SFQSV Net: State Fusion Quantum Spectral Vision Network for Enhanced Elderly Fall Detection and Preventive Care in Smart Communities

Abstract A fall occurs when an individual loses balance and strikes the ground or a nearby object. This is a leading cause of accidental death among people aged 65 years and above, making it a universal health issue. Although fall detection systems have shown positive outcomes, detecting falls in varied environments remains challenging. We proposed the State Fusion Quantum Spectral Vision Network (SFQSV Net) to detect falls in diverse scenarios. SFQSV Net integrates quantum-inspired spectral attention with residual and convolutional networks, enhancing spatial and spectral feature extraction. The model incorporates multi-head spectral squeeze attention, residual attention mapping, and a hierarchical quantum-inspired pyramid to enable adaptive multi-scale representation learning. Additionally, spectral-channel coupling, sparse attention, and state-space modeling stabilize the learning process, permit gradient flow, and improve computational efficiency. A fusion-based classification head increases prediction accuracy and interpretability. The model performed well, with an accuracy of 0.9891, macro F1-score of 0.9889, ROC-AUC of 0.9892, Cohen’s kappa of 0.9782, and MCC of 0.9783. These results validate that SFQSV Net outperforms state-of-the-art methods.