EMG-BASED SYSTEM FOR BASIC HAND MOVEMENT RECOGNITION

this paper presents a system for the automatic identification of six basic hand movements in healthy subjects based on a steady-state of electromyographic signals. the following basic hand motions were detected: opening, closing, flexion, extension, pronation, and supination, as well as the rest condition. a modular approach of pattern recognition with discrete wavelet transform, principal component analysis, and support vector machines was used to discriminate each movement. identification was completed off-line every 256 ms with a hardware-software interface composed of a signal acquisition system with two electromyographic differential channels using matlab？ and labview？ software. the system was trained and tested using five subjects of different gender, age, and physical complexion, with identification rates of up to 99.25 %.