Human wrist motion classification using decision tree and principle component analysis

Abstract

Abstract This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. Both methods were developed in MATLAB platform by managing and analyzing motion data in graphical user interface (GUI) form. By attaching the wearable sensors to each segments of interest, the collected data can be reconstructed in real time basis. Artificial intelligence technique is proposed to classify the subjects’ motions. Data reconstruction technique can be used to regenerate the motion of subjects’ limbs movement in 2D representation. Corresponding tests are performed to validate the accuracy of the application. The application is potentially to be used to access the quality of human motion in hospitals, clinics and human motion research. An experiment was set up in a laboratory environment for data collecting. Subjects were asked to perform three shape drawings using left and right hands. The instruments demonstrate adequate internal consistency of optimum average accuracy of 52 % for decision tree and 80 % for principle component analysis. Results were presented in tabular, image and graphical forms. The expected output of the study can be used to estimate joint angles in predicting Parkinson Disease for early stage symptoms, to track the moving behaviour in clinical gait analysis, to analyse limb movement in detecting resting tremor, to trace wrist motion in regenerating children handwriting difficulty, and to measure joint motion in evaluating patient rehabilitation progress.