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Performance Evaluation of Classifiers on WISDM Dataset for Human Activity Recognition
Abstract and Figures
Mobile Phone used not to be matter luxury only, it has become a significant need for rapidly evolving fast track world. In this paper, we evaluate the performance of a various machine learning classifiers on WISDM human activity recognition dataset which is available in public domain. We show that while keeping smartphone in pocket, it is very easy to recognize activity of daily living with the help of built-in sensors. We further demonstrated that by using a proper classifier, recognition rate can improve in most of the activities more than 96%. The experiments were performed by other researcher using Multilayer Perceptron classifier (MLP) and random forest (RF) classifier. They were received 91.7% and 75.9% of overall accuracy with MLP and RF on impersonal data respectively. Our results are much better with overall accuracy of 98.09% using random forest classifier. In addition, these activities are recognized quickly.
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