SenSE: Multimodal noninvasive wearable sensors and machine learning for predicting critical glycemic events

提案标题：SenSE：多模态非侵入性可穿戴传感器和机器学习用于预测关键血糖事件NSF-2037383：Coté，Gutierrez，埃拉贡特拉，McKay和DeSalvo非技术摘要：本研究的主要目标是开发一种可穿戴传感器系统，使糖尿病患者能够在无需抽血的情况下监测血糖（糖）水平。 该方法依赖于测量当血糖偏离其正常范围时发生的生理信号的细微变化。预测这些偏差对于糖尿病管理至关重要，因为低血糖症（低血糖）可能在短期内造成严重的健康后果，而高血糖症（高血糖）则会造成长期的健康后果。 这些信号是使用六种不同的传感器测量的，所有传感器都在可穿戴臂带上，并与机器学习算法相结合，以预测血糖水平，并帮助患者保持在正常血糖范围内。这种可穿戴系统最终可以用于预测和监测其他不良健康结果，如心脏问题（例如，充血性心力衰竭或高血压）。 该项目还为研究生和本科生提供研究培训。该研究小组将与大学项目合作，以扩大科学和工程的参与。此外，来自服务不足和代表性不足地区的高中学生和教师将参与大学实验室研究，以激发他们对科学和工程的兴趣。技术摘要：该项目旨在开发一种多模式可穿戴传感平台和机器学习算法，可用于非侵入性预测血糖事件。 管理糖尿病需要平衡与高血糖相关的糖尿病并发症的长期风险（失明、肾衰竭、截肢、中风和心脏病）和低血糖的急性风险（癫痫发作、昏迷或死亡）。 然而，目前，这只能通过连续葡萄糖监测仪（CGM）来实现，其昂贵、侵入性，并且通常仅对5%的糖尿病患者（1型）开处方。 因此，该项目的具体目标是：1）开发非侵入式传感器的多模式系统，包括六个传感器的最佳放置和集成; 2）开发血糖事件的预测模型，包括能够预测未来低血糖事件并表征高血糖范围内时间的统计和深度学习模型;和3）人类受试者的表征和验证，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

Proposal Title:SenSE: Multimodal noninvasive wearable sensors and machine learning for predicting critical glycemic eventsNSF-2037383: Coté, Gutierrez, Erraguntla, McKay and DeSalvoNon-technical Abstract:The main objective of this research is to develop a wearable sensor system that allows people with diabetes to monitor their blood glucose (sugar) levels without having to draw blood. The approach relies on measuring subtle changes in physiological signals that occur when blood glucose deviates from its normal range. Predicting these deviations is critical for diabetes management, as hypoglycemia (low blood glucose) can have severe health consequences in the short term, and hyperglycemia (high blood glucose) for the long term. The signals are measured using six different sensors, all on a wearable arm band, and are combined with machine-learning algorithms to predict sugar levels and help keep patients in the normal blood sugar range. This wearable system could eventually be used to predict and monitor other adverse health outcomes such as heart problems (e.g., congestive heart failure or hypertension). This project also provides research training to diverse students at the graduate and undergraduate level. The research team will work with the university programs to broaden participation in science and engineering. In addition, high school students and teachers from underserved and underrepresented areas will be involved to work on university-based laboratory research to excite them about science and engineering. Technical Abstract:This project seeks to develop a multimodal wearable sensing platform and machine learning algorithms that can be used to predict glycemic events non-invasively. Managing diabetes requires balancing the long-term risks of diabetes complication related to hyperglycemia (blindness, kidney failure, amputation, stroke, and heart disease) and the acute risks of hypoglycemia (seizure, coma, or death). At present, however, this can only be achieved with continuous glucose monitors (CGMs), which are expensive, invasive, and typically only prescribed to 5% of the patients with diabetes (type 1). Thus, the specific aims of this project are to: 1) Develop a multimodal system of non-invasive sensors including optimal placement and integration of six sensors; 2) Develop predictive models of glycemic events including statistical and deep-learning models capable of predicting future hypoglycemic events and characterize time in hyperglycemic range; and 3) Characterization and validation with human subjects, including two studies to be conducted in the clinic and at home.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.