System identification and damping coefficient estimation from EMG based on ANFIS to optimize human exoskeleton interaction

Anwar, T; Jumaily, AA

System identification and damping coefficient estimation from EMG based on ANFIS to optimize human exoskeleton interaction

Anwar, T Jumaily, AA

Permalink

Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 844 - 849
Issue Date:: 2016-11-07

Closed Access

	Filename	Description	Size
	07737776.pdf	Published version	681.16 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Anwar, T	en_US
dc.contributor.author	Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2016-11-07	en_US
dc.identifier.citation	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 844 - 849	en_US
dc.identifier.isbn	9781509006250	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122899
dc.description.abstract	© 2016 IEEE. although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation due to lack of identification of the system that can build a relationship relating knee joint dynamics to EMG signal. As a result, the insufficient bidirectional information interaction between exoskeleton and patient, the adaptive collaboration is very much absent. In the shared control situation at the interaction point, it is very important that the deficiency of impaired lower limb at knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance need to be provided by the robotic exoskeleton. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network based Fuzzy Inference System (ANFIS) has been proposed to identify a proprioceptive feedback system which plays the role of inverse dynamics in the closed loop controlled Robotic Rehabilitation Device. The identified ANFIS inverse dynamic model updates the current status of the interaction force at the patient robotic exoskeleton interaction point. Interaction forces, rate of change in surface electromyography (EMG) signal, Extracted RMS (Root Mean Square) are three input patterns to ANFIS model and impedance parameters damping coefficients, stiffness are the output. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.	en_US
dc.relation.ispartof	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2016.7737776	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	System identification and damping coefficient estimation from EMG based on ANFIS to optimize human exoskeleton interaction	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation due to lack of identification of the system that can build a relationship relating knee joint dynamics to EMG signal. As a result, the insufficient bidirectional information interaction between exoskeleton and patient, the adaptive collaboration is very much absent. In the shared control situation at the interaction point, it is very important that the deficiency of impaired lower limb at knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance need to be provided by the robotic exoskeleton. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network based Fuzzy Inference System (ANFIS) has been proposed to identify a proprioceptive feedback system which plays the role of inverse dynamics in the closed loop controlled Robotic Rehabilitation Device. The identified ANFIS inverse dynamic model updates the current status of the interaction force at the patient robotic exoskeleton interaction point. Interaction forces, rate of change in surface electromyography (EMG) signal, Extracted RMS (Root Mean Square) are three input patterns to ANFIS model and impedance parameters damping coefficients, stiffness are the output. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122899