Development of Proposed Bi-Modal Human Motion Classification System

Abstract

The main objective of this study is to discover and investigate greater levels of human motion activities recognition. The study presents four approaches of human motion data processing to recognize the human activities. Data collection process was performed in two ways: wearable sensor based in signal data and vision based in image data. The proposed approaches used to analyze the signal and image data are: wearable sensor using 3-space sensing with angular velocity and elevation angle as moderators, wearable sensor using statistical nine existing and a proposed developed classifiers as classification learning system, vision based using skeletonization with humerus-radius and horizontal-radius as measuring angle and vision based image-signal histogram using 2D-1D transformation method. The principal contributions of this thesis are the development of the human motion analysis methods with validated evaluation process tested on the proposed systems. The proposed systems achieved more than 98 % for signal processing and 97 % for image processing of accuracy on recognizing human activities.