The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110167, China.
The School of Computer Science and Engineering, Northeastern University & Neusoft Research of Intelligent Healthcare Technology, Shenyang 110167, China.
Sensors (Basel). 2024 Aug 20;24(16):5365. doi: 10.3390/s24165365.
Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.
老年人跌倒事件是一种常见且严重的健康风险，可能导致身体受伤和其他并发症。为了及时检测和响应跌倒事件，基于雷达的跌倒检测系统受到了广泛关注。在本文中，我们提出了一种基于雷达信号频谱的深度学习模型，称为卷积双向长短时记忆（CB-LSTM）模型。引入 CB-LSTM 模型使跌倒检测系统能够同时捕获时间序列和空间特征，从而提高检测的准确性和可靠性。广泛的对比实验表明，我们的模型在检测跌倒方面的准确率达到 98.83%，超过了目前其他相关方法。总之，本研究通过基于雷达的跌倒检测系统的设计和实验验证，利用频谱和深度学习方法为老年人跌倒监测提供了有效的技术支持，为提高老年人的生活质量和提供及时的救援措施提供了巨大的潜力。
