AI-Driven Heuristic Algorithms for IoT-Based Real-Time Fall Detection and Prevention

Unexpected Falls are a leading cause of injury, particularly among the elderly and individuals with mobility impairments. This proposed method presents a Heuristic Algorithm, a real-time fall detection system designed for low-power edge computing using the ESP32S3 microcontroller. Unlike traditional deep learning-based approaches that require significant computational resources, this method employs an optimized rule-based decision tree algorithm derived from machine learning techniques. The system integrates an MPU6050 IMU sensor to capture real-time accelerometer and gyroscope data, along with a KY-039 heart rate sensor for physiological monitoring. A lightweight rule-based classification model is deployed on the ESP32 to analyze sensor features to detect potential falls with high accuracy. Upon detection, the system triggers Sound Alert using the Active Buzzer 10mm and sends instant notifications via Telegram through IFTTT for remote assistance which ensures lowlatency processing, minimal memory footprint, and IoT-enabled emergency response. So this framework provides a costeffective, energy-efficient, and scalable solution for real-time fall detection in elderly care, industrial safety, and healthcare monitoring applications.