# Self-Care IoT Platform for Diabetic Mellitus
## 3 The Proposed Self-Care Application to Track the Life-Log with Glucose Measurement  
SMBG-based blood glucose management has inherent limitations. Even if the patient measures blood glucose frequently, it is difficult for the patient to find a sinfl lifestyle or to know how to improve. In other words, since the SMBG results are instantaneous BG levels, it helps to roughly analyze the effect of life patterns on patient blood glucose. It is difficult to know whether a patient has adhered to the correct target blood glucose range during the day, and the detection of hyperglycemia and hypoglycemia is also challenging. To overcome this limitation, we propose a self-care IoT pack that can help patients automatically acquire BG measurements and lifelong that can affect BG levels. The information received from the proposed IoT pack is shown in the proposed self-care program, as shown in Figure 6 in real-time. The information for self-care is mainly divided into five categories, as shown in Figure 6. First, sleep information is obtained from the wearable band, and the proposed platform displays wake up time and bedtime. Secondly, BG measurements are obtained from the glucose meter, indicating the patient's condition at BG measurement time. The conditions field is expressed as pre-and post-prandial blood glucose, fasting and bedtime blood glucose, and pre- and post-exercise blood glucose according to the patient's lifestyle. Third, the exercise is divided into indoor activity using a treadmill or indoor bicycle and aerobic exercise from the outside. Also, the exercise time, distance, and intensity for each exercise are displayed. Fourth, medication indicates the type and amount of drugs taken. Finally, the information about meals is divided into breakfast, lunch, dinner, and snack types. The type and amount of food eaten at each meal are obtained and marked. Figure 5: The proposed dosage information recorder. (BLE: Bluetooth low energy). The proposed platform records in detail all the life patterns that affect a patient's BG levels all day. In particular, the information obtained from each category is correlated to reduce the manual record of the patient. Therefore, the patient may record daily life patterns using the proposed self-care IoT pack automatically. The information is presented to the user in chronological order. Patients can view vital information that affects BG levels, such as sleep, meals, exercise, and metabolism. By providing both BG levels and lifestyle patterns together, it is easy for the patient to understand how life patterns affect blood glucose control throughout the day. Besides, the patient can easily find a good or bad lifestyle for self-management by providing each life pattern with a BG measurement for a specific period, such as one week, one month, or three months. The proposed self-care platform's application program consists of three major parts: initialization, life-log database, and dashboard, as shown in Figure 7.  
First, the initialization part is responsible for registering the patient information and the proposed self-care IoT devices. Then, it links the application to the web-server and all IoT devices as a unified self-care platform. Second, the life-log database is to collect the patient's activities of daily living from various IoT devices. The database is organized separately according to the predefined activities of daily living. This database is based on Google Firebase that provides a real-time database and backend as a service, thereby reducing data management costs. Third, the proposed application offers an intuitive and straightforward dashboard. Patients can obtain their glucose readings visualized in a simple format, see medication adherence levels, exercise, meal progress, and view unusual activities' effects on BG levels. When sending in their measurements, patients receive personalized coaching and tailored responses from the system to track their self-management progress.