Research Center Borstel-Leibniz Lung Center, 23845 Borstel, Germany.
Institute for Applied Informatics, Application Engineering, Alpen-Adria University, 9020 Klagenfurt, Austria.
Sensors (Basel). 2020 Feb 4;20(3):825. doi: 10.3390/s20030825.
Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.
由于传感器技术的重大进展，近年来，活动识别的研究已经引起了人们的兴趣并趋于成熟。现有的机器学习算法通过对训练过程中已经看到的实例进行分类，已经展示了有前景的结果。基于实际场景的活动识别方法应该涵盖各种领域中越来越多的活动，其中很大一部分实例将不会出现在训练数据集。然而，预先涵盖所有可能的活动是一项复杂且昂贵的任务。具体来说，我们需要一种能够在没有关于以前未见过的活动的传感器读数的先验知识的情况下，将学习模型扩展到检测未见活动的方法。在本文中，我们介绍了一种利用传感器数据发现新的未见活动的方法，这些活动在训练集中不存在。我们表明，传感器读数可以通过语义相似性，为零样本学习提供有前景的结果，从而可以将必要的知识从可见活动转移到未见活动。在从著名的 CASAS 数据集提取的两个数据集上进行的评估表明，所提出的零样本学习方法在识别未见（即在训练数据集中不存在）的新活动方面具有很高的性能。
