Smart Watch with Machine Learning Based Fall Detection System

The goal of the proposed work is to develop a tool in the form of a smart watch, for accurately and quickly detecting falls and alerting emergency contacts, potentially saving lives, and enhancing the quality of life for seniors and people with mobility issues. The smart watch designed can transmit real-time notifications to the user's smart phone and to their emergency contacts in the event of a fall, enabling quick actions. The tool can be modified to add new features like heart rate monitoring and prescription reminders, which could have significant use in the healthcare industry. The hardware components of the device include GPS and GSM modules. To increase the fall detection algorithm's accuracy and investigate prospective system extensions and upgrades, the system also involves the collection and analysis of real-world data. The overall goal is to create a useful smart watch with a fall detection system that can offer critical assistance to those in need and their emergency contacts. The system also involves creating a cloud-based infrastructure to store and process sensor data from the smart watch, enabling remote data analysis and monitoring. The research also investigates additional potential uses for the smart watch, such as activity tracking and health monitoring, in addition, to fall detection. Through this initiative, it is hoped to enhance the lives of people with mobility challenges, their careers, and the expanding field of wearable technology.