Yang, Rui and Wu, Yingnian and Liu, Xiaolong and Chen, Wenbai (2022) GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514
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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.
Item Type: | Article |
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Subjects: | OA Library Press > Computer Science |
Depositing User: | Unnamed user with email support@oalibrarypress.com |
Date Deposited: | 14 Jun 2023 07:46 |
Last Modified: | 16 Sep 2024 10:08 |
URI: | http://archive.submissionwrite.com/id/eprint/1194 |