Shared Control of Functional Electrical Stimulation and an Electric Motor in a Hybrid Neuroprosthesis

Sharma, Nitin

Title:

Shared Control of Functional Electrical Stimulation and an Electric Motor in a Hybrid Neuroprosthesis

dc.contributor.author	Sharma, Nitin
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	University of Pittsburgh	en_US
dc.date.accessioned	2017-03-23T18:12:55Z
dc.date.available	2017-03-23T18:12:55Z
dc.date.issued	2017-03-08
dc.description	Presented on March 8, 2017 from 12:00 p.m.-1:00 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118 on the Georgia Tech campus.	en_US
dc.description	Nitin Sharma is an assistant professor in the Department of Mechanical Engineering and Material Science at the University of Pittsburgh. His research interests include intelligent and robust control of functional electrical stimulation (FES), modeling, optimization, and control of FES-elicited walking, as well as control of uncertain nonlinear systems with input and state delays. Two NSF awards and one NIH R03 Award fund his current research in hybrid exoskeletons. Before joining the Pittsburgh faculty in 2012, Sharma was an Alberta Innovates-Health Solutions postdoctoral fellow in the Department of Physiology at the University of Alberta in Edmonton, Canada. Sharma received his Ph.D. in 201 O from the Department of Mechanical and Aerospace Engineering at the University of Florida. He received an 0. Hugo Schuck Award and the Best Student Paper Award in Robotics at the 2009 ASME Dynamic Systems and Controls Conference. He was a finalist for the Best Student Paper Award at the 2008 IEEE MultiConference on Systems and Control.	en_US
dc.description	Runtime: 44:12 minutes	en_US
dc.description.abstract	Functional Electrical Stimulation (FES) can be used to artificially activate paralyzed lower limb muscles to restore walking and standing function in persons with neurological disorders. Despite its potential, FES-based walking neuroprosthesis has achieved limited acceptability among persons with paraplegia. This low acceptance is primarily due to the early onset of muscle fatigue during FES and difficulty in obtaining a consistent and reliable response from the paralyzed muscle using traditional control methods. We are employing a hybrid strategy that integrates FES with a powered exoskeleton to overcome these hurdles. This hybrid strategy has several advantages. The main advantage is that the effects of muscle fatigue and any inconsistent response from FES can be compensated by the active exoskeleton, which can potentially lead to improved functional mobility in users with neurological impairments. Other advantages include a reduction in the overall weight of the exoskeleton and neuroplastic improvements in the neuromuscular system due to FES. However, closed-loop control methods are required to effectively integrate FES with a powered exoskeleton because the hybrid combination leads to redundancy in actuation and needs criteria to allocate control between FES and an electric motor. I will present algorithms and models recently developed by our research group to control the hybrid exoskeleton. These methods include: 1) a muscle fatigue model to inform the onset of muscle fatigue and muscle recovery during FES, 2) Shared control of FES and electric motor based on the fatigue model, and 3) muscle synergy inspired control of a hybrid walking exoskeleton.	en_US
dc.format.extent	44:12 minutes
dc.identifier.uri	http://hdl.handle.net/1853/56544
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Functional electrical stimulation	en_US
dc.subject	Neuroprosthesis	en_US
dc.subject	Powered exoskeleton	en_US
dc.subject	Robotics	en_US
dc.title	Shared Control of Functional Electrical Stimulation and an Electric Motor in a Hybrid Neuroprosthesis	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication	9bcc24f0-cb07-4df8-9acb-94b7b80c1e46