Compact time-independent pattern representation of entire human gait cycle for tracking of gait irregularities

Comparative performance of machine learning models for the classification of human gait

The efficacy of human activity recognition (HAR) models mostly relies on the characteristics derived from domain expertise. The input of the classification algorithm consists of many characteristics that are utilized to accurately and effectively classify human physical activities. In contemporary research, machine learning techniques have been increasingly employed to automatically extract characteristics from unprocessed sensory input to develop models for Human Activity Recognition (HAR) and classify various activities. The primary objective of this research is to compare and contrast several machine learning models and determine a reliable and precise classification model for classifying activities. This study does a comparison analysis in order to assess the efficacy of 10 distinct machine learning models using frequently used datasets in the field of HAR. In this work, three benchmark public human walking datasets are being used. The research is conducted based on eight evaluating parameters. Based on the study conducted, it was seen that the machine learning classification models Random Forest, Extra Tree, and Light Gradient Boosting Machine had superior performance in all the eight evaluating parameters compared to specific datasets. Consequently, it can be inferred that machine learning significantly enhances performance within the area of Human Activity Recognition (HAR). This study can be utilized to provide suitable model selection for HAR-based datasets. Furthermore, this research can be utilized to facilitate the identification of various walking patterns for bipedal robotic systems.