GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection

ABSTRACT

Diabetes is a disease that requires long-term monitoring, and noninvasive glucose detection effectively reduces patient self-monitor resistance. Traditional noninvasive blood glucose methods are limited by many aspects, such as equipment, environment, and safety, which are not suitable for practical use. Aiming at this problem, propose a lightweight network called Group Asymmetric Convolution Shuffle Network (GACSNet) for noninvasive blood glucose detection: use infrared imaging to acquire human metabolic heat and construct a dataset, combine asymmetric convolution with channel shuffle unit, the novel convolution neural network is designed, and extract metabolic heat and cool-heat deviation features in thermal imaging. The test set was analyzed and compared using Clarke’s error grid. The current neural network showed an mean absolute percentage error of 9.17%, with a training time of 2 min 54 s and a inference time of 1.35 ms, which was superior to several traditional convolution neural networks’ accuracy, training cost, and real-time performance in the blood glucose region 3.9–9 mmol/L, and provided new insights into noninvasive blood glucose detection.

Acknowledgments

This research was funded by Beijing Natural Science Foundation (4202026); and Qin Xin Talents Cultivation Program, Beijing Information Science & Technology University (QXTCP A202102).

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Beijing Natural Science Foundation [4202026]; Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University [QXTCP A202102].