Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times.

在活动识别中使用基于传感器的方法通常需要将许多环境传感器部署到对象和环境。每个传感器可以通过一种以上的动作来触发，例如，炊具的触摸传感器可以通过烹饪，洗碗等来触发。活动由一些传感器事件组成。当同一活动的传感器数量在两个活动的大多数中时，这两个活动被定义为难以区分的相似活动。为了解决识别相似活动的挑战，本文提出了一种新的活动识别方法，该方法结合了持续时间和时间块特征的高维特征，以提高推理性能。再进一步，我们利用这些类似的活动来构建可以提高可扩展性和标准化能力的层次结构模型。我们设计日常生活中类似活动的实验以评估该解决方案。结果表明，高维时间特征平均提高了类似活动识别的准确率1.88倍，层次结构的使用可以将特定规则推广为标准规则，从而将相似活动识别的计算时间平均减少了0.36倍。