Blood glucose level monitoring is of great importance, especially for subjects experiencing type 1 diabetes. Accurate monitoring of their blood glucose level prevents dangerous and life-threatening situations that might be experienced by those subjects. In addition, precise monitoring of blood glucose levels over long periods of time helps establishing knowledge about the daily mealtime routine which aids the medical staff to monitor subjects and properly intervene in hazardous cases such as hypo- or hyperglycemia. Establishing such knowledge will play a potential role when designing proper treatment intervention plan. In this research, we present a complete IoT framework, starting from hardware acquisition system to data analysis approaches that gives a hand for medical staff when long periods of blood glucose monitoring are essential for subjects. Also, this framework is validated with real-time data collection from 7 subjects over 10 successive days with temporal resolution of 5 minutes allowing for near real-time monitoring and analysis. Our results show the precisely estimated daily mealtime routines for 4 subjects out of the 7 with discard of 3 subjects due to huge data loss mainly. The daily mealtime routines for the 4 subjects are found to be matching to have a pattern of 4 periods of blood glucose level changes corresponding to the breakfast around 8 AM, the lunch around 5 PM, the dinner around 8 PM, and finally a within-day snack around 12 PM. The research shows the potential of IoT ecosystem in support for medically related studies.

翻译：血糖水平监测具有重要意义，尤其对于患有1型糖尿病的个体。准确的血糖监测可预防这些个体可能面临的危险及危及生命的情况。此外，长期精确监测血糖水平有助于建立日常进餐规律知识体系，这将辅助医疗人员监测患者并针对低血糖或高血糖等危险情况实施适当干预。建立此类知识对于制定合理的治疗方案具有潜在价值。本研究提出完整物联网框架——涵盖从硬件采集系统到数据分析方法的全流程，为需要长期血糖监测的医疗人员提供支持。该框架通过7名受试者连续10天（时间分辨率为5分钟）实时数据采集进行验证，实现近实时监测与分析。研究结果显示，7名受试者中有4名精确估算出日常进餐规律，另外3名因数据大量缺失被排除。4名受试者的日常进餐规律呈现4个血糖水平变化时段模式：早餐（约上午8点）、午餐（约下午5点）、晚餐（约晚8点）及日内零食（约中午12点）。研究表明物联网生态系统在支持医学相关研究方面具有潜力。