Abstract:
Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems ...Show MoreMetadata
Abstract:
Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems typically do not include interaction with the environment, which naturally occurs during functional tasks. Further, many applications have experts place the sEMG sensors on specific muscles, which benefits precision alignment that may not be possible by non-experts. This study informs algorithm development for controlling assistive devices for grasping and releasing objects using kinematics and non-specifically placed sEMG sensors. Significant effects of object type were found in the grip aperture and joint kinematics. Muscle activity was significantly affected by small alignment changes in the sensor placement, yet the features analyzed showed anticipatory mechanisms prior to grasp and release. The appropriate inclusion of placement variability within a control architecture can be coupled with the kinematics and sEMG features to inform object type and anticipate grasp and release.
Published in: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Date of Conference: 09-12 June 2015
Date Added to IEEE Xplore: 19 October 2015
Electronic ISBN:978-1-4673-7201-5