Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

Sensors (Basel). 2021 Oct 21;21(21):6989. doi: 10.3390/s21216989.


Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

Keywords: diabetes; metabolic heat production; multisensor fusion; noninvasive glucose concentration detection; regression model; wrist.

MeSH terms

  • Blood Glucose Self-Monitoring
  • Blood Glucose*
  • Energy Metabolism
  • Glucose*
  • Humans
  • Machine Learning


  • Blood Glucose
  • Glucose