Comparing the Performance of Machine Learning Algorithms for Human Activities Recognition using WISDM Dataset

  • Ya Min University of Computer Studies, Lashio, Myanmar
  • Yin Yin Htay University of Computer Studies, Lashio, Myanmar
  • Khin Khin Oo University of Computer Studies, Magway, Myanmar
Keywords: Human activity recognition, machine learning, data mining, tree based classifier, rule based classifier, accuracy


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. Mobile phones are used to be more than luxury products, it has become a kind of urgent need for a fast-moving world with rapid development. 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 this paper, we tested experiment using Tree based Classifiers (Decision Tree, J48, JRIP, and Random Forest) and Rule based algorithms Classifiers (Naive Bayes and AD1) to classify six activities of daily life by using Weka tool. According to the tested results Random Forest classifier is more accurate than other classifiers.


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How to Cite
Min, Y., Htay, Y. Y., & Oo, K. K. (2020). Comparing the Performance of Machine Learning Algorithms for Human Activities Recognition using WISDM Dataset. International Journal of Computer (IJC), 38(1), 61-72. Retrieved from