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：血糖水平监测在医学领域具有重要意义，尤其对于1型糖尿病患者而言，准确监测其血糖水平可有效预防可能出现的危险及危及生命的事件。此外，通过长时间精确监测血糖水平，有助于建立关于每日进食规律的知识体系，从而辅助医疗人员监测患者并在低血糖或高血糖等危险情况下进行适当干预。建立此类知识体系将为设计合理的治疗干预方案发挥重要作用。本研究提出了一套完整的物联网框架，涵盖从硬件采集系统到数据分析方法，为需要长期血糖监测患者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（可实现准实时监测与分析）的真实数据采集进行验证。结果显示，7名受试者中有4人的每日进食规律得到精确估算（其余3人主要因大量数据缺失被剔除）。这4名受试者的每日进食规律呈现为4个血糖水平变化模式：早餐约在上午8点、午餐约在下午5点、晚餐约在晚上8点，以及白天加餐约在中午12点。本研究展示了物联网生态系统在支持医学相关研究方面的潜力。