Activity recognition on subject independent using machine learning

Abstract

Recent Activity Daily Living (ADL) not only tackles simple activities, but also caters to a wide range of complex activities. Although the same activity has been carried out under the same environmental conditions, the acceleration signal obtained from each subject considerably differs. This happens due to the pattern of action generated for each subject is diverse based on several aspects such as subject age, gender, emotion and personality. This project therefore compares the accuracy of various machine learning models for ADL classification. On top of that, this research work also scrutinizes the effectiveness of various feature selection methods to identify the most relevant attribute for ADL classification. As a result, Random Forest was able to achieve the highest accuracy of 83.3% on subject independent matter in ADL classification. Meanwhile, CFS Subset Evaluator is considered to be a good feature selector as it successfully selected the 8 most relevant features compared with Correlation and Information Gain Evaluator.