Carús Juan Luis, Peláez Víctor, López Gloria, Fernández Miguel Ángel, Alvarez Eduardo, Díaz Gabriel
Fundación CTIC-Centro Tecnológico, Gijón, Asturias, Spain.
Stud Health Technol Inform. 2013;189:65-70.
Abnormal human behavior detection under free-living conditions is a reliable technique to detect activity disorders and diseases. This work proposes an acceleration-based algorithm to detect abnormal behavior as an abnormal increase or decrease in physical activity (PA). The algorithm is based on statistical features of human physical activity. Using a period of observed physical activity as a reference, the algorithm is able to detect abnormal behavior in other periods of time. The approach is unsupervised as the modeling of the reference behavior is not required. It has been validated with a group of 12 users under free-living conditions for two days. Results show a precision greater than 75% and a recall of 92%.
在自由生活条件下检测异常人类行为是一种检测活动障碍和疾病的可靠技术。这项工作提出了一种基于加速度的算法，以检测作为身体活动（PA）异常增加或减少的异常行为。该算法基于人类身体活动的统计特征。以一段观察到的身体活动作为参考，该算法能够检测其他时间段的异常行为。由于不需要对参考行为进行建模，该方法是无监督的。它已在自由生活条件下对一组12名用户进行了为期两天的验证。结果显示精度大于75%，召回率为92%。
