Estimation of ankle joint angle from peroneal and tibial electroneurograms based on muscle spindle model.

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Abstract

The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves. Based on the two ENGs and the joint angle trajectory, two muscle spindle models were constructed and their inverse models were integrated to compute angle estimates. The model parameters were optimized. The performance of our approach was compared with those of the adaptive neuro-fuzzy inference system and artificial neural network model. The results revealed that our model had a better performance of estimating the ankle joint angle in large-range movements and smaller tracking errors. This study provides a new estimation algorithm to extract the joint angle from the information conveyed in a nerve.

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Muscle Spindles
Ankle Joint
Muscle
Joints
Tibial Nerve
Peroneal Nerve
Architectural Accessibility
Neural Networks (Computer)
Electrodes
Rabbits
Fuzzy inference
Trajectories
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Estimation of ankle joint angle from peroneal and tibial electroneurograms based on muscle spindle model.",
abstract = "The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves. Based on the two ENGs and the joint angle trajectory, two muscle spindle models were constructed and their inverse models were integrated to compute angle estimates. The model parameters were optimized. The performance of our approach was compared with those of the adaptive neuro-fuzzy inference system and artificial neural network model. The results revealed that our model had a better performance of estimating the ankle joint angle in large-range movements and smaller tracking errors. This study provides a new estimation algorithm to extract the joint angle from the information conveyed in a nerve.",
author = "Lin, {Chou Ching K} and Ju, {Ming Shaung} and Chan, {Ching Chao}",
year = "2010",
language = "English",
pages = "2362--2366",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Estimation of ankle joint angle from peroneal and tibial electroneurograms based on muscle spindle model.

AU - Lin, Chou Ching K

AU - Ju, Ming Shaung

AU - Chan, Ching Chao

PY - 2010

Y1 - 2010

N2 - The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves. Based on the two ENGs and the joint angle trajectory, two muscle spindle models were constructed and their inverse models were integrated to compute angle estimates. The model parameters were optimized. The performance of our approach was compared with those of the adaptive neuro-fuzzy inference system and artificial neural network model. The results revealed that our model had a better performance of estimating the ankle joint angle in large-range movements and smaller tracking errors. This study provides a new estimation algorithm to extract the joint angle from the information conveyed in a nerve.

AB - The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves. Based on the two ENGs and the joint angle trajectory, two muscle spindle models were constructed and their inverse models were integrated to compute angle estimates. The model parameters were optimized. The performance of our approach was compared with those of the adaptive neuro-fuzzy inference system and artificial neural network model. The results revealed that our model had a better performance of estimating the ankle joint angle in large-range movements and smaller tracking errors. This study provides a new estimation algorithm to extract the joint angle from the information conveyed in a nerve.

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