论文标题
基于传感器的人类活动识别的深度学习：概述，挑战和机遇
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
论文作者
论文摘要
传感器设备和物联网的巨大扩散使基于传感器的活动识别的应用。但是，存在实质性的挑战，可能会影响实际情况下的识别系统的性能。最近，由于深度学习在许多领域都证明了其有效性，因此已经研究了许多深入的方法来应对活动识别的挑战。在这项研究中，我们介绍了基于传感器的人类活动识别的最新深度学习方法的调查。我们首先介绍感官数据的多模式，并为公共数据集提供信息，这些信息可用于在不同的挑战任务中进行评估。然后，我们提出了一种新的分类法，以通过挑战来构建深度方法。总结并分析了挑战和与挑战有关的深层方法，以概述当前的研究进度。在这项工作结束时，我们讨论了开放问题，并为将来的方向提供了一些见解。
The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions.
