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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages2362-2366
Number of pages5
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 2010 Aug 312010 Sep 4

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period10-08-3110-09-04

All Science Journal Classification (ASJC) codes

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

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