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


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.


. Khan, Adil Mehmood and Lee, Young-Koo and Lee, Sungyoung Y and Kim, Tae-Seong [2010] A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer, Information Technology in Biomedicine, IEEE Transactions;14:5–1166.

. Zhao, K.; Du, J.; Li, C.; Zhang, C.; Liu, H.; Xu, C. Healthy: A Diary System Based on Activity Recognition Using Smartphone. In Proceeedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), Hangzhou, China, 14–16 October 2013; pp. 290–294.

. Oscar. D. Lara and M. A. Labrador, "A Survey on Human Activity Recognition using Wearable Sensors," in IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1192-1209, Third Quarter 2013.

. J. W. Lockhart, T. Pulickal, and G. M. Weiss, “Applications of mobile activity recognition,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp ’12, 2012, p. 1054.

. S. Gallagher, “Smartphone Sensor Data Mining for Gait Abnormality Detection,” Fordham University, New York, 2014.

. T. van Kasteren, G. Englebienne, and B. Krse, “An activity monitoring system for elderly care using generative and discriminative models,” J. Personal and Ubiquitous Computing, 2010.

. Slim S.O., Atia A., Mostafa MS.M. (2016) An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition. In The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham.

. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea, “Machine recognition of human activities: A survey,” IEEE Trans. Circuits Syst.Video Technol., vol. 18, no. 11, pp. 1473–1488, 2008.

. J. Candamo, M. Shreve, D. Goldgof, D. Sapper, and R. Kasturi, “Understanding transit scenes: A survey on human behavior-recognition algorithms,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 1, pp. 206–224, 2010.

. M. Ahad, J. Tan, H. Kim, and S. Ishikawa, “Human activity recognition: Various paradigms,” in International Conference on Control, Automation and Systems, pp. 1896–1901, 2008.

. E. Kim, S. Helal, and D. Cook, “Human activity recognition and pattern discovery,” IEEE Pervasive Computing, vol. 9, no. 1, pp. 48–53, 2010.

. Jun Yang. 2009. Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics (IMCE '09). ACM, New York, NY, USA, 1-10.

. Shoaib, M.; Bosch, S.; Incel, O.D.; Scholten, H.; Havinga, P.J. "Fusion of smartphone motion sensors for physical activity recognition". Sensors 2014, 14, 10146–10176.

. N P. Kumari, M. López-Benítez, G. M. Lee, T. S. Kim and A. S. Minhas, "Wearable Internet of Things from human activity tracking to clinical integration," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, 2017, pp. 2361-2364.


. J. R. Kwapisz, G. M. Weiss, and S. a. Moore, “Activity recognition using cell phone accelerometers,” ACM SIGKDD Explor. Newsl., vol. 12, no. 2, p. 74, 2011




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