构建智能养老健康行为预测模型，提升老人长期健康管理精度。
Construction and optimization of health behavior prediction model for the elderly in smart elderly care
- 融合多模态数据与异常检测机制，动态追踪老人健康行为变化。
- 实验显示模型在突发行为检测准确率显著提升，隐私保护能力更强。
- 适合智慧养老平台、社区医疗系统等实际应用场景部署使用。
随着全球老龄化加剧，老年人健康管理成为社会关注焦点。本文设计并实现了一种智能养老服务平台模型，针对数据多样性、健康状态复杂性、长期依赖性、数据丢失、行为突变及数据隐私等问题，通过多模态数据融合、数据缺失处理、非线性预测、紧急事件检测和隐私保护等模块，实现对老年人健康行为的精准预测与动态管理。基于多源数据集与市场调研结果的实验表明，该模型在健康行为预测、紧急事件检测与个性化服务方面表现优异，有效提升了预测准确性与鲁棒性，满足智慧养老领域的实际应用需求。未来通过更多数据融合与技术优化，将为智慧养老提供更强大的技术支持。
原文摘要 · Abstract (English)
With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly care services.
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