Title :
Classification of hand grasp using perimeter change of the forearm
Author :
Hwiyong Choi ; Sangyoon Lee
Author_Institution :
Dept. of Mech. Design & Production Eng., Konkuk Univ., Seoul, South Korea
Abstract :
This paper presents hand grasp classifier using perimeter change of the forearm. Two sensors based on strain gauge were employed. Signal processing was applied to remove some ripples. Four different classes were trained. Real time classifier was used to recognize the trained grasps. Experimental results show that the average accuracy was 81.2%.
Keywords :
biology computing; biomechanics; sensors; signal classification; strain gauges; forearm; grasp recognition; hand grasp classification; ripple; sensor; signal processing; strain gauge; Accuracy; Bayesian methods; Humans; Muscles; Sensors; Strain; Training; Bayesian classification; Hand grasp classification; Strain gauge; perimeter change of the forearm;
Conference_Titel :
Ubiquitous Robots and Ambient Intelligence (URAI), 2012 9th International Conference on
Conference_Location :
Daejeon
Print_ISBN :
978-1-4673-3111-1
Electronic_ISBN :
978-1-4673-3110-4
DOI :
10.1109/URAI.2012.6463071
