Enhancing activity monitoring of smart homes using IOT sensors

Abstract

Accurate recognition of daily activities is vital for applications in healthcare, fitness tracking, and assisted living. Wrist-worn accelerometers, embedded in smartwatches and fitness trackers, provide an effective means of continuously monitoring user movements. Therefore this project explores the application of machine learning (ML) techniques to analyze accelerometer data from wrist-worn devices for precise daily activity recognition