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首页> 外文期刊>World review of science, technology and sustainable development >Causal-effect structure transformation based on hierarchical representation for biomedical sensing
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Causal-effect structure transformation based on hierarchical representation for biomedical sensing

机译：基于层次表示的生物医学传感因果结构转换

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摘要

In general, understanding causality among components in a target system, including a human body, is quite effective and efficient solution since utilisation of the causality helps predicting future system condition, making correct diagnosis and so forth. As for focusing on biomedical sensing, the causality among vital signals obtained from sensors built in measurement equipment should be considered to recognise human's health condition correctly. In addition, effective causality transformation is desired when measurement equipment is improved such as replacing or maintaining components in the equipment. In this article, causality transformation method for improving causality is proposed. It employs a hierarchical representation of the causality based on human-machine collaborative knowledge and its applications of visceral fat area estimation and heart rate monitoring are presented.
机译：通常，了解目标系统（包括人体）各组件之间的因果关系是非常有效的解决方案，因为利用因果关系有助于预测未来的系统状况，进行正确的诊断等。至于专注于生物医学传感，应考虑从测量设备内置传感器获得的生命信号之间的因果关系，以正确识别人类的健康状况。另外，当改进测量设备时，例如更换或维护设备中的组件，需要有效的因果关系转换。本文提出了一种改善因果关系的因果关系转换方法。它采用基于人机协作知识的因果关系的分层表示，并介绍了其在内脏脂肪面积估计和心率监测中的应用。

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