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Sensors 2018, 18(6), 1965; https://doi.org/10.3390/s18061965

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
3
College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450002, China
*
Authors to whom correspondence should be addressed.
Received: 7 May 2018 / Revised: 14 June 2018 / Accepted: 15 June 2018 / Published: 18 June 2018
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Abstract

Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well. View Full-Text
Keywords: human motion mode; human pose pattern; window length; machine-learning method; smartphone sensors human motion mode; human pose pattern; window length; machine-learning method; smartphone sensors
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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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Wang, G.; Li, Q.; Wang, L.; Wang, W.; Wu, M.; Liu, T. Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors. Sensors 2018, 18, 1965.

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