Artificial Neural Network to Predict Swinging of Lower Limb in Jumping Jack Exercise

This study focuses on the kinematic assessment of lower limb angles during jumping jack exercise. The jumping jack is the most common exercise performed by many induvial. The efficacy of maintaining the kinematics of the lower limb during the exercise determines how effective the exercise is conducted. It is also helpful for people to understand the deviation in lower limb exercises during the repeated exercise cycle and perform them correctly. This article addresses the variation of the kinematics of the lower limb during the jumping jack exercise. A subject for 5 minutes performs the exercise. The postural difference at the exercise’s start and the end are measured using the motion capture system. Further analysis using MATLAB angles is obtained. Also, Artificial Neural Network (ANN) model is derived to predict the lower limb angles for the subject. The input for the ANN model is the muscle activation obtained from EMG and angular acceleration of the lower limb. The analysed ANN model well predicts the lower limb kinematics. From this research, the muscle activation and the angular acceleration, one can determine the efficacy of the exercise using the ANN model.