Article
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Preserved in Portico This version is not peer-reviewed
Human Motion Recognition by Textile Sensor Based on Machine Learning Algorithms
Version 1
: Received: 13 July 2018 / Approved: 13 July 2018 / Online: 13 July 2018 (10:36:00 CEST)
A peer-reviewed article of this Preprint also exists.
Vu, C.C.; Kim, J. Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors 2018, 18, 3109. Vu, C.C.; Kim, J. Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors 2018, 18, 3109.
Abstract
Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in 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 applications. 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 products without unnecessary heavy and uncomfortable electronic devices.
Keywords
wearables; human motion monitoring; SWCNT; textiles; machine learning algorithm
Subject
Chemistry and Materials Science, Surfaces, Coatings and Films
Copyright: 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.
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