Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach

As society ages, the imbalance between family caregivers and elderly individuals increases, leading to inadequate support for seniors in many regions. This situation has ignited interest in automatic health monitoring systems, particularly in fall detection, due to the significant health risks that falls pose to older adults. This research presents a vision-based fall detection system that employs computer vision and deep learning to improve elderly care. Traditional systems often struggle to accurately detect falls from various camera angles, as they typically rely on static assessments of body posture. To tackle this challenge, we implement a feature fusion strategy within a deep learning framework to enhance detection accuracy across diverse perspectives. The process begins by generating a Human Silhouette Image (HSI) through background subtraction. By combining silhouette images from two consecutive frames, we create a Silhouette History Image (SHI), which captures the shape features of the individual. Simultaneously, Dense Optical Flow (DOF) extracts motion features from the same frames, allowing us to merge these with the SHI for a comprehensive input image. This fused representation is then processed using a pre trained Convolutional Neural Network (CNN) to extract deep features. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is subsequently trained on these features to recognize patterns indicative of fall events. Our approach’s effectiveness is validated through experiments on the UP-fall detection dataset, which includes 1,122 action videos and achieves an impressive 99% accuracy in fall detection.