Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Jeddah, Saudi Arabia.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2024 Sep 15;14(1):21537. doi: 10.1038/s41598-024-71545-6.
Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.
辅助生活设施迎合了老年人群体的需求，为日常活动提供帮助和支持。跌倒检测对于确保他们的健康和安全至关重要。老年人经常跌倒，可能会导致严重伤害和并发症。将计算机视觉技术融入辅助生活环境对于解决这些问题具有革命性意义。通过利用摄像机和复杂的方法，计算机视觉 (CV) 系统可以持续监测居民的动作，并实时识别任何潜在的跌倒事件。由深度学习 (DL) 技术驱动的 CV 系统可以通过摄像机对人员进行连续监控，研究复杂的视觉信息，快速检测潜在的跌倒风险或任何跌倒情况。该系统可以通过利用 DL 从大量视觉数据中学习，从而提高识别跌倒的能力，同时精确地减少误报。结合 CV 和 DL 可以提高跌倒检测的效率和可靠性，并允许主动干预，大大缩短紧急情况下的响应时间。本研究提出了一种新的用于跌倒检测和分类的计算机视觉深度特征融合 (DFFCV-FDC) 技术。DFFCV-FDC 方法的主要目的是利用 CV 概念检测跌倒事件。因此，DFFCV-FDC 方法使用高斯滤波 (GF) 方法消除噪声。此外，还涉及包括 MobileNet、DenseNet 和 ResNet 模型的深度特征融合过程。为了提高 DFFCV-FDC 技术的性能，基于改进的鹈鹕优化算法 (IPOA) 进行了超参数选择。最后，使用去噪自动编码器 (DAE) 模型识别跌倒。在基准跌倒数据库上对 DFFCV-FDC 方法的性能进行了分析。广泛的比较研究报告了 DFFCV-FDC 方法相对于现有技术的优越性。
