Learning muscle activation patterns via nonlinear oscillators: application to lower-limb assistance

Aguirre-Ollinger, G

Learning muscle activation patterns via nonlinear oscillators: application to lower-limb assistance

Aguirre-Ollinger, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 1182 - 1189
Issue Date:: 2013-01

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Field	Value	Language
dc.contributor.author	Aguirre-Ollinger, G	en_US
dc.contributor.editor	Amato Nancy	en_US
dc.date	2013-11-03	en_US
dc.date.issued	2013-01	en_US
dc.identifier.citation	IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 1182 - 1189	en_US
dc.identifier.isbn	978-1-4673-6357-0	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27585
dc.description.abstract	Achieving coordination between a lower-limb exoskeleton and its user is challenging because walking is a dynamic process that involves multiple, precisely timed muscle activations. Electromyographical (EMG) feedback, in spite of its drawbacks, provides an avenue for assistance by enabling users to reduce the level of muscle activation required for walking. As an alternative to direct EMG feedback, we present a method for exoskeleton control based on learning the activation pattern of specific muscles during cyclic movements. Using the example of pendular leg motion, the torque profile of one muscle group (hip flexors) is learned in a two-step process. First, the estimated torque profile is indexed to the phase of the swing movement using an adaptive frequency oscillator (AFO). The profile is then encoded using linear weighted regression. In the algorithm's assistive mode, the learned profile is reconstructed by means of the AFO and without need for additional EMG input. The reconstructed profile is converted into a torque profile to be physically delivered by the exoskeleton. We tested our method on a single-actuator exoskeleton that assists the hip joint during stationary leg swing. The learning and assistance functions were built on top of an admittance controller that enhances the exoskeleton's mechanical transparency. Initial tests showed a high level of coordination, i.e. simultaneous positive work, between the subjects' hip flexor torque and the exoskeleton's assistive torque. This result opens the door for future studies to test the users' ability to reduce their muscle activation in proportion to the assistance delivered by the exoskeleton	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IEEE/RSJ International Conference on Intelligent Robots and Systems	en_US
dc.relation.ispartof	IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)	en_US
dc.relation.isbasedon	10.1109/IROS.2013.6696500	en_US
dc.rights	© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Learning muscle activation patterns via nonlinear oscillators: application to lower-limb assistance	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Tokyo, Japan	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Tokyo, Japan	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2013-11-03	en_US

Abstract:

Achieving coordination between a lower-limb exoskeleton and its user is challenging because walking is a dynamic process that involves multiple, precisely timed muscle activations. Electromyographical (EMG) feedback, in spite of its drawbacks, provides an avenue for assistance by enabling users to reduce the level of muscle activation required for walking. As an alternative to direct EMG feedback, we present a method for exoskeleton control based on learning the activation pattern of specific muscles during cyclic movements. Using the example of pendular leg motion, the torque profile of one muscle group (hip flexors) is learned in a two-step process. First, the estimated torque profile is indexed to the phase of the swing movement using an adaptive frequency oscillator (AFO). The profile is then encoded using linear weighted regression. In the algorithm's assistive mode, the learned profile is reconstructed by means of the AFO and without need for additional EMG input. The reconstructed profile is converted into a torque profile to be physically delivered by the exoskeleton. We tested our method on a single-actuator exoskeleton that assists the hip joint during stationary leg swing. The learning and assistance functions were built on top of an admittance controller that enhances the exoskeleton's mechanical transparency. Initial tests showed a high level of coordination, i.e. simultaneous positive work, between the subjects' hip flexor torque and the exoskeleton's assistive torque. This result opens the door for future studies to test the users' ability to reduce their muscle activation in proportion to the assistance delivered by the exoskeleton

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http://hdl.handle.net/10453/27585