Identifying human activities using short-range and low-power radars has attracted much attention among the researchers and consumer electronics industry. This paper considers human activity recognition in the context of a single Frequency Modulated Continuous Wave (FMCW) radar as the measurement tool. A classification pipeline is proposed to handle the data pre-processing and feature extraction and a machine-learning based solution is devised to undertake the activity classification. The performance of the proposed architecture is evaluated under both unseen subjects and new room layouts. We show how the accuracy of the activity classification will be affected by situations such as poor aspect-angle and occlusions created by furniture that normally arise in realistic scenarios where an unseen layout is considered. A two-stage classifier will be then proposed to enhance the generalization of the model, especially, to unseen rooms. Besides, an extensive feature exploration will be conducted and the importance of features in the generalization will be studied. The results in this paper will conclude a machine learning pipeline that will generalize well to unseen subjects and new room layouts, which are two main difficulties that arise in most radar-based activity classification tasks.

中文翻译：

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基于单一雷达的人体活动识别的泛化性和可靠性

使用短距离和低功率雷达识别人类活动引起了研究人员和消费电子行业的广泛关注。本文考虑在单个调频连续波 (FMCW) 雷达作为测量工具的背景下识别人类活动。提出了一种分类管道来处理数据预处理和特征提取，并设计了一种基于机器学习的解决方案来进行活动分类。拟议架构的性能在看不见的主题和新的房间布局下进行评估。我们展示了活动分类的准确性将如何受到诸如家具产生的不良纵横角和遮挡等情况的影响，这些情况通常出现在考虑不可见布局的现实场景中。然后将提出一个两阶段分类器来增强模型的泛化，特别是对看不见的房间。此外，将进行广泛的特征探索，并研究特征在泛化中的重要性。本文的结果将得出一个机器学习管道，该管道将很好地推广到看不见的主题和新的房间布局，这是大多数基于雷达的活动分类任务中出现的两个主要困难。