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Open AccessArticle

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

HCI Lab., College of Computer Science, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea
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This paper is an extended version of our paper published in Song-Mi Lee; Heeryon Cho; Sang Min Yoon. Statistical Noise Reduction for Robust Human Activity Recognition. In Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, South Korea, 16–18 November 2017.
Sensors 2018, 18(4), 1055; https://doi.org/10.3390/s18041055
Received: 9 March 2018 / Revised: 25 March 2018 / Accepted: 29 March 2018 / Published: 1 April 2018
Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. View Full-Text
Keywords: human activity recognition; one-dimensional convolutional neural network; test data sharpening human activity recognition; one-dimensional convolutional neural network; test data sharpening
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MDPI and ACS Style

Cho, H.; Yoon, S.M. Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening. Sensors 2018, 18, 1055.

AMA Style

Cho H, Yoon SM. Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening. Sensors. 2018; 18(4):1055.

Chicago/Turabian Style

Cho, Heeryon; Yoon, Sang M. 2018. "Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening" Sensors 18, no. 4: 1055.

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