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Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms

Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Korea
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Sensors 2018, 18(9), 3109; https://doi.org/10.3390/s18093109
Received: 13 July 2018 / Revised: 5 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
(This article belongs to the Section Physical Sensors)
Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic application. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable product without unnecessary heavy and uncomfortable electronic devices. View Full-Text
Keywords: wearables; human motion monitoring; SWCNT; textiles; machine learning algorithm wearables; human motion monitoring; SWCNT; textiles; machine learning algorithm
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MDPI and ACS Style

Vu, C.C.; Kim, J. Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors 2018, 18, 3109. https://doi.org/10.3390/s18093109

AMA Style

Vu CC, Kim J. Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors. 2018; 18(9):3109. https://doi.org/10.3390/s18093109

Chicago/Turabian Style

Vu, Chi C., and Jooyong Kim. 2018. "Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms" Sensors 18, no. 9: 3109. https://doi.org/10.3390/s18093109

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