Computational Intelligence Generation of Subject-Specific Knee and Hip Healthy Joint Angles Reference Curves

Backpropagation Neural Network (BNN) and Extreme Learning Machine (ELM) can generate subject-specific joint angle reference profiles based on a subject’s height, weight, age and walking speed. These reference profiles are useful in various fields such as biomechanics and medicine, for detection of gait pathologies and rehabilitation. A common procedure used to identify abnormal gait is comparing an individual’s knee and hip curves against healthy reference curves. These reference curves are usually obtained from a heterogeneous sample of healthy subjects and might lack the specificity required to obtain accurate results. It is why the generation of reference curves according to an individual’s height, weight, age and gait speed shall result in a better comparison and diagnosis. The main objective of the present study is to observe which of the two Computational Intelligence (CI) methods, BNN and ELM, present more accurate results when used to generate reference curve profiles based on subject height, weight, age and gait speed for the knee and hip joint angles.