Activity recognition benefits the lives of residents in a smart home on a daily basis. One of the aims of this technology is to achieve good performance in activity recognition. The extraction and selection of the daily activity feature have a significant effect on this performance. However, commonly used extraction of daily activity features have limited the performance of daily activity recognition. Based on the nature of the time series of sensor events caused by daily activities, this paper presents a novel extraction approach for daily activity feature. First, time tuples are extracted from sensor events to form a time series. Subsequently, several common statistic formulas are proposed to form the space of daily activity features. Finally, a feature selection algorithm is employed to generate final daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on four different classifiers. The results of the experiment reveal that the proposed approach is an improvement over the commonly used approach.

中文翻译：

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基于传感器事件时间序列的日常活动特征提取方法

活动识别每天都会使智能家居中的居民的生活受益。该技术的目的之一是在活动识别中获得良好的性能。日常活动特征的提取和选择对此性能有重大影响。但是，常用的日常活动特征提取限制了日常活动识别的性能。基于日常活动引起的传感器事件时间序列的性质，本文提出了一种新的日常活动特征提取方法。首先，从传感器事件中提取时间元组以形成时间序列。随后，提出了几种常用的统计公式来形成日常活动特征的空间。最后，采用特征选择算法来生成最终的日常活动特征。为了评估所提出的方法，基于四个不同的分类器，采用两个不同的数据集进行活动识别。实验结果表明，该方法是对常用方法的改进。