Abstract
Much attention has been focused on the detection of volitionary motor commands from the efferent Peripheral Nervous System as a control signal for an advanced prosthetic limb, or the delivery of artificial sensory data to the Peripheral Nervous System as feedback. Less explored has been the potential for natural sensory signals to act as sensor input to neuroprosthetic systems. Many conditions with paralysis as a symptom leave the afferent peripheral nervous system functional, and potentially available as a feedback signal to a control system. In order to demonstrate the feasibility of using such a signal we decode a multiunit afferent nerve signal and use an extreme learning machine to perform a regression to decode force data. From this we were able to show that afferent signals from the fingertip can be decoded into force profiles.
Original language | English |
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Title of host publication | Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics: ICORR 2015: Enabling Technology Festival, 11-14 August, Nanyang Technological University, Singapore |
Publisher | IEEE |
Pages | 956-960 |
Number of pages | 5 |
ISBN (Print) | 9781479918096 |
DOIs | |
Publication status | Published - 2015 |
Event | IEEE International Conference on Rehabilitation Robotics - Duration: 11 Aug 2015 → … |
Conference
Conference | IEEE International Conference on Rehabilitation Robotics |
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Period | 11/08/15 → … |
Keywords
- artificial limbs
- prosthesis
- robotics