Jacqueline Hebert, MD, FRCPC

Professor, Faculty of Medicine & Dentistry - Physical Medicine & Rehabilitation Medicine Division

Contact

Professor, Faculty of Medicine & Dentistry - Physical Medicine & Rehabilitation Medicine Division
Email
jhebert@ualberta.ca

Overview

About

Dr. Hebert is the director of the BLINC Lab, and has research appointments at the University of Alberta and a clinical appointment at the Glenrose Rehabilitation Hospital.

Dr. Hebert leads the interdisciplinary clinical team that performs upper limb Targeted Reinnervation (TR) surgery in Edmonton, Alberta, Canada. Her research team studies advanced motor control and sensory feedback systems for upper limb myoelectric devices, under a group of projects known as "Bionic Limbs for Improved Natural Control" or "BLINC". Research interests also include using technology to quantify and improve outcomes following limb amputation.


Research

Current Research:

  • Targeted re-innervation for upper limb amputation
  • Sensory feedback systems for myoelectric prostheses
  • The Myoelectric Training Tool: a clinical and research platform
  • The Bento Arm
  • Outcome metrics for upper limb prosthetic performance
  • Computer Assisted Rehabilitation Environment for performance assessment

Courses

BME 530 - Topics in Biomedical Engineering

Individual sections covering such topics as signal processing and rehabilitation engineering. Prerequisite: consent of Instructor.

Fall Term 2020

Browse more courses taught by Jacqueline Hebert

Scholarly Activities

Research - Outcome metrics for upper limb prosthetic performance

We are developing novel outcome assessment tools to detect differences in functional performance with advanced prosthetic systems. Our Modified Box and Blocks Test with Motion Capture allows us to analyze the kinematics of arm and trunk motion during this task. When a prosthetic user moves blocks from one side of the box to the other, there are significant compensations of body movement that occur. The goal of developing advanced prosthetic devices with better control and motion capability is to reduce the abnormal movements seen in prosthetic users and provide more natural arm and trunk movement. New sensitive assessment metrics will help clinicians and prosthetic users decide which prosthetic devices are best to improve function and to minimize long term problems.

References:

Hebert JS, Lewicke J, Williams TR, Vette AH. Normative data for a modified Box and Blocks test measuring upper limb function via motion capture. Journal of Rehabilitation Research and Development. (In press; accepted March 2014). 

Hebert JS, Lewicke J. Case report of a modified Box and Blocks test with motion capture to measure prosthetic function. Journal of Rehabilitation Research and Development 2012, Vol 49, No 8, pages 1163-1174. PMID 23341309

BLINC: Outcome metrics for upper limb prosthetic performance
Research - Sensory feedback systems for myoelectric prostheses

Myoelectric prostheses have recently undergone extensive developments in their complexity and movement patterns, yet controlling these devices can be difficult as they lack the sensory feedback provided by traditional body powered prostheses. With targeted muscle reinnervation surgery, sensory nerves are relocated into the skin so that when touched on part of the reinnervated skin, the patient feels as though they are being touched on their missing limb. In previous work the BLINC group has shown that this restored hand map can be harnessed to provide feedback to the patient such that when they grip something with a robotic hand, a tactor pushes into their reinnervated skin and they feel as if they are gripping the object directly. We are now working on integrating sensory feedback devices into actual prostheses for clinical trials.

Current funding:

“A practical sensory feedback system for upper limb amputees.”

Investigators: JS Hebert, MR Dawson, JP Carey, KR Evans.

Funding: Glenrose Rehabilitation Hospital Foundation Clinical Research Award.

Relevant References:

Schofield JS, Evans KR, Carey JP, Hebert JS. Applications of Sensory Feedback in Motorized Upper Extremity Prosthesis: A Review. Expert Review of Medical Devices. (In press: accepted May 2014)

BLINC: Sensory feedback systems for myoelectric prostheses
Research - Targeted reinnervation for upper limb amputation

Dr. Hebert currently leads the only team in Canada performing the Targeted Reinnervation procedure on upper limb amputees, which has been performed in Edmonton since 2008 with a coordinated interdisciplinary team. We have an active clinical program for targeted reinnervation, supported by the Glenrose Rehabilitation Hospital. Our surgeons have developed a new precise surgical variation of the reinnervation procedure to specifically direct the sensory nerves to cutaneous sensory nerves to control the sensory reinnervation territory. We are currently investigating ways of providing sensory feedback to subjects with reinnervation, and improving motor control and performance.

Current funding:

Restoring upper limb movement sense to amputees; a move towards natural control of prosthetic limbs.

Investigators: PM Marasco, JS Hebert, KM Chan.

Funding: This project is in collaboration with Dr. Paul Marasco, PI on a National Institutes of Health (NIH) grant R01NS081710-02. The focus of the project is to investigate the return of proprioception or “kinesthesia” in our targeted reinnervation and other upper limb amputees.

Relevant references:

Hebert JS, Olson JL, Morhart MJ, Dawson MR, Marasco PD, Kuiken TA, Chan KM. Novel Targeted Sensory Reinnervation Technique To Restore Functional Hand Sensation After Transhumeral Amputation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, December 2013, Vol PP, No 99. DOI: 10.1109/TNSRE.2013.2294907

Hebert JS, Elzinga K, Chan KM, Olson JL, Morhart MJ. Updates in Targeted Sensory Reinnervation for Upper Limb Amputation. Current Surgery Reports, January 2014. Vol 2, No 3, pages 45-53. DOI: 10.1007/s40137-013-0045-7.

BLINC: Targeted reinnervation for upper limb amputation
Research - The Bento Arm

The original Myoelectric Training Tool (MTT) is both functional and inexpensive, but has an un-anatomical appearance and limited payload. To overcome these issues an improved robotic arm, called the “Bento Arm”, was designed specifically for myoelectric training and research applications. The Bento Arm includes five (5) degrees of freedom (shoulder rotation, elbow flexion, wrist rotation, wrist flexion, hand open/close) to match both commercially available and future anticipated myoelectric components.

The arm was designed to be 1:1 scale to anatomical proportions. The stronger MX-series of Dynamixel actuators allow for increased payloads and include integrated position and velocity joint feedback and control. Anthropomorphic arm shells were designed using 3D scanning technology to improve the aesthetics of the arm and allow it to be more easily visualized as an arm. A quick disconnect wrist was specified as the wrist connector to allow commercial myoelectric hands to be interchanged with custom grippers.

Two different software interfaces for the Bento Arm were developed including one that uses MATLAB’s xPC target with a Windows Graphical User Interface (GUI) and another that uses Robotic Operating System (ROS) with a Linux GUI. The prototype of the Bento Arm was 3D printed and tested with the ROS interface and was fitted with a SensorHand Speed (Ottobock, Inc.). Although the arm was primarily designed to be desk mounted we have also interfaced the prototype to a transhumeral socket to verify that it can be worn by amputee subjects in research trials.

BLINC: The Bento Arm
Research - The Myoelectric Training Tool: a clinical and research platform

The Myoelectric Training Tool (MTT) was developed by Michael (Rory) Dawson, BLINC Lab Research Engineer, with the support of a clinical research grant from the Glenrose Rehabilitation Hospital. This robotic arm has five degrees of freedom to mimic the functionality of commercial myoelectric prostheses including hand open/close, wrist rotation, wrist flexion/extension, elbow flexion/extension, and shoulder rotation. Surface EMG signals are used to control the arm, with a flexible control system that also allows the use of joystick or other control methods. The MTT is a versatile platform for research that we use for studying machine learning algorithms for improved control, and integrated sensory-motor feedback trials. We also have developed a clinical version used for training muscle signals in the early stages following upper limb amputation.

References:

Dawson MR, Fahimi F. The development of a myoelectric training tool for above-elbow amputees. Open Biomedical Engineering Journal. 2012;6:5–15

Pilarski, P.M.; Dawson, M.R.; Degris, T.; Carey, J.P.; Chan, K.M.; Hebert, J.S.; Sutton, R.S., “Adaptive Artificial Limbs: A Real-Time Approach to Prediction and Anticipation,” IEEE Robotics & Automation Magazine, vol. 20, no.1, pp. 53–64, March 2013. DOI: 10.1109/MRA.2012.2229948

BLINC: The Myoelectric Training Tool: a clinical and research platform