An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics. (27th December 2006)

• Record Type: Journal Article
• Authors: Parvini, Farid; Shahabi, Cyrus
• Is Part Of: International journal of bioinformatics research and applications. Volume 3:Number 1(2007)
• Page Start: 4
• Page End: 23
• Publication Date: 2006-12-27
• Subjects: multi-stream human sensor data -- sensor data analysis -- gesture recognition -- static gestures -- dynamic gestures -- user-independent -- device-independent -- range of motion -- American Sign Language -- ASL -- bioinformatics -- mechanics -- biomechanics -- biomedical data -- biomedical engineering; Bioinformatics -- Periodicals; 570.285
• Languages: English
• ISSNs: 1744-5485

Abstract

We propose a novel approach for recognising static and dynamic hand gestures by analysing the raw data streams generated by the sensors attached to the human hands. We utilise the concept of 'range of motion' in the movement of fingers and exploit this characteristic to analyse the acquired data for recognising hand signs. Our approach for hand gesture recognition addresses two major problems: user-dependency and device-dependency. Furthermore, we show that our approach neither requires calibration nor involves training. We apply our approach for recognising American Sign Language (ASL) signs and show that more than 75% accuracy in sign recognition can be achieved.