Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-care, smart home monitoring, and surveillance. Existing approaches for activity recognition in smart homes have achieved promising results. Most of these approaches evaluate real-time recognition of activities using only sensor activations that precede the evaluation time (where the decision is made). However, in several critical situations, such as diagnosing people with dementia, “preceding sensor activations” are not always sufficient to accurately recognize the inhabitant's daily activities in each evaluated time. To improve performance, we propose a method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made. For this, the proposed method uses multiple incremental fuzzy temporal windows to extract features from both preceding and some oncoming sensor activations. The proposed method is evaluated with two temporal deep learning models (convolutional neural network and long short-term memory), on a binary sensor dataset of real daily living activities. The experimental evaluation shows that the proposed method achieves significantly better results than the real-time approach, and that the representation with fuzzy temporal windows enhances performance within deep learning models.

中文翻译：

---

使用二值传感器上的延迟模糊时间窗口在智能家居中进行有效的活动识别

由于人类活动识别已广泛应用于医疗保健，智能家居监控和监视等各个领域，因此在过去几年中已成为活跃的研究领域。现有的用于智能家居中的活动识别的方法已经取得了可喜的成果。这些方法中的大多数仅使用评估时间（做出决定的地方）之前的传感器激活来评估活动的实时识别。但是，在某些危急情况下，例如诊断患有痴呆症的人，“之前的传感器激活”并不总是足以准确地识别出每个评估时间内居民的日常活动。为了提高性能，我们提出了一种延迟识别过程的方法，以包括一些需要做出决策的时间点之后发生的传感器激活。为此，提出的方法使用多个增量模糊时间窗口从先前的和即将来临的传感器激活中提取特征。在真实的日常生活活动的二进制传感器数据集上，使用两个时间深度学习模型（卷积神经网络和长短期记忆）对提出的方法进行了评估。实验评估表明，与实时方法相比，该方法取得了明显更好的结果，并且模糊时间窗的表示增强了深度学习模型的性能。所提出的方法使用多个增量模糊时间窗口从先前的和即将来临的传感器激活中提取特征。在真实的日常生活活动的二进制传感器数据集上，使用两个时间深度学习模型（卷积神经网络和长短期记忆）对提出的方法进行了评估。实验评估表明，与实时方法相比，该方法取得了明显更好的结果，并且模糊时间窗口的表示增强了深度学习模型的性能。所提出的方法使用多个增量模糊时间窗口从先前的和即将来临的传感器激活中提取特征。在真实的日常生活活动的二进制传感器数据集上，使用两个时间深度学习模型（卷积神经网络和长短期记忆）对提出的方法进行了评估。实验评估表明，与实时方法相比，该方法取得了明显更好的结果，并且模糊时间窗的表示增强了深度学习模型的性能。