Planning Grant: NSF Engineering Research Center for Smart Personalized Assistive Devices and Enabling Systems (SPADES)

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The Planning Grants for Engineering Research Centers competition was run as a pilot solicitation within the ERC program. Planning grants are not required as part of the full ERC competition, but intended to build capacity among teams to plan for convergent, center-scale engineering research.Assistive devices provide the critical support required to overcome physical limitations, prevent injury, and improve safety for healthy workers and people with disabilities. The epidemic of diabetes, dementia, and obesity, the rise in childhood disability, and the aging population have increased the use of assistive devices, making them an instrumental part of the care system in our society. While many users of have an increased need for assistive devices, they may have a declined willingness to use them due to self-pride, poor fit, high cost, and changing needs. The Engineering Research Center (ERC) for Smart Personalized Assistive Devices and Enabling Systems (SPADES) is proposed to meet this societal need by creating personalized assistive devices (PADs) and smart care system (SCS). Advancements in science and engineering have enabled the material, design, manufacturing, learning, and control of PADs. A distributed manufacturing and service network for PADs will be established as the foundation for SCS. The ERC for SPADES is aimed to create an innovation ecosystem and transform PADs and SCS in our future society with the diversity and culture of inclusion for women, underrepresented minority (URM), and veterans. This planning grant will establish a convergent research and education team with partnership among engineering, psychology, sociology, kinesiology, rehabilitation, and industry (the breadth of knowledge) and integration of fundamental sciences (the depth of knowledge). New scientific knowledge in polymeric soft material for additive manufacturing (AM), contact mechanics, human-centered design, and machine learning will be explored for PADs and SCS. AM of micro-architectured and functionally graded materials will be studied to construct conformal shapes for human contact interface with personalized properties. The dynamic biomechanical and contact models will be established to design a PAD contact interface that matches to the optimal personal preference space of a user, which is identified based on the multidimensional unfolding modeling and inputs from the user feedback and biometric sensor data measured during evaluation. To address the evolution of user needs, the real-time control of active PADs and the big data based learning for improved PAD design and control will be established. The big data is based on robust extraction, transformation, and identification capabilities to analyze the evolution of sensor measurements and model predictions, which are the foundation of learning in SCS. A multi-level decision making strategy will utilize data derived from sensors and user feedback, combined with supervised learning and multi-objective optimization, to drive the decision-making strategy for both passive and active PADs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.