Howedi Aadel, Lotfi Ahmad, Pourabdollah Amir
School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK.
Entropy (Basel). 2019 Apr 19;21(4):416. doi: 10.3390/e21040416.
Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.
人类活动识别（HAR）是从不同类型传感器收集的数据中自动检测人类行为的过程。与HAR相关的研究特别关注在家庭环境中对单个居住者的人类活动进行监测和识别，其中假设在任何给定时间只有一个人在场。然后，利用活动识别来识别日常生活常规活动中的任何异常情况。尽管已发表的文献中有此假设，但居住环境通常有不止一个人居住和/或伴有宠物。本文探索了一种基于不同熵度量的新方法，包括近似熵（ApEn）、样本熵（SampEn）和模糊熵（FuzzyEn），用于在家庭环境中检测和识别访客。该研究主要关注另一个人何时拜访主要居住者，因此，无法区分他们的移动活动。本研究的目标是评估熵度量是否可用于在家庭环境中检测和识别访客。一旦区分出主要居住者与其他人，现有的活动识别和异常检测过程就可应用于主要居住者。使用两个不同的数据集对所提出的方法进行了测试和验证。实验结果表明，基于从占用传感器收集的数据，所提出的方法可用于在家庭环境中高精度地检测和识别访客。
