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 %.

• Autores: Camacho Navarro, Jhonatan; León Vargas, Fabian; Barrero Pérez, Jaime
• Tipo de recurso: Article of journal
• Fecha de publicación: 2012
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/40593
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/40593; http://bdigital.unal.edu.co/30690/
• Palabra clave: Electromyography; hand-prosthesis; pattern recognition; principal component analysis; discrete wavelet transform; support vector machines
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional