Abstract
Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.
| Original language | English |
|---|---|
| Pages (from-to) | 541-565 |
| Number of pages | 25 |
| Journal | Physiological measurement |
| Volume | 34 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2013 May 1 |
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All Science Journal Classification (ASJC) codes
- Biophysics
- Physiology
- Biomedical Engineering
- Physiology (medical)
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Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction. / Chu, Jun Uk; Song, Kang Il; Han, Sungmin; Lee, Soo Hyun; Kang, Ji Yoon; Hwang, Dosik; Suh, Jun Kyo Francis; Choi, Kuiwon; Youn, Inchan.
In: Physiological measurement, Vol. 34, No. 5, 01.05.2013, p. 541-565.Research output: Contribution to journal › Article
TY - JOUR
T1 - Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction
AU - Chu, Jun Uk
AU - Song, Kang Il
AU - Han, Sungmin
AU - Lee, Soo Hyun
AU - Kang, Ji Yoon
AU - Hwang, Dosik
AU - Suh, Jun Kyo Francis
AU - Choi, Kuiwon
AU - Youn, Inchan
PY - 2013/5/1
Y1 - 2013/5/1
N2 - Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.
AB - Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.
UR - http://www.scopus.com/inward/record.url?scp=84876911855&partnerID=8YFLogxK
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U2 - 10.1088/0967-3334/34/5/541
DO - 10.1088/0967-3334/34/5/541
M3 - Article
C2 - 23604025
AN - SCOPUS:84876911855
VL - 34
SP - 541
EP - 565
JO - Physiological Measurement
JF - Physiological Measurement
SN - 0967-3334
IS - 5
ER -