Decoding force from multiunit recordings from the median nerve

James Wright; Vaughan G. Macefield; André van Schaik; Jonathan Tapson

doi:10.1109/ICORR.2015.7281327

Decoding force from multiunit recordings from the median nerve

James Wright, Vaughan G. Macefield, André van Schaik, Jonathan Tapson

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

3 Citations (Scopus)

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
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	https://doi.org/10.1109/ICORR.2015.7281327
Publication status	Published - 2015
Event	IEEE International Conference on Rehabilitation Robotics - Duration: 11 Aug 2015 → …

Conference

Conference	IEEE International Conference on Rehabilitation Robotics
Period	11/08/15 → …

Keywords

artificial limbs
prosthesis
robotics

Access to Document

10.1109/ICORR.2015.7281327

Cite this

Wright, J., Macefield, V. G., Schaik, A. V., & Tapson, J. (2015). Decoding force from multiunit recordings from the median nerve. In Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics: ICORR 2015: Enabling Technology Festival, 11-14 August, Nanyang Technological University, Singapore (pp. 956-960). IEEE. https://doi.org/10.1109/ICORR.2015.7281327

@inproceedings{22441528e4f84f26b62964fcfcc41170,

title = "Decoding force from multiunit recordings from the median nerve",

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.",

keywords = "artificial limbs, prosthesis, robotics",

author = "James Wright and Macefield, {Vaughan G.} and Schaik, {Andr{\'e} van} and Jonathan Tapson",

year = "2015",

doi = "10.1109/ICORR.2015.7281327",

language = "English",

isbn = "9781479918096",

pages = "956--960",

booktitle = "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",

note = "IEEE International Conference on Rehabilitation Robotics ; Conference date: 11-08-2015",

}

Wright, J, Macefield, VG, Schaik, AV & Tapson, J 2015, Decoding force from multiunit recordings from the median nerve. in Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics: ICORR 2015: Enabling Technology Festival, 11-14 August, Nanyang Technological University, Singapore. IEEE, pp. 956-960, IEEE International Conference on Rehabilitation Robotics, 11/08/15. https://doi.org/10.1109/ICORR.2015.7281327

Decoding force from multiunit recordings from the median nerve. / Wright, James; Macefield, Vaughan G.; Schaik, André van et al.
Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics: ICORR 2015: Enabling Technology Festival, 11-14 August, Nanyang Technological University, Singapore. IEEE, 2015. p. 956-960.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AU - Macefield, Vaughan G.

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AU - Tapson, Jonathan

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N2 - 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.

AB - 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.

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KW - prosthesis

KW - robotics

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ER -

Decoding force from multiunit recordings from the median nerve

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