SenSE: Closed-loop Artificial Pancreas with Noninvasive Monitoring of Glucose and Diet

摘要标题：SenSE：无创监测血糖和饮食的闭环人工胰腺提案编号：2037383主要研究者：Xia Zhou，Gregory Forlenza，Qiang Liu，Temiloluwa Prioleau& Tam Vu机构：达特茅斯学院，科罗拉多大学&德克萨斯大学非技术摘要：1型糖尿病影响着美国超过125万人，其发病率每年都在增加。大多数1型糖尿病患者无法成功达到目标血糖水平，尽管近年来技术的使用不断扩大，但血糖控制实际上一直在恶化。糖尿病控制不佳是失明、肾衰竭、心脏病发作和周围血管疾病的主要原因，这些疾病可能导致截肢，在某些情况下甚至死亡。联合使用动态血糖监测仪和胰岛素泵被认为是目前管理1型糖尿病的金标准。然而，现有技术受到两个公认的限制：由于需要手动“进餐通知”来估计胰岛素剂量而导致的高用户负担，以及由于不断地破坏皮肤以插入传感器的不便和来自传感器粘合剂的刺激而导致的对葡萄糖监测设备的低粘附性。因此，有一个迫切需要在公共卫生的全自动，闭环血糖控制的1型糖尿病患者，这将是重点的拟议research.Technical摘要：拟议的工作旨在开发和试点的联合多模态传感系统，可以提供连续的非侵入性葡萄糖监测数据，除了自动化的膳食检测与膳食摄入量的估计。这两种类型的信息对于告知人工胰腺算法以预测推注（即短效）胰岛素剂量需求是必要的。更具体地说，所提出的系统是以耳戴式设备的形式，解决关键系统和算法挑战，以实现完全闭环的人工胰腺系统。拟议工作的知识价值如下。首先，它将开发新的系统设计，通过融合三个信号流（即，第二，它将通过解决皮肤散射的挑战和减轻混杂因素和用户多样性的影响来推进现有的基于光学的无创葡萄糖监测;第三，它将展示新的节能机器学习模型，致力于在微控制器上实现本地执行，处理有限数量的标记训练数据，确保数据隐私和通信效率;最后，它将表征和验证与糖尿病管理最相关的生物和行为生物标志物，用于实施人工胰腺系统，并开发自适应采样和噪声处理的新方法，以确保准确监测目标事件。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来提供支持。

Abstract Title:SenSE: Closed-loop Artificial Pancreas with Noninvasive Monitoring of Glucose and DietProposal Number: 2037383Principal Investigator: Xia Zhou, Gregory Forlenza, Qiang Liu, Temiloluwa Prioleau & Tam VuInstitutions: Dartmouth College, University of Colorado & University of TexasNon-technical Abstract:Type-1 diabetes affects over 1.25 million people in the United States and its incidence is increasing every year. The majority of type 1 patients are not successful in achieving target blood glucose levels and control has actually been worsening in recent years despite expanded use of technologies. Inadequately controlled diabetes is the leading cause of blindness, kidney failure, heart attack and peripheral vascular disease possibly leading to amputation and in some cases death. Joint use of a continuous glucose monitor and insulin pump is considered the current gold standard for management of type-1 diabetes. Existing technologies, however, suffer from two well-established limitations: high user burden due to the need for manual “meal announcements” to estimate insulin doses and low adherence to glucose monitoring devices due to the inconvenience of constantly breaking the skin for sensor insertion and irritation from the sensor adhesive. Thus, there is an urgent need in public health for fully automated, closed-loop glycemic control for type-1 diabetes patients, which will be the focus of the proposed research.Technical Abstract:The proposed work aims to develop and pilot a joint multimodal sensing system that can provide continuous noninvasive glucose monitoring data in addition to automated meal detection with an estimate of meal intake. Both types of information are necessary to inform artificial pancreas algorithms for prediction of bolus (i.e. short-acting) insulin dose needs. More specifically, the proposed system is in the form of an ear-worn device, tackling key systems and algorithmic challenges to realize a fully closed-loop artificial pancreas system. The intellectual merits of the proposed work are following. First, it will develop novel system designs to realize fine-grained, robust dietary sensing by fusing three streams of signals (i.e., bio-electrical signals, motion, and acoustic signals); Second, it will advance existing optical-based noninvasive glucose monitoring by addressing challenges of skin scattering and mitigating the impact of confounding factors and user diversity; Third, it will present new energy-efficient machine learning models dedicated to enabling local execution at micro-controllers, dealing with the limited number of labeled training data, and ensuring data privacy and communication efficiency; Finally, it will characterize and validate biological and behavioral biomarkers most relevant to diabetes management for implementing an artificial pancreas systems and develop new approaches for adaptive sampling and noise handling to ensure accurate monitoring of target events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.