Abstract

Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human's daily living. In the paper, the benchmark dataset is considered for this work is acquired from the WISDM laboratory, which is available in public domain. We tested experiment using AdaBoost.M1 algorithm with Decision Stump, Hoeffding Tree, Random Tree, J48, Random Forest and REP Tree to classify six activities of daily life by using Weka tool. Then we also see the test output from weka experimenter for these six classifiers. We found the using Adaboost,M1 with Random Forest, J.48 and REP Tree improves overall accuracy. We showed that the difference in accuracy for Random Forest, REP Tree and J48 algorithms compared to Decision Stump, and Hoeffding Tree is statistically significant. We also show that the accuracy of these algorithms compared to Decision Stump, and Hoeffding Tree is high, so we can say that these two algorithms achieved a statistically significantly better result than the Decision Stump, and Hoeffding Tree and Random Tree baseline. Khin Khin Oo "Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28073.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28073/daily-human-activity-recognition-using-adaboost-classifiers-on-wisdm-dataset/khin-khin-oo

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