Unsupervised learning for human activity recognition using smartphone sensors
Unsupervised learning for human activity recognition using smartphone sensors
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DOI：
10.1016/j.eswa.2014.04.037
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发表时间：
2014-10-15
影响因子：
8.5
通讯作者：
Bae, Changseok
中科院分区：
文献类型：
--
作者：
Kwon, Yongjin;Kang, Kyuchang;Bae, Changseok
为了提供更复杂的医疗服务，有必要收集关于患者的准确信息。获得有意义信息的一个令人印象深刻的研究领域是人类活动识别，这是近几十年来通过使用监督学习技术进行的。然而，以前的研究在生成训练数据集和扩展要识别的活动数量方面遇到了困难。在本文中，为了找到一种避免这些问题的新方法，我们提出了一种用于人类活动识别的无监督学习方法，即使在活动数量未知的情况下，也可以使用智能手机传感器收集的传感器数据来识别人类活动。实验结果表明，当活动数k已知时，混合高斯算法能够准确地区分这些活动，而层次聚类法或DBSCAN算法则通过基于Calinski-Harabasz指数获得k或在k未知时选择合适的E和MinPts值来获得90%以上的准确率。我们相信，我们的方法的结果提供了一种自动选择适当的k值的方法，在该值时，活动识别的准确率最大化，而不需要手动生成训练数据集。(C)2014爱思唯尔有限公司。保留所有权利。
To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Calinski-Harabasz index, or by choosing appropriate values for E and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand. (C) 2014 Elsevier Ltd. All rights reserved.