Activity of daily living is an important indicator of the health status and functional capabilities of an individual. Activity recognition, which aims at understanding the behavioral patterns of people, has increasingly received attention in recent years. However, there are still a number of challenges confronting the task. First, labelling training data is expensive and time-consuming, leading to limited availability of annotations. Secondly, activities performed by individuals have considerable variability, which renders the generally used supervised learning with a fixed label set unsuitable. To address these issues, we propose a dynamic active learning-based activity recognition method in this work. Different from traditional active learning methods which select samples based on a fixed label set, the proposed method not only selects informative samples from known classes, but also dynamically identifies new activities which are not included in the predefined label set. Starting with a classifier that has access to a limited number of labelled samples, we iteratively extend the training set with informative labels by fully considering the uncertainty, diversity and representativeness of samples, based on which better-informed classifiers can be trained, further reducing the annotation cost. We evaluate the proposed method on two synthetic datasets and two existing benchmark datasets. Experimental results demonstrate that our method not only boosts the activity recognition performance with considerably reduced annotation cost, but also enables adaptive daily activity analysis allowing the presence and detection of novel activities and patterns.

中文翻译：

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基于动态主动学习的人类活动识别

日常生活活动是一个人的健康状况和功能能力的重要指标。近年来，旨在了解人们行为模式的活动识别越来越受到关注。然而，这项任务仍然面临许多挑战。首先，标记训练数据既昂贵又耗时，导致注释的可用性有限。其次，个体执行的活动具有相当大的可变性，这使得通常使用的具有固定标签集的监督学习不合适。为了解决这些问题，我们在这项工作中提出了一种基于动态主动学习的活动识别方法。不同于传统的基于固定标签集选择样本的主动学习方法，所提出的方法不仅从已知类别中选择信息样本，而且还动态识别未包含在预定义标签集中的新活动。从一个可以访问有限数量标记样本的分类器开始，我们通过充分考虑样本的不确定性、多样性和代表性，用信息标签迭代扩展训练集，在此基础上可以训练更明智的分类器，进一步减少注释成本。我们在两个合成数据集和两个现有基准数据集上评估所提出的方法。实验结果表明，我们的方法不仅提高了活动识别性能，而且大大降低了注释成本，