Utilizing Closed-Loop Physiological Feedback for Dynamic Compression in Soft Robotic Wearables

Orthostatic intolerance (OI) is commonly experienced by pilots during high-G maneuvers, astronauts returning to Earth, and individuals with autonomic-related diseases. OI occurs due to blood pooling in the lower body and associated decrease in cerebral blood flow, and could result in hypotension, dizziness, changes in vision, and potentially syncope. Compression garments are currently used for health benefits involving blood flow, especially in sports, military applications, and older populations. However, current garments, such as passive elastic and inflatable garments, are bulky, they need to be activated by the user, and they typically offer a unique level of compression. This research effort outlines the development of the first prototype of a compact, fully automated compression garment utilizing a closed-loop physiological feedback system to mitigate the severity of an OI event. The closed-loop system involves the use of continuous physiological monitoring, supporting the recognition of OI events. The compression garment also utilizes Shape Memory Alloys (SMAs) as actuators for compression, allowing for the ability to dynamically compress the garment as needed, based on OI symptoms. Our first compression garment prototype successfully collected and utilized real-time physiological data of the user to drive compression when pre-syncope symptoms were present. Compared to legacy compression devices, our design shows that SMAs are an effective replacement to generate adequate compression, allowing for both sustained and dynamic actuation within the garment. Additionally, the ability to dynamically control the compression of the garment means that, under nominal conditions (i.e., not experiencing an OI event), the garment can be deactivated, improving the users' experience and comfort. The preliminary findings of the compression garment are indicative of an effective countermeasure for OI events. Finally, the utilization of the user's biometrics within a closed looped system can be applied in many other applications where environmental factors are unknown or cannot be measured.