Despite the promise of predictive analytics in healthcare, the lack of continuous internal sensing devices has impeded its realization. With the exception of CGMs, no current commercially available wearable devices yield information intimate to the body. To overcome this deficiency, our research group has developed a minimally invasive wearable device capable of continuous monitoring of glucose and electrolytes in the superficial layer of the skin in an extremely minimally invasive manner.

This technology, capable of continuously acquiring blood chemistry information, could augment the functions of currently existing activity sensors to enable the continuous measurement of both internal and external signals to better monitor chronic conditions, such as diabetes and arrhythmias, as well as facilitate the use of predictive analytics and preventative medicine. It is hypothesised that this convergent, multiplexed data could be used in predictive analytics platforms to uncover the onset of potentially hazardous conditions, to enable immediate restoration to the optimal health conditions, and essentially to prevent the onset of serious illnesses.

Aim: To acquire data to build a database of data to facilitate the development of algorithms for predictive analytics to uncover new applications.

One selected candidate will receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

  • Professor Sam Sia
  • Department/School: BME/SEAS
  • Location: CUMC

Project timeline

  • Earliest starting date: 06/01/2019
  • End date: 08/31/2019
  • Number of hours per week of research expected during Summer 2019: ~40

Candidate requirements

  • Skill sets: Machine Learning
  • Student eligibility (as of Spring 2019): freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Other comments: Interest in health information, continuous data sets.