Indian Journal of Science and Technology

Abstract

Objectives: This study is an endeavor to provide quick, on-the-go classification of a human activity dataset with an aim to improve on the classification time of a machine learning algorithm for Human Activity Recognition (HAR) datasets. Methods/Statistical analysis: It proposes the use of a customized sampler called the Normal On-The-Go (Normal OTG) sampler to reduce the classification time. Concocted using a combination of stratified, random and normal sampling, the Normal OTG sampler was tested on HAR datasets and was found to significantly reduce the training time of the most commonly used machine learning algorithms. Three datasets, ShoaibSA, ShoaibPA and USC-HAD were used to conduct the experiments. Findings: It was found that using as little as 5% samples from the training dataset sampled by the Normal OTG sampler, sufficiently reliable accuracy was obtained from most of the 9 classifiers that were used. The results indicated that almost 96% of time was saved in the training process in the case of USC-HAD, and 62% and 83% time was saved in the case of ShoaibPA and ShoaibSA respectively. It was also found that the results were consistent among the three datasets. Application/Improvements: The study helps training of data in human activity recognition a faster process and thereof, making algorithm selection a less tedious procedure

Keywords: Classification Time, Human Activity Recognition, Robust, Sampling Technique