Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming. State-of-the-art unsupervised remedies investigated to alleviate the burdens of data annotations in HAR mainly explore training autoencoder frameworks. In this paper: we explore generative adversarial network (GAN) paradigms to learn unsupervised feature representations from wearable sensor data; and design a new GAN framework-Geometrically-Guided GAN or Guided-GAN-for the task. To demonstrate the effectiveness of our formulation, we evaluate the features learned by Guided-GAN in an unsupervised manner on three downstream classification benchmarks. Our results demonstrate Guided-GAN to outperform existing unsupervised approaches whilst closely approaching the performance with fully supervised learned representations. The proposed approach paves the way to bridge the gap between unsupervised and supervised human activity recognition whilst helping to reduce the cost of human data annotation tasks.
翻译:人类活动识别(HAR)是无处不在的计算中的一个重要研究领域,在这种计算中,获取大型标签传感器数据是乏味的、劳动密集型的和耗时的。为减轻HAR数据说明负担而调查的最先进的、不受监督的补救措施主要是探索培训自动编码器框架。在本文中,我们探索了基因对抗网络(GAN)模式,以便从可磨损的传感器数据中学习不受监督的特征表现;设计了一个新的GAN框架-Geophical-Guided GAN 或Droad-GAN 用于这项任务。为了展示我们的编制工作的有效性,我们以不受监督的方式评估指导GAN在下游三个分类基准方面学到的特征。我们的成果显示,指导GAN超越了现有的不受监督的方法,同时以受到充分监督的知情的表述方式接近业绩。拟议方法为弥合未经监督和监督的人类活动识别之间的差距铺平了道路,同时帮助降低人类数据说明任务的成本。