Activity detection and analysis on Android - Ong, Xian Hui (2015)
Tracking and learning the activity patterns of an individual is vital when providing healthcare and awareness to the needy such as the elderly or healthcare patients. In this project, unobtrusive detection of the person activities in outdoor environment is implemented through an Android smartphone device. Common activities such as Running, Standing, Walking, Falling down as well as Climbing up and down are carried out. Features extracted from the raw inertial sensor data are first collected from the mobile device and subsequently used to build classification models using different machine learning algorithms in WEKA Analyzing Tool.