Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok-si 25913, Republic of Korea.
Department of Computer Engineering, Kangwon National University, Samcheok-si 25913, Republic of Korea.
Sensors (Basel). 2023 Feb 2;23(3):1656. doi: 10.3390/s23031656.
The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%).
2 型糖尿病(T2DM)的管理通常不仅侧重于药物治疗。由于多种原因,医学营养疗法经常被患者遗忘,例如难以确定适合自己的正确营养模式、调节日常营养模式,甚至不注意医生给出的营养饮食建议。营养疗法的管理是糖尿病患者可以预防疾病复杂化的重要努力之一。制定适当营养的饮食计划将有助于患者管理健康饮食。智能餐盘健康饮食的开发是一项技术创新,可帮助患者和用户了解特定食物的类型、重量和营养成分。这项研究涉及 50 种食物,总共 30800 种食物,使用了 YOLOv5s 算法,其中使用 Chenbo 称重传感器(1kg)、HX711 称重 A/D 模块压力传感器和 IMX219-160 相机模块(waveshare)对食物的识别、重量测量和营养成分进行了调查。研究结果表明,在对四种食物(米饭(58%)、酱油焖鹌鹑蛋(60%)、麻辣牛肉汤(62%)和萝卜干(31%))的分析中,识别精度较高,重量和营养成分的精度均为 100%。