论文标题
无监督的基于学习的深度聚类用于人类活动识别
Unsupervised Deep Learning-based clustering for Human Activity Recognition
论文作者
论文摘要
应用深度学习技术来识别基于惯性传感器的日常生活活动（ADL）的主要问题之一是缺乏适当的标签数据集来培训基于深度学习的模型。由于配备有惯性传感器的移动设备广泛传播，可以收集数据以识别人类活动，因此可以提供大量数据。不幸的是，此数据未标记。本文提出了光盘（深惯性感觉聚类），这是一种基于DL的聚类结构，自动标记多维惯性信号。特别是，该体系结构结合了一个经常性的自动编码器和聚类标准，以预测与人类活动相关的未标记的信号。在三个公开可用的HAR数据集上评估了所提出的体系结构，并将其与四种众所周知的端到端深度聚类方法进行了比较。该实验证明了光盘对聚类精度和归一化相互信息指标的有效性。
One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labelled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual information metrics.
