摘要：

为解决人体健康评估方法个性化监测不足的问题以及在满足不同场景下健康状态精细化评估的需求, 需要一种基于多场景的人体健康状态评估方法来实现长期自动化监测。提出一种基于层次分析法(AHP)和熵权法(EWM)组合的多场景人体健康状态评估模型。首先采集人体在运动、休息、工作/学习和娱乐等4种不同场景下的健康监测指标数据, 构建相应的评估指标体系。然后分别根据评估指标计算出AHP和EWM权重, 再采用量子粒子群优化(QPSO)算法对AHP和EWM中的主客观权重进行分配, 以确保评价指标占比的客观性。最后通过模糊综合评价法对人体健康状态进行评估和量化, 并利用实际监测数据对方法的可靠性和稳定性进行验证。实验结果表明, 在4种场景下所提方法的综合得分分别为63.78、59.83、58.71和59.21, 表明在不同场景下该模型都具有较好的准确性和稳定性。根据评估结果, 对测试者的身体状态评价结果进行分析, 并给出一些健康建议。所提模型可全面了解人体在不同场景下的健康状况, 并为人们提供科学的健康指导, 从而为健康管理和疾病预防提供科学依据。

关键词: 健康状态, 多重场景, 层次分析法, 熵权法, 量子粒子群优化算法, 模糊综合评价法

Abstract:

To solve the problem of insufficient personalized monitoring in human health assessment methods and meet the demand for fine-grained health status assessment in different scenarios, a multi-scenario-based human health status assessment method is needed to achieve long-term automated monitoring. This study proposes a multi-scenario human health assessment model based on a combination of the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). First, health monitoring index data for the human body in four different scenarios, including exercise, rest, work/study, and recreation, are collected to construct the corresponding assessment index system. Then, the AHP and EWM weights are calculated for the assessment indicators, and the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is used to distribute the subjective and objective weights for the AHP and EWM to ensure the objectivity of the proportion of evaluation indicators. Finally, the human health state is assessed and quantified using the fuzzy comprehensive evaluation method, and the reliability and stability of the method are verified using actual monitoring data. The experimental results show that the composite scores of the proposed method under the four scenarios (exercise, rest, work/study, and recreation) are 63.78, 59.83, 58.71, and 59.21, respectively, indicating that the model has good accuracy and stability under different scenarios. The results of the physical state evaluation of the testers are analyzed, and some health suggestions are given. The model proposed in this study can comprehensively determine the health status of the human body under different scenarios and provide scientific health guidance. Thus, it provides a scientific basis for health management and disease prevention.

Key words: health status, multiple scenarios, Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, fuzzy comprehensive evaluation method