Abstract
This paper presents an architecture design for a patient monitoring system integrated with Internet of Things (IoT) technology to detect and quantify patient stress levels. Research in remote patient prediction systems is considered one of the most important areas at present. This technology offers the potential to improve stress assessment, provide interventional treatment, and provide personalized stress management techniques. A Raspberry Pi microcontroller was used as a key controller. The unit is equipped with electroencephalography sensors, electrocardiogram sensors, glucose sensors, and electromyography sensors to record physiological signals indicative of stress, such as cardiac activity and human brain activity, a method for monitoring blood glucose levels in diabetic patients and measuring electrical activity. Muscles are collected from these four sensors and transmit information via communication channels (Wi-Fi, USB). The information obtained is transferred to a storage database, where patient data is securely stored. In the storage database, interaction between the patient and the doctor occurs via a 4G communication channel. Data is transmitted via a 4G communication channel from the storage database to the doctor’s personal computer. From the doctor’s personal computer, data is transferred to the doctor’s control panel, and from there the data is transferred to a web server, where all data is processed and the patient is monitored. In the course of research, it was found that the proposed device has 95% reliability in measuring cardiac activity and human brain activity, a method for monitoring blood glucose levels in patients with diabetes and measuring the electrical activity of muscles.