Quadriceps muscle models using fuzzy logic and ANFIS

Author

Salleh, S.M. ; Jailani, R. ; Tokhi, M.O.

Author_Institution

Neuromuscular Rehabilitation Res. Group, Univ. Teknol. MARA Malaysia, Shah Alam, Malaysia

fYear

2013

fDate

19-20 Aug. 2013

Firstpage

406

Lastpage

410

Abstract

This paper presents a development of quadriceps muscle models using ANFIS and Fuzzy Logic based on Functional Electrical Stimulation (FES). The models inputs parameter consists of stimulation frequency, pulse width, and sampling time are used to predict quadriceps output torque. The muscle models developed are then validate with the clinical data to evaluate the accuracy of the torque output predicted with the identified parameters. In this study, Fuzzy Logic muscle model gives better performance representing quadriceps muscle model.

Keywords

fuzzy logic; fuzzy neural nets; inference mechanisms; medical computing; muscle; ANFIS; FES; functional electrical stimulation; fuzzy logic; pulse width; quadriceps muscle models; quadriceps output torque prediction; sampling time; stimulation frequency; Biological system modeling; Data models; Force; Fuzzy logic; Mathematical model; Muscles; Neuromuscular stimulation; ANFIS; Functional Electrical Stimulation (FES); Fuzzy Logic; Quadriceps Muscle Model;

fLanguage

English

Publisher

ieee

Conference_Titel

System Engineering and Technology (ICSET), 2013 IEEE 3rd International Conference on

Conference_Location

Shah Alam

Print_ISBN

978-1-4799-1028-1

Type

conf

DOI

10.1109/ICSEngT.2013.6650209

Filename

6650209