The number of people is going up really fast. This means we need to come up with ways to take care of people health. We want to make sure people can still move around freely. This paper is about a patient monitoring system that uses the Internet of Things. The patient monitoring system is special because it can detect if someone falls down and it can also track what is going on inside the body. The system is better than systems that relied on the cloud. Those old systems were slow. Used a lot of bandwidth. The new patient monitoring system uses something called Edge Analytics. This means that the system can process information from sensors right where the information is collected than sending it somewhere else. The system uses a combination of sensors that track movement and vital signs. It has a machine learning algorithm that helps tell the difference between everyday activities and real falls. The system looks at the data away and decides what is important. This helps get emergency responders to people 40 percent faster than other systems that use the cloud. Tests show that the system is really good at detecting falls and sending heart rate and oxygen level data using the MQTT [11] protocol. The system is good, at sending this information like heart rate and (SpO_2) data when it needs to. This research highlights the effectiveness of decentralized IoT frameworks in enhancing patient safety, ensuring data privacy, and providing caregivers with a robust, low-power solution for remote health management. The main part of this system is the way it handles data. It looks at the data in an order and does things right away when it gets the data. When you are wearing the device it is always checking how you are moving and how you are standing. It does this often. The device does not just send all the information to a computer else. It actually thinks about the information it gets. It takes the information about how you're moving and turns it into patterns of what you are doing. The wearable device is really good at understanding what is going on with the patient.
Internet of Things (IoT), Edge Analytics, Real-Time Fall Detection, Machine Learning, Wearable Sensors, MQTT, Patient Monitoring, TinyML, Signal Vector Magnitude (SVM), Elderly Care.
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