Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for HAR in a smart space without privacy and accessibility issues, data streams generated by deployed pervasive sensors are leveraged. In this paper, we focus on a group activity by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme which extracts causality patterns from pervasive sensor event sequences generated by a group of users to support as good recognition accuracy as the state-of-the-art graphical model. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern matching algorithm computes the likelihoods of stored group activities to the given test event sequence by means of matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from our testbed and CASAS datasets where users perform their tasks on a daily basis and validate its effectiveness in a real environment. Experiment results show that the proposed scheme performs higher recognition accuracy and with a small amount of runtime overhead than the existing schemes.

翻译：人类活动识别（HAR）是普适计算领域的关键挑战，基于不同学科已提出多种解决方案。具体而言，针对不存在隐私与可访问性问题的智能空间中的HAR，可利用部署的普适传感器产生的数据流。本文聚焦于无需用户身份识别的协作型群体活动，提出一种高效的群体活动识别方案，该方案从群体用户产生的普适传感器事件序列中提取因果模式，以实现与现有最优图模型相当的识别精度。为过滤给定数据流中的无关噪声事件，采用一组规则突出显示因果相关事件；随后，模式树算法通过增长树结构提取频繁因果模式。基于所提取模式，采用加权求和模式匹配算法，通过匹配事件模式计数计算测试事件序列与存储群体活动之间的似然度，从而实现群体活动识别。我们利用实验平台采集的数据及CASAS数据集（用户执行日常任务）评估所提方案，并在真实环境中验证其有效性。实验结果表明，与现有方案相比，所提方案在保持较低运行时开销的同时，实现了更高的识别精度。