A Prediction of Fingertip Force and Joint Kinematics of Individual Fingers using Motoneuron Firing Activities

Abstract

<h3>Research Objectives</h3> Advanced assistive robotic hands can help individuals with hand impairment to perform daily activities. The detection of intended motor output of individual fingers, however, remains a challenge. The objective of this study was to develop a decoding method to continuously and simultaneously predict fingertip forces and joint kinematics of individual fingers using motoneuron firing frequency. <h3>Design</h3> Blind source separation method was applied to EMG signals to obtain motor unit (MU) firing activities. MU refinement procedure was used to determine finger- and task-specific MU pools. Obtained MU pools were tested on different datasets involving both isometric and dynamic tasks (combined). Populational MU firing frequencies were calculated to estimate the fingertip forces and joint angles concurrently using regression models. Model performance was compared with conventional EMG amplitude-based approach. <h3>Setting</h3> The study was carried out at the NC State/UNC Joint Department of Biomedical Engineering. <h3>Participants</h3> Seven healthy volunteers (Age: 28±7) participated in this study. No participant had any prior neuro-muscular ailments. All participants provided an informed consent. <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measure(s)</h3> Concurrent and continuous prediction of finger kinetics and kinematics via motoneuron activities. <h3>Results</h3> MU approach led to better performance (higher correlation between the predicted and measured output and lower prediction error) in predicting both joint angles and fingertip forces than the EMG amplitude approach. <h3>Conclusion(s)</h3> MU pools can be identified for specific finger-task combinations, which allows concurrent control of fingertip forces and joint angles with assistive robot hands. Separation matrix derived from single finger trials was used applied to combined trials to avoid the computationally intensive nature of HD-EMG decomposition and to determine its usability in real-time. Further development of this method could provide concurrent and continuous control of individual finger movements of highly dexterous robotic hands.