Abstract  [Link]

The US PI (Vinjamuri) has proposed to model hand synergies based on the interactions between cerebral cortex, thalamus,cerebellum (recurrent neural network) and basal ganglia (spiking neural network) in the active research award. In contrast, in this supplement, the US PI working with the India PI (Dr. Nayan Kakoty) is planning to model the neurological circuits in direct collaboration with the clinicians. The India PI has prior experience and has already modeled basal ganglia working in unison with deep brain stimulators for corrective arm movements. This gives the PIs opportunities to develop their models together (one developed using a machine learning framework and the other based on neurological circuits in direct collaboration with clinicians) and compare their performance in real-time control of hand prosthetics and exoskeletons.

The US PI has thus far tested synergy-based models in motor control in hand exoskeletons only and the India PI has not tested synergy-based models in control of hand prosthetics. Through this collaboration,the synergy-based control will be tested in prosthetic hands developed in the India PI's lab. This also gives both PIs the opportunities to advance models in biomechanics and control applied to the control of prosthetics and exoskeletons. The PIs have overlapping and complementary research interests.This research will promote the understanding of neural representations and central mechanisms for human motor control and motor learning, using biomimetic models informed by clinical guidance.

 

Publications

Please refer to publications page[Link]

 

 

Abstract  [Link]

With a brain controlled exoskeleton, an individual with spinal cord injury performed a symbolic-kickoff of for the World Cup in Brazil in 2014. Human-machine interfaces have not only become popular technologies but have become the hope of many individuals for restoring their lost limb function. Decades of research went into making the interface between the human and the machine seamless, but scientists were unable to effectively address the inherent challenges, namely, complexity, adaptability and variability. To overcome the above challenges, it is critical to computationally understand and quantitatively characterize how humans control their senses and motor abilities. Biomimetically inspired models can help to understand this process, and can enable efficient control of the machine. The human hand has many dimensions and is an ideal testbed to understand sensorimotor control while interacting with computers and other machines. Hence the goal of this project is to design and develop biomimetic models that control the human hand and extend these models to the control of multidimensional machines. The societal impacts of the proposed project will be the development of new designs of artificial limbs for individuals with disabilities that are as close to natural in their functions. The educational and outreach impacts of the project will create opportunities for students and working engineers to learn the importance of human machine interfaces. The project will facilitate mentored international research and educational opportunities for students. The hands-on modules developed as an outflow of the proposed research will ignite interest in science and technology among students at all levels, particularly women and underrepresented minorities.

The means by which the central nervous system effortlessly controls the high dimensional human hand is still an unsolved mystery. To address this high dimensional control problem, many bioinspired motor control models have been proposed, one of which is based on synergies. According to this model, instead of controlling individual motor units, central nervous system simplifies the control using coordinated control of groups of motor units called synergies. However, there are several unanswered questions today. Where are synergies present? What is their role in motor control and motor learning? To answer these fundamental questions, this project takes a holistic and comprehensive approach. It combines the concepts of human motor control, computational neuroscience, machine learning and validation with noninvasive human experiments. The research objectives of this project are: to model the generation of synergies in human hand movements and validate the model with noninvasive human experiments using computational models, electroencephalography and transcranial magnetic stimulation, to model the behavior and the role of synergies in motor learning and to apply these synergies in multidimensional machine control and machine-assisted learning.

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.

 

Publications

Please refer to publications page[Link]

 

 

ABSTRACT [Link]

Age-related diseases, neurological disorders, such as stroke, spinal cord injury and traumatic brain injury can have debilitating effects on cognitive-motor function, significantly decreasing quality and length of life. There is a critical need for technologies to effectively address the care and rehabilitation. However, innovation in these neurotechnologies faces several challenges: slow rate of evaluation when compared to invention, lack of standards for safety, efficacy, and long-term reliability, longer time-to-market and lack of affordable technologies. To address these challenges, the University of Maryland, Baltimore County (UMBC) site will partner with Arizona State University (ASU) and the University of Houston (UH) to join a multi-institution Industry/University Cooperative Research Center (IUCRC) for Building Reliable Advances and Innovation in Neurotechnology (BRAIN).

BRAIN's vision is a synergistic, interdisciplinary approach to develop and validate affordable patient-centered technologies. BRAIN will leverage expertise in neurotechnologies at UMBC, UH and ASU to 1) enhance testing and validation of new technologies through industrial partnerships 2) develop standards for technologies using system-level approaches 3) characterize innovative technologies such as biosensors and quantitative analysis tools for systems and behaviors 4) evaluate the impact of these technologies on quality of life and 5) reduce the cost of neurotechnologies.

BRAIN's mission is multifold: to accelerate the progress of science and advance national health by transferring engineering innovations in neurotechnology to the end users, and to rectify underrepresentation in science, technology, engineering, and math (STEM) fields by broadening new participation and retaining current participants in STEM. It also will focus on problems in the neurological space that affect underrepresented groups disproportionately. BRAIN will become an innovative neurotechnology hub, creating a pipeline from discoveries to solutions while helping talented students, scientists, and engineers in the region take their innovations to the next level and solve one of the greatest unmet medical and health care needs of our time.

BRAIN will leverage unique research and development ecosystems with industrial partnerships to design and validate neurotechnologies that can effectively transform the lives of individuals with disabilities. The UMBC IUCRC site will contribute in five primary themes ? cyber human systems, artificial intelligence, neurobionics, nerve repair and regeneration and neuromechanics. The site will bring in expertise in artificial intelligence, big data analytics, cloud computing, computational modeling, neural interfaces, biomechanics, wearable robots, virtual and augmented reality and other noninvasive solutions. The site's data management plan will ensure data integrity, data sharing, and data mining according to institutional and federal guidelines.

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.

The planning meeting was held on Sep 9,2022 in-person at UMBC in collaboration with Georgia Tech connected remotely over webconference. Please see agenda of the event here. Please see photos of the event here.