This core focuses on enhancing the function and acceptability of advanced assistive devices (e.g., exoskeletons, artificial limbs and neural prostheses) by addressing the human-machine interface, resulting in more efficient cooperative interactions. 

 This core also aims to reduce the cognitive burden of controlling a device by endowing the devices with intelligence using cutting-edge machine learning approaches.

CAPABILITIES

  • ​Validated gaze and movement assessment technology
  • Effective sensory-motor training strategy for prosthesis control
  • Studying the effects of an innovative augmented sensory feedback protocol for motor control training using a virtual environment and a desktop mounted robotic arm
  • Developing an inexpensive, modular prosthetic socket platform to reduce time and resource costs for evaluation of myoelectric control
  • Determining if the use of the 3D-printed modular socket is quantitatively and qualitatively similar to that of a user-specific prosthetic socket
  • Exploring integration of sensory feedback systems into our modular sockets
  • Development and translation of the Gaze and Movement Analysis (GaMA), a novel testing protocol using synchronized motion and eye tracking to explore and quantify human visual-motor behaviour during goal-directed reaching tasks
  • Translation of this metric to other sites in North America
  • Conducting studies on lower limb osseointegration (direct skeletal fixation of a prosthesis)

EQUIPMENT

  • Modular prosthetic limb
  • Bipedal robots
  • Wireless electromyography systems​
  • Cyberglove
  • Real-time machine learning
  • Mac mini servers
  • 3D printers
  • Eye tracking system
  • Bento arm
  • Handi-hand
  • ​Brachioplexus