About

Our mission is to give back mobility to patients

Neuromusculoskeletal  model

We are developing the next generation of fast and robust personalized neuromusculoskeletal models to predict internal muscle-tendon states.

See:

• Robust real-time musculoskeletal modelling driven by electromyograms
• Back neuromusculoskeletal model
• CEINMS-RT: an open-source framework for the continuous neuro-mechanical model-based control of wearable robots

Wearable robotics

We are creating the future of wearable robotics controller for rehabilitation of neural injured individuals to give them back autonomy and mobility.

We also aim to reduce injury by providing human augmentation controller that steer neuromusculoskeletal tissue toward homeostasis.

To achieve this, we embed real-time personalized neuromusculoskeletal into robotics controller to create a communication channel between the human and the machine. Furthermore, we create smart assistance that provide optimal stimuli for neuromusculoskeletal using predictive simulation.

See:

• Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling
• Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
• Adaptive model-based myoelectric control for a soft wearable arm exosuit: A new generation of wearable robot control
• Neuromechanical model-based adaptive control of bilateral ankle exoskeletons: biological joint torque and electromyogram reduction across walking conditions

Neuromusculoskeletal predictive simulation

We are working on creating a fast predictive simulation to simulate human and wearable robotics devices to create new controllers, designs and rehabilitation strategies in silico using our expertise in biomechanics and robotics.

See:

• MyoSim: Fast and physiologically realistic MuJoCo models for musculoskeletal and exoskeletal studies
• MyoSuite--A contact-rich simulation suite for musculoskeletal motor control
• Reinforcement learning identifies age-related balance strategy shifts

Featured publications

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Research partners

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  McGill

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  CRIR

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  HJR

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  CIM