Aggregated Activity Recognition Using Smart Devices

Abstract

Activity recognition has become of great importance in many fields especially in fitness monitoring, health and elder care by offering the opportunity for large amount of applications which recognize human's daily life activities. Human activity recognition (HAR) was not only limited on health care field or monitoring sports, but it also started to emerge in the religious branch and monitor people behavior while performing their religious activity like praying. The prevalence of smart phones in our society with their ever growing sensing power has opened the door for more sophisticated data mining applications which takes the raw sensor data as input and classify the motion activity performed. The main sensor used in performing activity recognition is the accelerometer. This paper presents a framework for activity recognition using smart phone sensors to recognize simple daily activities and then aggregate these simple activities (walking, standing, sitting,…) to recognize a more complex one which is prayer. Features extracted from raw sensor data are used to train and test supervised machine learning algorithms.