People around the globe rely on their blood samples for their glucose level measurement. There is a demand for non-invasive, precise and cost-effective solutions to monitor blood glucose level and control of diabetes. Serum glucose is an accurate blood glucose measurement method in comparison to capillary glucose measurement. Presently, the serum glucose is measured through laboratory setup with an invasive approach. The invasive method is painful and is not suitable for continuous glucose measurement. In this paper, we propose a novel wearable non-invasive consumer device (called iGLU 2.0) which can be used by consumers for accurate continuous blood glucose monitoring. This device uses a novel short near infrared (NIR) spectroscopy developed by us. It is incorporated with Internet-of-Medical-Things (IoMT) for smart healthcare where the healthcare data is stored on the cloud and is accessible to the users and caregivers. Analysis of the optimized regression model is performed and the system is calibrated and validated through healthy, prediabetic and diabetic patients. The robust regression models of serum glucose level is then deployed as the mechanism for precise measurement in iGLU 2.0. The performance of iGLU 2.0 is validated with the prediction of capillary blood glucose using Average Error (AvgE) and Mean Absolute Relative Difference (mARD) which are calculated as 6.09% and 6.07%, respectively, whereas for serum glucose, AvgE and mARD are estimated as 4.88% and 4.86%, respectively.

中文翻译：

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iGLU 2.0：一种用于在 IoMT 框架中进行准确无创连续血清葡萄糖测量的新型可穿戴设备

世界各地的人们依靠他们的血液样本来测量血糖水平。需要非侵入性、精确且具有成本效益的解决方案来监测血糖水平和控制糖尿病。与毛细血管葡萄糖测量相比，血清葡萄糖是一种准确的血糖测量方法。目前，血清葡萄糖是通过实验室设置以侵入性方法测量的。侵入式方法痛苦，不适合连续测量血糖。在本文中，我们提出了一种新型可穿戴非侵入式消费设备（称为 iGLU 2.0），消费者可以使用它进行准确的连续血糖监测。该设备使用我们开发的新型短近红外 (NIR) 光谱。它与医疗物联网 (IoMT) 相结合，用于智能医疗保健，其中医疗保健数据存储在云中，可供用户和护理人员访问。对优化的回归模型进行分析，并通过健康、糖尿病前期和糖尿病患者对系统进行校准和验证。然后将血清葡萄糖水平的稳健回归模型部署为 iGLU 2.0 中精确测量的机制。iGLU 2.0 的性能通过使用平均误差 (AvgE) 和平均绝对相对差 (mARD) 的毛细血管血糖预测得到验证，分别计算为 6.09% 和 6.07%，而对于血清葡萄糖，AvgE 和 mARD 是估计值分别为 4.88% 和 4.86%。对优化的回归模型进行分析，并通过健康、糖尿病前期和糖尿病患者对系统进行校准和验证。然后将血清葡萄糖水平的稳健回归模型部署为 iGLU 2.0 中精确测量的机制。iGLU 2.0 的性能通过使用平均误差 (AvgE) 和平均绝对相对差 (mARD) 的毛细血管血糖预测得到验证，分别计算为 6.09% 和 6.07%，而对于血清葡萄糖，AvgE 和 mARD 是估计的分别为 4.88% 和 4.86%。对优化的回归模型进行分析，并通过健康、糖尿病前期和糖尿病患者对系统进行校准和验证。然后将血清葡萄糖水平的稳健回归模型部署为 iGLU 2.0 中精确测量的机制。iGLU 2.0 的性能通过使用平均误差 (AvgE) 和平均绝对相对差 (mARD) 的毛细血管血糖预测得到验证，分别计算为 6.09% 和 6.07%，而对于血清葡萄糖，AvgE 和 mARD 是估计的分别为 4.88% 和 4.86%。