Decoding force from multiunit recordings from the median nerve

James Wright; Vaughan G. Macefield; Andre Van Schaik; Jonathan Tapson

doi:10.1109/ICORR.2015.7281327

Decoding force from multiunit recordings from the median nerve

James Wright, Vaughan G. Macefield, Andre Van Schaik, Jonathan Tapson

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › 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)
Subtitle of host publication	Enabling Technology Festival
Editors	Haoyong Yu, David Braun, Domenico Campolo
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	956-960
Number of pages	5
ISBN (Electronic)	9781479918072
ISBN (Print)	9781479918089
DOIs	https://doi.org/10.1109/ICORR.2015.7281327
Publication status	Published - 28 Sept 2015
Externally published	Yes
Event	IEEE International Conference on Rehabilitation Robotics (ICORR) 2015 - Nanyang Technological University, Singapore, Singapore Duration: 11 Aug 2015 → 14 Aug 2015 Conference number: 14th https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7281165 https://ieeexplore.ieee.org/xpl/conhome/7256409/proceeding

Publication series

Name	IEEE International Conference on Rehabilitation Robotics
Volume	2015-September
ISSN (Print)	1945-7898
ISSN (Electronic)	1945-7901

Conference

Conference	IEEE International Conference on Rehabilitation Robotics (ICORR) 2015
Country/Territory	Singapore
City	Singapore
Period	11/08/15 → 14/08/15
Internet address	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7281165 https://ieeexplore.ieee.org/xpl/conhome/7256409/proceeding

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10.1109/ICORR.2015.7281327

Cite this

Wright, J., Macefield, V. G., Van Schaik, A., & Tapson, J. (2015). Decoding force from multiunit recordings from the median nerve. In H. Yu, D. Braun, & D. Campolo (Eds.), Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015): Enabling Technology Festival (pp. 956-960). Article 7281327 (IEEE International Conference on Rehabilitation Robotics; Vol. 2015-September). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICORR.2015.7281327

Wright, James ; Macefield, Vaughan G. ; Van Schaik, Andre et al. / Decoding force from multiunit recordings from the median nerve. Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015): Enabling Technology Festival. editor / Haoyong Yu ; David Braun ; Domenico Campolo. United States : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 956-960 (IEEE International Conference on Rehabilitation Robotics).

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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.",

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Wright, J, Macefield, VG, Van Schaik, A & Tapson, J 2015, Decoding force from multiunit recordings from the median nerve. in H Yu, D Braun & D Campolo (eds), Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015): Enabling Technology Festival., 7281327, IEEE International Conference on Rehabilitation Robotics, vol. 2015-September, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 956-960, IEEE International Conference on Rehabilitation Robotics (ICORR) 2015, Singapore, Singapore, 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.; Van Schaik, Andre et al.
Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015): Enabling Technology Festival. ed. / Haoyong Yu; David Braun; Domenico Campolo. United States: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 956-960 7281327 (IEEE International Conference on Rehabilitation Robotics; Vol. 2015-September).

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

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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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Wright J, Macefield VG, Van Schaik A, Tapson J. Decoding force from multiunit recordings from the median nerve. In Yu H, Braun D, Campolo D, editors, Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015): Enabling Technology Festival. United States: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 956-960. 7281327. (IEEE International Conference on Rehabilitation Robotics). doi: 10.1109/ICORR.2015.7281327

Decoding force from multiunit recordings from the median nerve

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