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.
