Then, recurrence quantification analysis (RQA) was used to generate recurrence plots, revealing relevant insights into the underlying dynamics, predictability, and complexity of hamstring muscle activity. A t-test on the RQA parameters between rest and active states showed significant differences, indicating distinct behaviors between these states.