Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-201
Elderly Health Status Monitoring Using WNN and PSO
Associate Professor, College of Liberal Arts and Education, Hebei Open University, Shijiazhuang, 050080, China, E-mail: qinhuijuan2026@outlook.com
Project Management
Received February 6, 2026; revised July 16, 2026; accepted September 6, 2026
Available online September 20, 2026
Abstract: With the global population aging, monitoring the health status of elderly people has become a key research focus. However, the lack of data format interoperability among medical platforms reduces the accuracy of monitoring results. Therefore, this study proposes a health status monitoring model that integrates the Wavelet Neural Network (WNN) and the Particle Swarm Optimization (PSO) algorithm. The model extracts and learns physiological features and data variations by integrating the Wavelet Neural Network with the Particle Swarm Optimization algorithm, enabling health status monitoring. Experimental results show that the proposed model achieves a prediction consistency of 0.946 and a monitoring accuracy of 96.18% on a self-constructed dataset. In addition, the proposed model's monitoring F1 score exceeds 94%, and its accuracy in identifying chronic disease types reaches 98.74%. These results demonstrate that the model can accurately and efficiently assess the health status of elderly people and capture subtle variations, providing reliable technical support for building adaptive, high-precision health-monitoring systems.
Keywords: Wavelet Neural Network(WNN); Particle Swarm Optimization(PSO); health status monitoring; elderly.
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Citation: Qin, H. (2026). Elderly Health Status Monitoring Using WNN and PSO. Journal of Engineering, Project, and Production Management, 16(6), 2026-201.
