Human-Machine Interaction with Mobility Enhancing Soft Exosuits

Abstract:

Stroke is the leading cause of long-term disability in the US with approximately 7 million stroke survivors living in the US today and for patients with neurological disorders, it has been shown that limited gait velocity commonly results in walking that is predominantly restricted to the household. Unlike traditional exoskeletons, which contain rigid linkage elements, the vision for this work is for exosuits that use soft materials such as textiles to provide a more conformal, unobtrusive and compliant means to interface to the human body. This represents a fundamental change in the paradigm of how people have viewed and designed wearable robots for the last half a century. Such a solution would have broad impact beyond the stroke patient population and could provide benefit to children with Cerebral Palsy or elderly individuals with muscle weakness. In addition there are plans to create a set of novel instructional educational toolkits for patient-in-the-loop cyber-physical systems that will be shared via an online portal and the CPS- VO. With a patient-in-the-loop CPS, the patient, the physical suit, the computational control algorithms and the task/environment form a system in which all of the elements need to seamlessly interact. Through a modeling and experimental approach involving extensive human subjects studies, the team propose to create a unified engineering, biomechanical and physiological framework for designing and evaluating patient-in-the-loop CPS that include co- operative controllers that adapt in real-time to the patient to ensure safety and reliability an integrated system.  Specifically the project will seek to gain a fundamental understanding how to (1) analytically and experimentally characterize how forces are transmitted from these soft systems to the patient through the underlying soft tissue so as to generate assistance, (2) apply the optimal magnitude and timing of assistance to the patient to promote a more symmetric and natural gait by monitoring biomechanical, physiological and suit sensor data and (3) fuse information from different sensors monitoring patient motion and interaction forces to create an integrated CPS with a co-operative controller than can adapt to non-periodic movements of the patient.