ANFIS to estimate damping coefficient from EMG to optimize the interaction force

Anwar, T; Jumaiiy, AA

ANFIS to estimate damping coefficient from EMG to optimize the interaction force

Anwar, T Jumaiiy, AA

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 818 - 823
Issue Date:: 2016-11-07

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Field	Value	Language
dc.contributor.author	Anwar, T	en_US
dc.contributor.author	Jumaiiy, 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. 818 - 823	en_US
dc.identifier.isbn	9781509006250	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106281
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. This is mostly due to the insufficient bidirectional information interaction between exoskeleton and patient. In the shared control at the interaction point, it is very important that the deficiency of impaired lower limb in sharing the knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance the robotic exoskeleton would provide. 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 for proprioceptive feedback on the status of the interaction force at the patient robotic exoskeleton interaction point. ANFIS has been used to model the relationship between input and output. Interaction forces, rate of change in surface electromyography (EMG) signal are two inputs to ANFIS model and impedance parameters damping coefficients (also stiffness) is output. Impedance control law has damping as one of the tuning parameter. The resultant total torque is calculated from this law. 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.2016.7737772	en_US
dc.title	ANFIS to estimate damping coefficient from EMG to optimize the interaction force	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. This is mostly due to the insufficient bidirectional information interaction between exoskeleton and patient. In the shared control at the interaction point, it is very important that the deficiency of impaired lower limb in sharing the knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance the robotic exoskeleton would provide. 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 for proprioceptive feedback on the status of the interaction force at the patient robotic exoskeleton interaction point. ANFIS has been used to model the relationship between input and output. Interaction forces, rate of change in surface electromyography (EMG) signal are two inputs to ANFIS model and impedance parameters damping coefficients (also stiffness) is output. Impedance control law has damping as one of the tuning parameter. The resultant total torque is calculated from this law. 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.

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http://hdl.handle.net/10453/106281