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A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands.
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The validity of using one force platform to quantify whole-body forces, velocities, and power during a plyometric push-up
Abstract Background Previous studies have typically measured velocity and power parameters during the push-up, either using one or two force platforms. The purpose of the study was to compare the force, velocity, and power parameters between the one-force-platform method and the two-force-platform method during plyometric push-ups. Methods Thirty-four physically active young adults participated in the study to perform the plyometric push-up. For the two-force-platform calculation method, the forces applied to the feet and hands were both measured. For the one-force-platform calculation method, the forces applied to the feet were assumed to be constant, while the forces applied to hands were measured by one force platform. Whole-body linear velocities were calculated based on the impulse and momentum theorem. Whole-body power was calculated as the product of the whole-body forces and velocities. Results The one-force-platform method overestimated the whole-body velocities and power compared with the two-force-platform method (1.39 ± 0.37 m/s vs. 0.90 ± 0.23 m/s, Cohen’s d = 1.59, p < 0.05; 1.63 ± 0.47 W/body weight vs. 1.03 ± 0.29 W/body weight, Cohen’s d = 1.49, p < 0.05). These differences were caused by the decreased forces applied to the feet compared to the initial value throughout most of the push-up phase. Large to perfect correlations (r = 0.55 – 0.99) were found for most variables between the two-force-platform and one-force-platform methods. Previous findings of push-up velocities and power using the two-force-platform and one-force-platform methods should be compared with caution. While the two-force-platform method is recommended, linear regression equations may be used to predict velocities and power parameters obtained from one force platform. Conclusions For those professionals who need to accurately quantify kinetic variables during the plyometric push-up, the two-force-platform method should be considered.
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- Award ID(s):
- 2151465
- PAR ID:
- 10313193
- Date Published:
- Journal Name:
- BMC Sports Science, Medicine and Rehabilitation
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2052-1847
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s13102-021-00330-z
