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A Context-Aware mHealth System for Online Physiological Monitoring in Remote Healthcare

机译:用于远程医疗中在线生理监测的情境感知mHealth系统

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Physiological or biological stress is an organism's response to a stressor such as an environmental condition or a stimulus. The identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. Activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. This is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. This paper presents a context-aware telemonitoring platform, IPM-mHealth, that receives vital parameters from multiple sensors for online, real-time analysis. IPM-mHealth provides the technical basis for effectively evaluating patients' physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. The two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. Finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. The test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis.
机译:生理或生物压力是生物体对压力源(例如环境条件或刺激)的反应。在进行日常生活活动时识别生理压力是预防医学健康研究的重要领域。活动会启动动态的生理反应,可用作整体健康状况的指标。这对高风险人群尤其重要;评估心血管疾病患者日常活动中的身体状况仍然非常困难。本文介绍了一种上下文感知的远程监控平台IPM-mHealth,该平台从多个传感器接收重要参数以进行在线实时分析。 IPM-mHealth通过身体功能与日常活动之间的相关性,为有效评估患者的生理状况提供了技术基础,无论是住院患者还是在家中。该平台的两个核心模块包括:1)基于三轴加速度传感器的在线活动识别算法,以及2)基于知识的条件推理决策模块,该模块使用上下文信息来提高确定潜在事件发生的准确性。危险的异常心率。最后,我们提出了相关的实验来收集来自人类受试者的心脏信息和上身加速度数据。测试结果表明,该平台具有用于长期健康观察的巨大潜力,并且可以通过自动活动分析来帮助我们定义最佳的患者活动概况。

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