Context Analysis and Estimation of Mobile Users Considering the Time Series of Data
Context Analysis and Estimation of Mobile Users Considering the Time Series of Data
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DOI：
10.1109/gcce50665.2020.9292016
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Hiromi Shimizu;M. Suganuma;W. Kameyama
中科院分区：
文献类型：
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作者：
Hiromi Shimizu;M. Suganuma;W. Kameyama
近来，在诸如顾客行为分析的领域中，对理解移动的用户活动的需求已经增加。我们之前的研究表明，移动的用户的上下文可以在一定程度上估计使用各种传感器数据的移动的手机和用户的生物信号，通过应用机器学习方法。在本文中，我们建议通过考虑时间序列的数据来分析和估计上下文，以提高准确性。对于分析和估计，使用从两个主题收集的数据，应用卷积神经网络（CNN）和各种机器学习方法将数据分类到预定义的八个和七个上下文中并进行比较。结果表明，具有256个窗口宽度的CNN分别为每个主题实现了97.6%和97.1%的最高宏观F1分数。这表明，通过考虑数据的时间序列，可以更准确地进行使用传感器数据和生物信号的上下文分析和估计。
Recently, the demand for understanding mobile user’s activities has been increasing in the fields such as customer behavior analysis. Our previous study shows that mobile user’s context can be estimated to some extent using various sensor data of mobile phone and user’s bio-signals by applying machine learning methods. In this paper, we propose to analyze and estimate the context by considering the time series of data to improve the accuracy. For the analysis and the estimation, using the data collected from two subjects, convolutional neural network (CNN) and various machine learning methods to classify the data into pre-defined eight and seven contexts are applied and compared. The results show that CNN with 256 window width achieve the highest macro F1-score of 97.6% and 97.1% for each subject, respectively. It suggests that the context analysis and estimation using sensor data and bio-signal can be done much more accurately by considering the time series of data.