An Edge Computing Enabled Internet of Medical Things Device for Remote Patient Health Monitoring Applications

Recent days, Patient Monitoring helps the medical professionals to monitor patient throughout the day. Also, the use of Internet of Medical Things (IoMT) enables this process to be done remotely. IoMT is a subset of Internet of Things, specialized in medical applications. Moreover, Artificial Intelligence (AI) based image classification algorithms can be incorporated to improvise and automate the process, to detect the patients' health state. In this work, an edge computing enabled Internet of Medical Things (IoMT) device is designed and developed for remote health monitoring applications. Further, a less expensive ESP32 CAM device is utilized as an edge computing enabled IoMT device in which a MobileNetV2 version 0.35 model Algorithm is integrated to predict two different patient activities such as asleep and critical conditions. Results demonstrate that the MobileNetV2 version 0.35 model has a better classification accuracy of 97.5%. Also, it is observed that the proposed AI-enabled edge computing device is capable of identifying the patient's conditions and it alerts the physician about the patient's critical conditions using Short Messaging Service (SMS). This work appears to be of high societal relevance since the proposed device helps the physician to monitor the patient's conditions in a remote manner.