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Events on March 27, 2018

Richard Weir

Richard Weir, University of Colorado Denver Presents:

Virally transfected optogenetics as the basis of a nerve interface for the control of prosthetic hands

March 27, 2018 at 12:25pm for 30min
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Bio. F. ff. Weir, PhD, is Director of the Biomechatronics Development Laboratory at the University of Colorado Denver | Anschutz Medical Campus. He is also a Research Healthcare Scientist for the VA Eastern Colorado Health Care System – Denver VA Medical Center, and holds Research Associate Professor appointments in the Departments of Bioengineering and Physical Medicine & Rehabilitation at the University of Colorado Denver | Anschutz Medical Campus. Dr. Weir received a BA in mathematics and a BAI in microelectronics and electrical engineering from Trinity College Dublin, Ireland, in 1983. After working as a control engineer in England, he moved to the USA and obtained his MS and PhD degrees in biomedical engineering from Northwestern University in Evanston, IL. After working at Northwestern University and the Rehabilitation Institute of Chicago for many years Dr. Weir moved his laboratory to University of Colorado Denver | Anschutz Medical Campus.

Abstract:

My laboratory’s research is focused on the development advanced prosthetic systems for individuals with limb loss. This research covers all aspects of the problem ranging from neural control and sensing, mechatronic design and development, novel actuator technologies, and clinical deployment of these systems.  We were extensively involved in the design and development of the prosthetic arm systems built under the DARPA Revolutionizing Prosthetics initiatives. We developed the implantable myoelectric sensor (IMES) system for use in the control of these prosthetic systems which are currently undergoing first-in-human trials at Walter Reed Medical Center. Most recently, we have been exploring novel ways of using optogenetics to non-invasively optically interface with the peripheral nervous system with the goal of providing enhanced prosthesis control and sensory feedback to users with limb loss.

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Daniel McDonnall

Daniel McDonnall, President of Ripple Presents:

Myoelectric implant for improved prosthesis control

March 27, 2018 at 12:55pm for 30min
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Abstract:

We have developed an implantable system which records 32 channels of myoelectric data from multiple residual muscles, and transmits these data to an external transceiver placed in the prosthetic socket. Our objective is to provide simultaneous multi-degree of freedom prosthesis control, ultimately providing an intuitive control experience. This approach supports a high number of independent control signals and provides access to EMG from deep muscles that cannot be accessed with surface electrodes.

Implants were validated in a 6-month canine study. Devices were implanted in the front limb by placing the electronics package behind the shoulder blades with electrodes implanted in deltoideous and the lateral head of triceps muscles. One week following implantation, each animal was fitted with a backpack carrying an external transceiver coil and a battery-powered data acquisition system, and the dogs were allowed to freely walk down a hallway. EMG recorded from each animal as it walked down the hallway had very low noise and, in conjunction with recorded video, clearly indicated swing/stance phases of gait.

Posted by: Nathan Galli

Timo Heister, Ph.D.

Timo Heister, Ph.D., Clemson University Presents:

On Conserving FEM Discretizations for Incompressible Fluid Flow

March 27, 2018 at 3:30pm for 1hr
JWB 335

Abstract:

We will present and discuss new solution approaches for incompressible fluid flow for Finite Element (FEM) computations. Reformulation of the PDEs of the incompressible Navier-Stokes equations allows for better conservation of physical properties like energy or momentum. This comes with additional computational challenges for boundary conditions, time discretization, linear solvers, and adaptive mesh refinement.

Here, we will show some solutions to these challenges and compare to traditional solution approaches both theoretically and by showing numerical results of well-known benchmark problems.

Posted by: Nathan Galli