Estimation of ankle joint angle from peroneal and tibial electroneurograms a muscle spindle model approach

Ching Chao Chan, Chou-Ching Lin, Ming-Shaung Ju

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study develops a method for estimating the angle of a passively stretched ankle joint from electroneurograms (ENGs) based on structural muscle spindle models of the tibial and peroneal nerves. Passive ramp-and-hold and alternating stretches of the ankle joint are performed on an anesthetized rabbit. Two cuff electrodes are employed to measure the ENGs of peroneal and tibial nerves simultaneously. From the two ENG signals and the joint angle trajectory, two intrafusal muscle fiber models are constructed and their inverse models are derived. The results of the two models are combined to generate the final angle estimate. An optimization method, called sequential quadratic programming, is employed to find the model parameters that minimize the squared errors between the ankle angles predicted by the model and the measured ankle angles. The performance of the proposed approach is compared with those of an adaptive neuro-fuzzy inference system and an artificial neural network model. The results reveal that the proposed model has the best performance in estimating the ankle joint angle in large-range movements and the smallest tracing error. The proposed method effectively estimates the passive ankle joint angle using the inverse physiological model of an intrafusal muscle fiber.

Original languageEnglish
Article number1250080
JournalJournal of Mechanics in Medicine and Biology
Volume12
Issue number4
DOIs
Publication statusPublished - 2012 Sep 1

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Muscle
Physiological models
Fibers
Quadratic programming
Fuzzy inference
Trajectories
Neural networks
Electrodes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

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abstract = "This study develops a method for estimating the angle of a passively stretched ankle joint from electroneurograms (ENGs) based on structural muscle spindle models of the tibial and peroneal nerves. Passive ramp-and-hold and alternating stretches of the ankle joint are performed on an anesthetized rabbit. Two cuff electrodes are employed to measure the ENGs of peroneal and tibial nerves simultaneously. From the two ENG signals and the joint angle trajectory, two intrafusal muscle fiber models are constructed and their inverse models are derived. The results of the two models are combined to generate the final angle estimate. An optimization method, called sequential quadratic programming, is employed to find the model parameters that minimize the squared errors between the ankle angles predicted by the model and the measured ankle angles. The performance of the proposed approach is compared with those of an adaptive neuro-fuzzy inference system and an artificial neural network model. The results reveal that the proposed model has the best performance in estimating the ankle joint angle in large-range movements and the smallest tracing error. The proposed method effectively estimates the passive ankle joint angle using the inverse physiological model of an intrafusal muscle fiber.",
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N2 - This study develops a method for estimating the angle of a passively stretched ankle joint from electroneurograms (ENGs) based on structural muscle spindle models of the tibial and peroneal nerves. Passive ramp-and-hold and alternating stretches of the ankle joint are performed on an anesthetized rabbit. Two cuff electrodes are employed to measure the ENGs of peroneal and tibial nerves simultaneously. From the two ENG signals and the joint angle trajectory, two intrafusal muscle fiber models are constructed and their inverse models are derived. The results of the two models are combined to generate the final angle estimate. An optimization method, called sequential quadratic programming, is employed to find the model parameters that minimize the squared errors between the ankle angles predicted by the model and the measured ankle angles. The performance of the proposed approach is compared with those of an adaptive neuro-fuzzy inference system and an artificial neural network model. The results reveal that the proposed model has the best performance in estimating the ankle joint angle in large-range movements and the smallest tracing error. The proposed method effectively estimates the passive ankle joint angle using the inverse physiological model of an intrafusal muscle fiber.

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