Sensor-based human activity recognition is to recognise human daily activities through a collection of ambient and wearable sensors. It is the key enabler for many healthcare applications, especially in ambient assisted living. The advance of sensing and communication technologies has driven the deployment of sensors in many residential and care home settings. However, the challenge still resides in the lack of sufficient, high-quality activity annotations on sensor data, which most of the existing activity recognition algorithms rely on. In this paper, we propose an Unsupervised Domain adaptation technique for Activity Recognition, called UDAR, which supports sharing and transferring activity models from one dataset to another heterogeneous dataset without the need of activity labels on the latter. This approach has combined knowledge- and data-driven techniques to achieve coarse- and fine-grained feature alignment. We have evaluated UDAR on five third-party, real-world datasets and have demonstrated high recognition accuracy and robustness against sensor noise, compared to the state-of-the-art domain adaptation techniques.

基于传感器的人类活动识别是通过收集环境和可穿戴式传感器来识别人类的日常活动。它是许多医疗保健应用的关键推动力，尤其是在环境辅助生活中。传感和通信技术的进步推动了传感器在许多住宅和养老院环境中的部署。但是，挑战仍然存在于传感器数据上缺乏足够的，高质量的活动注释，而大多数现有活动识别算法都依赖该注释。在本文中，我们提出了一种用于活动识别的无监督域自适应技术，称为UDAR。，它支持将活动模型从一个数据集共享和转移到另一个异构数据集，而无需在后者上进行活动标签。这种方法结合了知识驱动和数据驱动技术，以实现粗粒度和细粒度特征对齐。与最新的领域自适应技术相比，我们已经在五个第三方真实世界的数据集上评估了UDAR，并展示了较高的识别精度和抗传感器噪声的鲁棒性。