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Abstract
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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.
Language: en |