Sensor-rich physical activity recognition for blood glucose prediction in people with type 1 diabetes

富含传感器的身体活动识别可预测 1 型糖尿病患者的血糖

• 批准号: 2452249
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2452249
• 关键词: Sensor; rich; physical; activity; recognition

Hybrid closed-loop artificial pancreas systems are the current state of the art of diabetes technology [1]. These systems reduce the burden placed on those with diabetes by performing some of the processing required in diabetes management. However, to reach the next goal of diabetes technology, the fully closed-loop system, there are still challenges and a significant one of these is around management during physical activity. The impact of physical activity on blood glucose levels is not fully understood, although it is thought to vary noticeably between individuals and activity type [2]. This project aims to explore the potential of using additional sensor data, such as heart rate or accelerometer readings, during physical activity to improve the prediction of future blood glucose. This process will begin with a mixed method user study in which participants with type 1 diabetes will be recruited and questioned to understand: - Which medical devices and strategies they use to manage their diabetes. - How they currently manage physical activity. - What additional sensing equipment they would find acceptable to use during activity.This is to ensure that the algorithm developed is usable and useful to people and to gain an understanding of the nuances of blood glucose management that people with diabetes experience during physical activity. The link between physical activity and blood glucose levels will be explored through the user study, existing literature and available datasets. As part of this current glucose prediction algorithms will be investigated to inform the approach taken next.The next stage will be then developing an algorithm that takes the additional data and predicts blood glucose in the future. Machine learning techniques will be utilised to build this algorithm, with consideration taken into how it can be personalised to individuals. The OhioT1DM Dataset appears to contain potentially useful data and may form a starting point for this algorithm [3]. However, building a dataset of blood glucose, insulin dosage and activity sensor data may be necessary for the project and will form a useful contribution to the wider research topic. This will be especially so if data can be collected of multiple people performing the same exercise to compare their blood glucose response.Further stages of the project will be driven by areas of interest that arise during the earlier stages and directly by the users involved in this project.The link between physical activity and blood glucose levels is not fully understood and the work here aims to contribute its understanding. There is also currently limited use of additional sensor data in closed-loop control algorithms, bringing novelty to the research. However, several papers do mention similar ideas, suggesting that there are other researchers currently looking into it. Additionally, having a user-driven approach to the design of closed-loop algorithms will produce novel HCI contributions.References[1] Weaver KW, Hirsch IB. The Hybrid Closed-Loop System: Evolution and Practical Applications. Diabetes Technol Ther. 2018;20(S2):S216-S223. doi:10.1089/dia.2018.0091[2] Colberg SR, Hernandez MJ. The big blue test: effects of 14 minutes of physical activity on blood glucose levels. Diabetes Care. 2013;36(2):e21. doi:10.2337/dc12-1671[3] Marling C, Bunescu R. The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020. CEUR Workshop Proc. 2020;2675:71-74.

混合闭环人工胰腺系统是糖尿病技术的当前发展水平[1]。这些系统通过执行糖尿病管理所需的一些处理来减轻糖尿病患者的负担。然而，为了实现糖尿病技术的下一个目标，即完全闭环系统，仍然存在挑战，其中一个重要的挑战是体力活动期间的管理。身体活动对血糖水平的影响尚未完全了解，尽管人们认为个体和活动类型之间存在显著差异[2]。该项目旨在探索在身体活动期间使用额外传感器数据（如心率或加速度计读数）的潜力，以改善对未来血糖的预测。这一过程将开始与混合方法的用户研究，其中1型糖尿病参与者将被招募和询问，以了解：-他们使用哪些医疗设备和策略来管理他们的糖尿病。- 他们目前如何管理身体活动。- 他们认为在活动期间可以使用哪些额外的传感设备。这是为了确保开发的算法对人们可用且有用，并了解糖尿病患者在体力活动期间经历的血糖管理的细微差别。将通过用户研究、现有文献和可用数据集探索体力活动与血糖水平之间的联系。作为该研究的一部分，将研究当前的血糖预测算法，以告知下一步采取的方法。下一阶段将开发一种算法，该算法将获取额外的数据并预测未来的血糖。机器学习技术将用于构建该算法，并考虑如何将其个性化。OhioT 1DM数据集似乎包含潜在有用的数据，并可能构成该算法的起点[3]。然而，建立一个血糖，胰岛素剂量和活动传感器数据的数据集可能是该项目所必需的，并将对更广泛的研究课题做出有益的贡献。如果可以收集多个人进行相同运动的数据，以比较他们的血糖反应，则更是如此。项目的进一步阶段将由早期阶段出现的感兴趣领域驱动，并直接由参与该项目的用户驱动。身体活动和血糖水平之间的联系尚未完全了解，这里的工作旨在促进对它的理解。目前在闭环控制算法中使用额外的传感器数据也很有限，这给研究带来了新奇。然而，有几篇论文确实提到了类似的想法，这表明目前有其他研究人员正在研究它。此外，使用用户驱动的方法来设计闭环算法将产生新的HCI贡献。参考文献[1] Weaver KW，Hirsch IB.混合闭环系统：发展和实际应用。糖尿病技术治疗2018;20（S2）：S216-S223。doi：10.1089/dia.2018.0091[2] Colberg SR，埃尔南德斯MJ.蓝色大测试：14分钟的体力活动对血糖水平的影响。糖尿病护理。2013;36（2）：e21. doi：10.2337/dc12-1671[3] Marling C，Bunescu R. OhioT 1DM血糖水平预测数据集：2020年更新。CEUR研讨会程序2020;2675：71-74。