Assessment of upper limb muscle tone level based on estimated impedance parameters

Zaw, Zaw Lay Htoon and Sidek, Shahrul Na'im and Fatai, Sado and Yunahar, Taufik (2017) Assessment of upper limb muscle tone level based on estimated impedance parameters. In: IEEE-EMBS Conference of Biomedical, Engineering and Sciences (IECBES 2016), 4th-8th Dec. 2016, Kuala Lumpur.

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Abstract

Many strategies have been developed by occupational and physical therapists for the assessment of poststroke patients’ upper limb muscle tone and physical recovery progress. Despite, having the appropriate skills, they face serious challenges in quantifying continuously, the patients’ recovery progress. Moreover, the therapy has become more costly and time consuming since the patients are required to have a face-to-face contact with the therapist over a long period of time. By deploying robot-assisted rehabilitation therapy, some of these problems have been addressed, however, serious challenges still exist in the aspect of proper estimation and assessment of patients muscle tone level and recovery progress during rehabilitation therapy. This paper proposes an appropriate strategy for prediction and assessment of subjects’ muscle tone level and recovery based on the estimation of upper-limb mechanical impedance parameters. The subjects’ mechanical impedance parameters are estimated using a recursive least square estimator method and the muscle tone level are predicted by Artificial Neural Network (ANN) which has been trained using the estimated impedance parameters. Preliminary experimental result shows that the upper-limb impedance parameters can be estimated to an accuracy level of 90%, while simulation studies have revealed that the muscle tone level can be reliably predicted at 95.01% accuracy level.

Item Type:	Conference or Workshop Item (Other)
Additional Information:	3028/53828
Uncontrolled Keywords:	occupational therapists; robot-assisted rehabilitation; post-stroke patients; 3-DOF robot-assisted; upperlimb impedance parameter; recursive least square estimator; Artificial Neural Network (ANN)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr. Shahrul Naim Sidek
Date Deposited:	09 Jan 2017 17:08
Last Modified:	10 Jan 2019 12:53
URI:	http://irep.iium.edu.my/id/eprint/53828

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