Knee Injured Recovery Analysis Using Extreme Learning Machine

Abstract

The physiotherapists analyse gait patterns to recognize normal and pathological gait movements. The gait patterns are affected by the characteristics of the individual (gender, age, weight and height) and the walking speed. In this paper, a gait analysis system to evaluate the severity of gait pathology is proposed. The Machine Learning (ML) algorithm can generate reference knee patterns for specific individuals. Gait index are used to compare the patterns generated by the ELM and patterns of the patients who suffered a surgical knee reconstruction. Two gait index are compared: The Gait Variable Score (GVS) and the Global Index (GIndex) developed by the authors. The GIndex classified 7 patients as not recovery, corroborating with the opinion of physiotherapists, while the GVS only classified 2 as not recovered. The proposed gait analysis system using the Extreme Learning Machine (ELM) and the GIndex can be useful tool for physiotherapy team in the gait pathology diagnosis and evaluation of future pathologies.