Advanced technologies in assistive devices

Enhancing Assistive Devices with Sensorimotor Integration and Machine Learning

The Sensorimotor Integration & Machine Learning 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.

Use Case Scenarios

BLINC Capabilities

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Validated gaze and movement assessment technology

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Effective sensory-motor training strategy for prosthesis control

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Studying the effects of an innovative augmented sensory feedback protocol for motor control training using a virtual environment and a desktop mounted robotic arm

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Developing an inexpensive, modular prosthetic socket platform to reduce time and resource costs for evaluation of myoelectric control

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Conducting studies on lower limb osseointegration (direct skeletal fixation of a prosthesis)

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Determining if the use of the 3D-printed modular socket is quantitatively and qualitatively similar to that of a user-specific prosthetic socket

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Exploring integration of sensory feedback systems into our modular sockets

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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

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Translation of this metric to other sites in North America

Related Equipment

Bionic Limbs for Improved Natural Control Equipment

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Use Case Scenarios

Equipment Highlights

  • Modular prosthetic limb

  • Bipedal robots

  • Wireless electromyography systems​

  • Real-time machine learning

  • Brachioplexus

  • Mac mini servers

  • 3D printer

  • Eye tracking system

  • Bento arm

  • Handi-hand

  • Cyberglove

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