Event Title
Surface Electromyographic (sEMG) Transduction of Hand Joint Angles for Human Interfacing Devices (HID)
Location
Loosemore Auditorium
Description
PURPOSE: This study investigates the use of surface electromyography (sEMG) as a tool to improve human interfacing devices (HID) information bandwidth through the transduction of the fingertip workspace. SUBJECTS: A Sollerman Hand Function Test (SHFT) sEMG dataset of twenty-two subjects performing 26 activities of living (ADL) is utilized, seven sEMG spots and Fingertip Joint Angles recorded in 18 degrees of freedom are observed for each subject. METHODS AND MATERIALS: Electrophysiology, physiology, and anatomy of the forearm and hand are investigated to optimize sensor location, and viable command gestures. Enveloping, spectral, and coherence analysis are applied to the SHFT dataset to differentiate pinching and grasping tasks. ANALYSES: Pinches and grasps were found to cause very different activation patterns in sEMG spot 3 relating to flexion of digits I - V. Spectral moment was less correlated with differentiation and provided information about the degree of object manipulation and extent of fatigue during tasks. Coherence increased between flexors and extensors with intensity of tasks but was found corrupted by crosstalk with increasing intensity of flexion. RESULTS: Viable gestures for detection algorithms are identified based on the muscles discerned to be visible in the dataset through intensity, spectral moment, power spectra, and coherence. The study also designed an sEMG amplification system capable of capturing HD-sEMG for future work. CONCLUSIONS: The study shows pinch and grasp differentiation through intensity envelopes of digit I-V flexors. Methods for further analysis of the SHFT dataset and design tools for a new dataset are provided.
Surface Electromyographic (sEMG) Transduction of Hand Joint Angles for Human Interfacing Devices (HID)
Loosemore Auditorium
PURPOSE: This study investigates the use of surface electromyography (sEMG) as a tool to improve human interfacing devices (HID) information bandwidth through the transduction of the fingertip workspace. SUBJECTS: A Sollerman Hand Function Test (SHFT) sEMG dataset of twenty-two subjects performing 26 activities of living (ADL) is utilized, seven sEMG spots and Fingertip Joint Angles recorded in 18 degrees of freedom are observed for each subject. METHODS AND MATERIALS: Electrophysiology, physiology, and anatomy of the forearm and hand are investigated to optimize sensor location, and viable command gestures. Enveloping, spectral, and coherence analysis are applied to the SHFT dataset to differentiate pinching and grasping tasks. ANALYSES: Pinches and grasps were found to cause very different activation patterns in sEMG spot 3 relating to flexion of digits I - V. Spectral moment was less correlated with differentiation and provided information about the degree of object manipulation and extent of fatigue during tasks. Coherence increased between flexors and extensors with intensity of tasks but was found corrupted by crosstalk with increasing intensity of flexion. RESULTS: Viable gestures for detection algorithms are identified based on the muscles discerned to be visible in the dataset through intensity, spectral moment, power spectra, and coherence. The study also designed an sEMG amplification system capable of capturing HD-sEMG for future work. CONCLUSIONS: The study shows pinch and grasp differentiation through intensity envelopes of digit I-V flexors. Methods for further analysis of the SHFT dataset and design tools for a new dataset are provided.