Knee joint model identification using genetic algorithms

B. S.K.K. Ibrahim; M. S. Huq; M. O. Tokhi; S. C. Gharooni; R. Jailani; Z. Hussain

doi:10.1142/9789814291279_0139

Knee joint model identification using genetic algorithms

B. S.K.K. Ibrahim, M. S. Huq, M. O. Tokhi, S. C. Gharooni, R. Jailani, Z. Hussain

University of Sheffield

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Abstract

FES requires an accurate model of electrically stimulated muscles to control the muscle contraction force. Characterization of electrically stimulated muscle is complex because of the non-linearity and time varying nature of the system with interdependent variables. The muscle model consists of passive properties and active properties. In this research a new approach for estimating passive and active properties of the electrically stimulated quadriceps muscle group is investigated. The objective of this study is to develop a model for the passive and active joint properties of paraplegic for control system development. Passive properties such as elasticity moment and viscosity moment are identified from a passive pendulum test. Active properties including muscle activation and contraction are identified from an electrically stimulated swinging leg test. The identification and estimation approach are based on genetic algorithm tuning of a fuzzy system.

Original language	English
Title of host publication	Mobile Robotics
Subtitle of host publication	Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009
Editors	O. Tosun, M. O. Tokhi, G. S. Virk, H. L. Akin
Publisher	World Scientific Publishing
Pages	1145-1152
Number of pages	8
ISBN (Print)	9814291269, 9789814291262
DOIs	https://doi.org/10.1142/9789814291279_0139
Publication status	Published - Oct 2009
Externally published	Yes
Event	12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - Istanbul, Turkey Duration: 9 Sept 2009 → 11 Sept 2009 Conference number: 12

Publication series

Name	Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009

Conference

Conference	12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines
Abbreviated title	CLAWAR 2009
Country/Territory	Turkey
City	Istanbul
Period	9/09/09 → 11/09/09

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction

Access to Document

10.1142/9789814291279_0139

Cite this

Ibrahim, B. S. K. K., Huq, M. S., Tokhi, M. O., Gharooni, S. C., Jailani, R., & Hussain, Z. (2009). Knee joint model identification using genetic algorithms. In O. Tosun, M. O. Tokhi, G. S. Virk, & H. L. Akin (Eds.), Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009 (pp. 1145-1152). (Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009). World Scientific Publishing. https://doi.org/10.1142/9789814291279_0139

Knee joint model identification using genetic algorithms. / Ibrahim, B. S.K.K.; Huq, M. S.; Tokhi, M. O. et al.
Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009. ed. / O. Tosun; M. O. Tokhi; G. S. Virk; H. L. Akin. World Scientific Publishing, 2009. p. 1145-1152 (Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Ibrahim, BSKK, Huq, MS, Tokhi, MO, Gharooni, SC, Jailani, R & Hussain, Z 2009, Knee joint model identification using genetic algorithms. in O Tosun, MO Tokhi, GS Virk & HL Akin (eds), Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009. Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009, World Scientific Publishing, pp. 1145-1152, 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Istanbul, Turkey, 9/09/09. https://doi.org/10.1142/9789814291279_0139

Ibrahim BSKK, Huq MS, Tokhi MO, Gharooni SC, Jailani R, Hussain Z. Knee joint model identification using genetic algorithms. In Tosun O, Tokhi MO, Virk GS, Akin HL, editors, Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009. World Scientific Publishing. 2009. p. 1145-1152. (Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009). doi: 10.1142/9789814291279_0139

Ibrahim, B. S.K.K. ; Huq, M. S. ; Tokhi, M. O. et al. / Knee joint model identification using genetic algorithms. Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009. editor / O. Tosun ; M. O. Tokhi ; G. S. Virk ; H. L. Akin. World Scientific Publishing, 2009. pp. 1145-1152 (Mobile Robotics: Solutions and Challenges - Proceedings of the 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2009).

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title = "Knee joint model identification using genetic algorithms",

abstract = "FES requires an accurate model of electrically stimulated muscles to control the muscle contraction force. Characterization of electrically stimulated muscle is complex because of the non-linearity and time varying nature of the system with interdependent variables. The muscle model consists of passive properties and active properties. In this research a new approach for estimating passive and active properties of the electrically stimulated quadriceps muscle group is investigated. The objective of this study is to develop a model for the passive and active joint properties of paraplegic for control system development. Passive properties such as elasticity moment and viscosity moment are identified from a passive pendulum test. Active properties including muscle activation and contraction are identified from an electrically stimulated swinging leg test. The identification and estimation approach are based on genetic algorithm tuning of a fuzzy system.",

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AU - Jailani, R.

AU - Hussain, Z.

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AB - FES requires an accurate model of electrically stimulated muscles to control the muscle contraction force. Characterization of electrically stimulated muscle is complex because of the non-linearity and time varying nature of the system with interdependent variables. The muscle model consists of passive properties and active properties. In this research a new approach for estimating passive and active properties of the electrically stimulated quadriceps muscle group is investigated. The objective of this study is to develop a model for the passive and active joint properties of paraplegic for control system development. Passive properties such as elasticity moment and viscosity moment are identified from a passive pendulum test. Active properties including muscle activation and contraction are identified from an electrically stimulated swinging leg test. The identification and estimation approach are based on genetic algorithm tuning of a fuzzy system.

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ER -

Knee joint model identification using genetic algorithms

Abstract

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