Computational spectroscopy and imaging for mHealth
|Interdisciplinary Areas:||Internet of Things and Cyber Physical Systems, Engineering and Healthcare/Medicine/Biology, Data/Information/Computation
Our approach for next-generation mobile health (mHealth) is centered in developing simple yet reliable technologies, in which data-driven approaches minimize complicated hardware components or avoid additional attachments to smartphones, and integrating such mHealth technologies into existing EHR systems. From a practical standpoint, it would not be possible to incorporate a bulky optical component in a conventional smartphone. In this respect, computational spectrometry and imaging can play an important role in facilitating the development of mHealth technologies that can be embedded into conventional smartphones, without any additional attachments. For example, if hyperspectral imaging is possible simply using a conventional camera, our smartphone could offer unprecedented widespread access to individual health monitoring. By successful completion, such a computational method does not require any accessory attachments to the smartphone can potentially be scaled up to be integrated with EHR systems. We further envision that such technologies will allow us to leverage big clinical data sources (e.g. generated from EHR systems) and advanced data science tools (regression, compressive sensing, and machine learning) to improving health care and management for patients in resources limited settings, such as low- and middle-income counties and home settings.
March or April 2019
- Solid background and hands-on experience in sparse optimization, compressive sensing, and machine learning.
- Solid background and hands-on experience in optical imaging and photonics.
Young L. Kim, PhD,
Weldon School of Engineering http://web.ics.purdue.edu/~kim50/publication.htm
Paul M. Griffin, PhD
Regenstrief Center for Healthcare Engineering and Industrial Engineering
1. T. Kim, M.A. Visbal-Onufrak, R.L. Konger, and Y.L. Kim, "Data-driven imaging of tissue inflammation using RGB-based hyperspectral reconstruction toward personal monitoring of dermatologic health", Biomedical Optics Express 8:5282-5296, 2017.
Optics 21: 107001, 2016.