Smart Wearable Embedded Systems for Human Activity Recognition (HAR) Using Edge Computing

The medical industry is currently utilizing a significant number of automated tools. The hardware is examined and used on both healthy individuals and those who have Covid-19 to capture breathing data, and a correlation has been found between them. A person's gait can be determined. This study also uses Kodular app, ESP32 module and ADXL335 Accelerometer sensor as an apparatus for detecting the changes. There are no complicated components or wiring needed for the ADXL335 accelerometer circuit, making it simple. It has a direct connection to an Arduino. The spread of these wearable gadgets increases the variety of their applications. With the development of wearable technologies that can process and store enormous amounts of health data, continuous human body monitoring is becoming more common. There are numerous technologies that enable the capture of movement among them. Accelerometers are the most frequently cited because they are already found in many devices, use little power, and cost little, theyhave been studied by researchers. and produce outstanding results. The fall detection system can be used in elderly fall detection system, monitoring the hospital patients, wheelchair person falls, etc. Finding anomalies in the large datasets that can be utilized to calculate the respiration rate is the study's aim. The designed system can be further used in many other applications where a fall need to be detected or where a fall is needed.