论文标题
拉哈尔：使用LDA的潜在人类活动识别
LaHAR: Latent Human Activity Recognition using LDA
论文作者
论文摘要
处理顺序多传感器数据在许多任务中变得很重要，因为传感器的可用性急剧增加，这些传感器可以随着时间的推移获取顺序数据。人类活动识别（HAR）是从这种可用性中积极受益的领域之一。与大多数通过考虑预定义活动类别来解决HAR的方法不同，本文提出了一种新的方法，以发现顺序数据中的潜在HAR模式。为此，我们采用了潜在的Dirichlet分配（LDA），该分配最初是文本分析中使用的主题建模方法。为了使数据适合LDA，我们从顺序数据中提取所谓的“感官单词”。我们对一个具有挑战性的HAR数据集进行了实验，表明LDA能够在顺序数据中发现基本结构，这些结构提供了数据的人为可理解的代表。外部评估表明，与标记的活动相比，LDA能够准确地聚类HAR数据序列。
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time. Human Activity Recognition (HAR) is one of the fields which are actively benefiting from this availability. Unlike most of the approaches addressing HAR by considering predefined activity classes, this paper proposes a novel approach to discover the latent HAR patterns in sequential data. To this end, we employed Latent Dirichlet Allocation (LDA), which is initially a topic modelling approach used in text analysis. To make the data suitable for LDA, we extract the so-called "sensory words" from the sequential data. We carried out experiments on a challenging HAR dataset, demonstrating that LDA is capable of uncovering underlying structures in sequential data, which provide a human-understandable representation of the data. The extrinsic evaluations reveal that LDA is capable of accurately clustering HAR data sequences compared to the labelled activities.
