A Comparison Study of Classifier Algorithms for Mobile-phone's Accelerometer Based Activity Recognition

Nowadays, many mobile phones have been equipped with sensors to enable the delivery of advanced features/services to the users. Accelerometer is one of the sensors that embedded to several types of mobile phones. Our earlier research has shown that data from mobile-phone embedded accelerometer can be used for activity recognition purpose [1]. As a continuation of the research towards the search for a suitable and reliable algorithm for real-time activity recognition using mobile phone, an evaluation and comparison study of the performance of seven different categories of classifier algorithms in classifying user activities were conducted. Five basic human activities (jogging, jumping, sitting, standing, and walking) were tested. The training and testing data were done using Weka 3.6.6 data mining tool. The overall accuracy rate for classifier training managed to exceed 96% and exceeded 90% for classifier testing, which are very encouraging results.