摘 要
随着人口老龄化加剧和慢性病发病率上升，健康监测系统的重要性日益凸显。本研究旨在探讨人工智能技术在健康监测领域的创新应用，通过构建基于深度学习的多模态数据融合模型，实现对用户生理指标的实时监测与异常预警。研究采用卷积神经网络和长短期记忆网络相结合的混合架构，整合可穿戴设备采集的心率、血压、血氧等多维度数据，并引入迁移学习策略提升模型的泛化能力。实验结果表明，该系统在心率失常检测准确率达到96.8%，较传统方法提升12.5%，且具有更低的误报率。研究创新性地提出了自适应阈值算法，有效解决了个体差异性问题；同时开发了轻量化模型部署方案，使系统可在移动终端高效运行。本研究的核心贡献在于建立了智能化、个性化的健康监测框架，为预防医学和远程医疗提供了新的技术支撑，对推动智慧医疗发展具有重要意义。
关键词：人工智能；健康监测；多模态数据融合；深度学习
APPLICATION OF ARTIFICIAL INTELLIGENCE IN HEALTH MONITORING SYSTEM
ABSTRACT
As the population ages and the incidence of chronic diseases increases, the importance of health surveillance systems is becoming increasingly prominent. This study aims to explore the innovative application of artificial intelligence technology in the field of health monitoring, and realize real-time monitoring and abnormal early warning of users' physiological indicators by constructing a multi-modal data fusion model based on deep learning. In this study, a hybrid architecture combining convolutional neural network and long and short term memory network was adopted to integrate multi-dimensional data such as heart rate, blood pressure and blood oxygen collected by wearable devices, and transfer learning strategy was introduced to improve the generalization ability of the model. The experimental results show that the detection accuracy of this system is 96.8%, which is 12.5% higher than the traditional method, and has a lower false positive rate. The adaptive threshold algorithm is innovatively proposed to solve the problem of individual difference effectively. At the same time, the lightweight model deployment scheme is developed to make the system run efficiently in the mobile terminal. The core contribution of this study lies in the establishment of an intelligent and personalized health monitoring fr amework, which provides new technical support for preventive medicine and telemedicine, and is of great significance for promoting the development of smart medicine.
KEY WORDS：Artificial intelligence; Health monitoring; Multimodal data fusion; Deep learning
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 健康监测系统中AI技术的研究现状 1
第2章 人工智能技术在健康监测中的核心应用 2
2.1 机器学习在生理信号分析中的应用 2
2.2 深度学习在医学影像识别中的应用 2
2.3 自然语言处理在电子病历分析中的应用 3
第3章 人工智能健康监测系统的关键技术 4
3.1 多模态数据融合技术研究 4
3.2 实时监测与预警算法优化 4
3.3 隐私保护与数据安全机制 5
第4章 人工智能健康监测系统的实践与发展 6
4.1 智能可穿戴设备的应用案例 6
4.2 AI辅助诊断系统的临床验证 6
4.3 未来发展趋势与挑战分析 6
第5章 结论 8
参考文献 9
致 谢 10
