Angular velocity and elevation angle: the proposed human model scalable tracking model using linear regression

Abstract

A scalable tracking human model was proposed for recognizing human jogging and walking activities. The model aims to detect and track a particular subject by using wearable sensor. Data collected are in accelerometer readings in three axes and gyroscope readings in three axes. The development of proposed human model is based on the moderating effects on human movements. Two moderators were proposed as the moderating factors of human motion and they are angular velocity and elevation angle. Linear regression is used to investigate the relationship among inputs, moderators and outputs of the model. The result of this study showed that the angular velocity and elevation angle moderators are affecting the relation of research output. Acceleration in x-axis (Ax) and angular velocity in y-axis (Gy) are the two main components in directing a motion. Classification between jogging and walking motions was done by measuring the magnitude of angular velocity and elevation angle. Jogging motion was classified and identified with larger angular velocity and elevation angle. The two proposed hypotheses were supported and proved by research output. The result is expected to be beneficial and able to assist researcher in investigating human motions.