On the use of wearable sensors to enhance motion intention detection for a contralaterally controlled FES system

• Published in: Proceedings (International Conference on Wearable and Implantable Body Sensor Networks : Print) pp. 324 - 328
• Main Author: Ruiz-Olaya, Andres F.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Algorithms; Classification; Classification algorithms; Control systems; Electromyography; Feature extraction; Medical treatment; Movements; Muscles; Networks; Pattern recognition; Sensors; Therapy; Wearable
• ISSN: 2376-8894
• DOI: 10.1109/BSN.2016.7516282

During the last years, there has been a relevant progress in motor learning and functional recovery after the occurrence of a brain lesion. Rehabilitation of motor function has been associated to motor learning that occurs during repetitive, frequent and intensive training. Contralaterally controlled functional electrical stimulation (CCFES) is a new therapy designed to improve the recovery of paretic limbs after stroke, that could provide repetitive training-based therapies and has been developed to control the upper and lower limbs movements in response to user's intentionality. Electromyography (EMG) signals reflect directly the human motion intention, so it can be used as input information to control a CCFES system. Implementation of the EMG-based pattern recognition is not easy to be accomplished due to some difficulties, among them that the activity level of each muscle for a certain motion is different between each person. Inertial Measurement Units (IMU) is a kind of wearable sensors that are used to gather movement data. IMUs could provide valuable kinematic information in an EMG-based pattern recognition process to improve classification. This work describes the use of IMUS to improve detecting motion intention from EMG data. Results shows that myoelectric algorithm using information from IMUs was better in classification of seven movements at the upper-limb level that algorithm using only EMG data.