The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains . In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals . Several previous studies made use of wearable devices to detect medication behaviors of patients . However, the wearable devices cause inconvenience while equipping the devices . In addition, they suffer from inconsistency problems due to errors of measured values . We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients . Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations . The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices . To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions . Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments . Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.
Index: IoT , deep learning , healthcare , medication, monitoring