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Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.
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Effects of Biofeedback on Muscle Effort Reduction when Holding Positions with a Powered Upper Limb Exoskeleton
Exoskeletons have the potential to support daily activities by assisting the movement performance. Previous studies have shown that powered elbow exoskeletons can reduce muscle effort during continuous cyclic movement. However, natural movements have embedded stationary hold periods as well as transitions between postures. The human performance when using exoskeletons for more natural movements should be evaluated. In this study, we implemented visual and haptic electromyography (EMG) biofeedback to help people use an upper limb exoskeleton to perform a target position matching task. Participants (n=36) did not significantly reduce their muscle effort during hold periods when provided with biofeedback. Participants had difficulty relaxing their muscles at more flexed postures during hold periods, suggesting that they continued to provide effort instead of taking advantage of the device. To fully benefit from robotic exoskeletons, additional training and more advanced controllers might be needed.
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- Award ID(s):
- 2110133
- PAR ID:
- 10527426
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 67
- Issue:
- 1
- ISSN:
- 1071-1813
- Page Range / eLocation ID:
- 790 to 794
- 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/10.1177/21695067231192634
