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Sensors 2018, 18(6), 1850; https://doi.org/10.3390/s18061850

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

School of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China
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Received: 25 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 6 June 2018
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Abstract

Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance. View Full-Text
Keywords: human activity recognition; user-adaptive algorithm; K-Means clustering; local outlier factor; multivariate Gaussian distribution; personalized classifier human activity recognition; user-adaptive algorithm; K-Means clustering; local outlier factor; multivariate Gaussian distribution; personalized classifier
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhao, S.; Li, W.; Cao, J. A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution. Sensors 2018, 18, 1850.

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