An Adaptive Learning Elderly Fall Detection System using LSTM and Convolutional Neural Network

Falls are a significant health concern, particularly for the elderly, leading to severe injuries and increased healthcare costs. Accurate and timely fall detection is crucial for prompt intervention. Traditional methods using wearable devices or ambient sensors have limitations, while recent video-based approaches struggle to capture temporal dynamics and adapt to new fall patterns. We propose an adaptive learning fall detection system combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to address these challenges. The system leverages CNNs for spatial feature extraction and LSTMs for temporal modeling, accurately detecting falls from video data. An adaptive learning mechanism enables continuous learning and adaptation to new fall patterns and environmental conditions. Evaluated on a diverse dataset, our approach outperforms CNN-only methods, achieving higher accuracy, precision, recall, and F1-score. Ablation studies confirm the contributions of LSTM temporal modeling and adaptive learning in improving performance. The system maintains high performance under various conditions and shows potential for application in elderly care, home monitoring, hospitals, and public spaces. The proposed system represents a significant step towards reliable and effective fall detection, enhancing the safety and well-being of individuals at risk of falling.