L2M – Vision-Based Upper-Limb Assistive Devices

Upper-limb assistive devices remain hard to use because control often feels unnatural and tiring, which leads to abandonment. The intern’s M.Sc. project proposes a non-invasive, vision-based control layer that links what the user looks at to objects in the real-world 3D environment and predicts natural hand paths and joint actions. This approach is intended to lower mental effort, shorten training, and support embodiment so devices feel more like part of the body. Canadian prosthetic and exoskeleton teams can use the results to choose control features, define clear performance targets, and plan integration with existing hardware and software. Through Lab2Market, industry and clinical stakeholders will be engaged to identify adoption barriers, ethics and workflow needs, and pilot designs, creating a practical path to a validated, commercially viable product in Canada’s digital rehabilitation space.

Faculty Supervisor:

Albert Vette

Student:

Partner:

Edmonton Unlimited

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Alberta

Program: