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Prediction of Personalised Life Expectancy using Personal Health Devices in mHealth Networks

机译：使用mHealth网络中的个人健康设备预测个性化预期寿命

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The ability to predict life expectancy (LE) for an individual or a group of people has been in demand for long, however the accuracy and validity of results are difficult to enhance due to the numerous variables required for consideration. The main causes of issues are that human behaviour and activities can be so different and unpredictable that it is almost impossible to measure, classify, define and predict against generic statistic values, which themselves are too numerous in variables to determine. However, health-related data are becoming increasingly available with the emergence of data science technologies and there has been an increase of smartphone and wearable device applications that allow for health and fitness tracking to aid these demands. Some health-related data, such as calorie expenditure and sleep and heart rate monitoring can be provided by apps that are collected by sensors and processed in the cloud. A personalized life expectancy (PLE) can be provided for individuals to improve wellbeing and encourage healthy lifestyle changes. There is currently no work that has addressed a PLE information that can be customized for the individual. This article proposes a novel and innovative idea of calculating and predicting LE. This paper provides a solution that improves the accuracy of a group LE based on individual health data as well as encouraging individuals to change their lifestyle by monitoring their own PLE to improve their quality of their life.
机译：长期以来，一直需要能够预测个人或一群人的预期寿命（LE）的能力，但是由于需要考虑许多变量，因此难以提高结果的准确性和有效性。问题的主要原因是人类的行为和活动可能如此不同且不可预测，以至于几乎不可能对通用统计值进行测量，分类，定义和预测，而通用统计值本身变量太多，无法确定。但是，随着数据科学技术的出现，与健康相关的数据变得越来越可用，并且智能手机和可穿戴设备应用程序的数量也不断增加，这些应用程序允许进行健康和健身跟踪来满足这些需求。可以通过传感器收集并在云中处理的应用程序来提供一些与健康相关的数据，例如卡路里消耗以及睡眠和心率监测。可以为个人提供个性化的预期寿命（PLE），以改善健康状况并鼓励健康的生活方式改变。当前，没有针对可为个人定制的PLE信息的工作。本文提出了一种计算和预测LE的新颖且创新的想法。本文提供了一种解决方案，该解决方案可以基于个人健康数据提高组LE的准确性，并通过监视自己的PLE来改善生活质量，从而鼓励个人改变生活方式。

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