This is a collaborative proposal (with UC Davis) which is aimed at making concrete some of the major goals of Assistive Robotics. A team of experts has been brought together from the fields of signal processing and control, robotic grasping, and rehabilitative medicine to create a field-deployable assistive robotic system that will allow severely disabled patients to control a robot arm/hand system to perform complex grasping and manipulation tasks using novel Brain Muscle Computer Interfaces (BMCI). Further, the intent of this effort is not just technology-driven, but is also driven by clear and necessary clinical needs, and will be evaluated on how well it meets these clinical requirements. Validation will be performed at the Department of Regenerative and Rehabilitation Medicine at Columbia University on a diverse set of disabled users who will provide important feedback on the technology being developed, and this feedback will be used to iterate on the system design and implementation.

Intellectual Merit: The intellectual merit of this proposal includes: o Novel research in Human Machine Interfaces that has the potential to be transformative in eliciting rich, multi-degree-of-freedom signal content from simple and non-invasive surface electromyographic (sEMG) sensors. o Development of smart adaptive software that employs machine learning algorithms that can continually monitor user performance, and then automatically calibrate and tune system parameters based on system performance. o Data driven methods for real-time grasp planning algorithms that can be used with both known and unknown objects. o Methods for finding pose-robust grasps that are tolerant of errors in sensing. o Evaluation of an underactuated hand as a grasping device for certain application tasks. o Integration of 3D vision with real-time grasp planning. o Scientific evaluation at the clinical level of the impact of these new technologies on the disabled population.

Broader Impacts: The broader impacts of this proposal include: o Development of a complete system to aid the severely disabled population with tetraplegia. o Extensions of this technlogy to others lacking motor control function including multiple sclerosis, stroke, amyotrophic lateral sclerosis (ALS or Lou Gehrig disease), cerebral palsy, and muscular dystrophy. o New technology that can extend the reach and impact of the field of Assistive Robotics. o Major extensions to the open-source GraspIt! software system that will allow many other researchers to leverage the results of this project. o Educational thrusts that will bring together engineering students, clinicians and the disabled population to extend the reach and scope of Assistive Robotics. o New directions in Human Machine Interfaces that can extend beyond the disabled population and into a variety of other applications.

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Columbia University
New York
United States
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