Science Principal Investigator: Donghyeon Ryu, Ph.D., New Mexico Institute of Mining and Technology

The proposed project aims at providing non-invasive and self-learning health monitoring solutions for early diagnosis of health disorders of NASA astronauts during space exploration by detecting subtle changes in the astronauts’ daily behavioral and sleep patterns. It is essential to keep the NASA astronauts informed about their fitness and health in space by minimally invasive and intrusive means. The health monitoring wearable is considered the most promising solution for accomplishing this goal.

State-of-the-art health monitoring wearables are not well suited for monitoring astronauts’ health during space mission operations due to batteries needing to be recharged regularly and extreme conditions of space. The Science Investigator, Dr. Donghyeon Ryu, and the co-investigators propose to innovate the health monitoring wearables by developing a self-powered sensing fiber and a machine-learning (M-L)-based health diagnosis platform. In the previous awarded NASA EPSCoR CAN project, Dr. Ryu invented the multifunctional mechano-luminescenceoptoelectronic (MLO) composites, The MLO was shown to generate direct current (DC) when subjected to external mechanical stimuli through the mechanical-radiant-electrical (MRE) energy conversion mechanism. The MRE energy conversion is enabled by the two functional building blocks (i.e., the mechano-luminescent composites generating the light
under the strain and the mechano-optoelectronic thin films generating the electricity using the light) integrated in the MLO design platform. The DC generated from the MLO was shown to vary in magnitude with the applied strain. In addition, the generated DC can be used as an electrical energy source.

In order to accomplish the development of the non-invasive and self-learning health monitoring wearables using the self-powered MLO sensing fibers, the team will focus on three research tasks to: 1) develop the hybrid manufacturing technique for the MLO fiber, 2) design microstructures of the functional building blocks to optimize the sensing capability, 3) create a non-invasive and self-learning
health monitoring platform.