NDLI: Slip detection using the power spectrum of sensory nerve recordings

Content Provider	IEEE Xplore Digital Library
Author	Guzman, A.W. Riso, R.R. Durand, D.M.
Copyright Year	1997
Description	Author affiliation: Appl. Neural Control Lab., Case Western Reserve Univ., Cleveland, OH, USA (Guzman, A.W.)
Abstract	In cases of spinal cord injury primary sensory afferents below the injury remain fully functional and could be used as natural sensors to provide a feedback signal for FNS control. This paper presents a slip detection method using the power spectrum of spiral cuff ENG. The central toe pad of a cat is used as a model for human glabrous skin. Three modes of tactile stimuli (slip, stretch and pressure) are applied to the central toe pad in an anesthetized preparation while recording the electroneurogram (ENG) from the tibial nerve using a three band spiral cuff electrode. Discriminant analysis of the power spectra of 10 ms data segments was used to classify randomly selected segments as "slip" or "non-slip". The highest classification rate (81% correct) was achieved by normalizing the power spectra by the total power of the segment. Three window sizes (10 ms, 20 ms and 40 ms) were compared and found to have no effect on classification rate. The classification rate appeared to correlate well with the velocity of the slipping object, ranging from 55% to 90% for velocities of 2.5 mm/s to 15 mm/s. In conclusion, the power spectrum can be used to distinguish slip from other types of tactile stimulus and higher velocities are detected more reliably.
Starting Page	2008
Ending Page	2010
File Size	401883
Page Count	3
File Format	PDF
ISBN	0780342623
ISSN	1094687X
DOI	10.1109/IEMBS.1997.758739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skin Humans Electrodes Spirals Frequency Force sensors Laboratories Biomedical engineering Biomedical informatics Image analysis
Content Type	Text
Resource Type	Article

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