Analyzing Patient Motion and Brain Activity with Machine Learning and Internet of Things

Abstract

Improvements in wireless technology have helped a variety of medical professions. There are a lot of places out there that use a pulse monitor, but all they do is record the information that comes out of it. But, it is not only difficult but also costly to acquire access to sophisticated medical gear. The suggested real-time heart rate monitoring system uses a web server and an Arduino microcontroller to link the heart rate to the internet, retrieve heart rate values, store them in a cloud service, and alert the treating physician. The filter bank that was developed by extrapolating heart rates from BBIs shows the extrapolated heart rates as zero crossings in the output signal. Before alerting medical personnel, an algorithm assesses the reliability of the observed heart rate in describing the patient’s condition. Extract BBIs are then used to derive HRV features. Once a threat has been recognised, a notification is sent to the appropriate expert as soon as possible. The patient’s movements are tracked using an infrared (IR) sensor linked to an Arduino microcontroller, and the controller or specialist is alerted to any abnormalities via a buzzer. The proposed framework is used by a variety of customers, not just the informed expert (User agreeable). By employing this method, you can expect precise, speedy, and cheap outcomes. This procedure will be utilised in the development of an Arduinobased Heart Rate Monitor System with a Heartbeat Sensor. By contrasting the results with those obtained by analysing an ECG signal with an oscilloscope and by manually calculating the heart rate, the show reveals a far more beneficial setup.