End-point robot manipulandum

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Much of my current research program involves investigating sensorimotor learning. Specifically, my work characterizes the role of sensory plasticity in motor learning and this successful program has been continuously funded by my NSERC Discovery Grants since 2005. This research program investigates both the visual and sensory induced (proprioceptive) contributions to motor learning and motor control of the arm, as well as proprioceptive (felt) acuity of the hand motion. Being able to rigorously measure and control movements of the arm is necessary to quantify proprioceptive acuity and understanding and model its contribution to motor learning and motor control. I have published 36 peer-reviewed articles (11 in the past 5 years alone) that involved these carefully controlled movement of the hand using a CFI-funded InMotion2 robot manipulandum that I acquired in 2005. After 15 years of regular use, this manipulandum is no longer operating despite replacing all the computer components. To continue this important research, it is urgent that we purchase a replacement, like a Kinarm endpoint robot, to fulfill the goals of my NSERC-funded program to investigate and model how the brain combines different types of sensory information to control and adapt movements. Such replacement will also allow my HQPs to complete their dissertations and continue local and international collaborations which will benefit Canada to gain high profile in this field. Moreover, a new robot manipulandum platform would also allow me to incorporate more sophisticated virtual reality stimuli and thereby expand my research program to address how the brain learns, and stores information about object and tool dynamics that are necessary for interacting with objects. The results of my research will provide a richer understanding of the sources of information used by the brain for controlling, correcting, adapting and learning new movements, and the neural mechanisms underlying the incorporation of this information. The results will provide not only fundamental insight into location estimates of our effectors, but also into how we learn movement-related properties of objects that we interact with and manipulate. This has important practical benefits for the development of tools and environments that are easier to manipulate and navigate. In general, the insights gained from the proposed research will lead to improved technology for rehabilitation, better human-machine interfaces, and better motor-skills training for all levels of performance.