Abstract: Falls are important concern among elderly individuals, often leading to severe injuries or fatalities. Prompt detection of fall can significantly mitigate these risks by enabling timely medical intervention. This paper presents an advanced fall detect system that utilizes convolutional nueral network (CNNs) and multi-sensor fusion to accurately detect falls in real-time. The system operates on a local server, capturing video data via a web camera and integrating continuous wave radar data to enhance detection accuracy. Through extensive testing, the system demonstrated high accuracy, reliability, and user-friendliness, making it a valuable tool for improving the safety and well-being of elderly individuals.

Keywords:
● Fall Detection
● Elderly Safety
● Convolutional Nueral Networks (CNNs)
● Multi-Sensor Fusion
● Real-Time Monitoring
● Machine Learning
● Continuous Wave Radar

| DOI: 10.17148/IARJSET.2024.11737