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An automatic data mining method to detect abnormal human behaviour using physical activity measurements

机译:一种自动数据挖掘方法,利用身体活动测量来检测人类的异常行为

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摘要

Abnormal human behaviour detection under free-living conditions is a reliable method to detect disorders and diseases in healthcare applications. The problem with current methods to detect human behaviour changes is the use of supervised techniques that require human intervention. This work proposes an automatic data mining method based on physical activity measurements. Abnormal human behaviour is detected as an increase or decrease of the physical activity according to the historical data. Human behaviour is evaluated in real time grading its abnormality. The method has been validated involving users with a precision of 100% and a recall of 92%. (C) 2014 Elsevier B.V. All rights reserved.
机译:自由生活条件下的异常人类行为检测是在医疗保健应用中检测疾病和疾病的可靠方法。当前检测人类行为变化的方法存在的问题是使用需要人工干预的监督技术。这项工作提出了一种基于身体活动测量的自动数据挖掘方法。根据历史数据,人类异常行为被检测为身体活动的增加或减少。实时评估人类行为的异常程度。该方法已经过验证,涉及的用户精度为100%,召回率为92%。 (C)2014 Elsevier B.V.保留所有权利。

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