Usage of Moving Average to Heart Rate, Blood Pressure and Blood Sugar

In today's world, many people use wearable devices (like smartwatches) or health apps to keep track of important health signs like heart rate, blood pressure, and blood sugar levels. These signs change over time, and sometimes unusual changes (called anomalies) can be an early warning of a health problem. To catch these problems early, we need a method that can monitor the data continuously and detect when something is wrong. This thesis focuses on using a simple method called the Simple Moving Average (SMA). It works by taking the average of recent values to smooth out short-term changes, helping to highlight real trends or sudden shifts. The SMA is easy to use and works well on devices with limited computing power (like small sensors or wearables). This research shows how SMA can help detect abnormal patterns in health data, which could alert doctors or users in real time. In short, this study shows that even a basic algorithm like SMA can be useful for monitoring health and spotting early signs of trouble, especially when used in real-time systems.