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We develop novel, wearable devices for monitoring human health such as blood glucose level and calorie expenditure.

The Bioelectronics Lab has proposed to adapt ISO 7933 to provide an inverse Predicted Heat Strain Model, which treats calorie expenditure as the unknown value to be solved. The inverse PHSM requires measurements of environmental conditions, sweat rate, body temperature, and speed of movement. We develop a wearable device and smart-phone application which will acquire the necessary data for the inverse PHSM.

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Diabetes is a major global health problem, affecting approximately 400 million people on the world. Physicians encourage diabetics to use household blood glucose meters to monitor themselves, preferably several times a day.  Pre-diabetics may also benefit from blood glucose monitoring.  Early intervention can help prevent future diabetes.


Existing blood glucose monitoring technology requires a sample of blood which is obtained with a needle puncture.  Most people find this procedure painful, and so they do not take frequent measurements.


We develop two method to measure the blood glucose noninvasively:

  1. Changes in the intensity of infrared light radiating from the body at wavelengths which corresponded to glucose absorptions can be observed and used to measure glucose.
  2. Raman scattering, can slightly but precisely alter the color of a small fraction of laser light when it interacts with glucose.

The Bioelectronics laboratory is developing miniaturized versions of both the infrared absorbance device and the Raman spectrophotometry device.  Preliminary testing on human subjects has been approved and is expected to begin soon.



  • Noninvasive Glucose Monitoring By Mid-infrared Self-emission Method - View the Non-invasive Glucose Monitoring Poster

  • Xin Zhao and Schmidt. J. Dominik, Tunable Fabry-Perot filter for optical glucose monitoring, Conference of Biodevice,  2014,France
  • Sheng Yang, Yen-Chun Yeh, John J. Ladasky and Dominik J. Schmidt, Algorithm to calculate human calorie expenditure based on a predicted heat strain model, the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics

  • Ming Li, Hao Qin, and May Huang, RGB-D Image-based Pose Estimation with Monte Carlo Localization, 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017, Japan