Probability model of rock climbing recognition based on information fusion sensor time series

Abstract Rock climbing is a sports activity that integrates competition, entertainment, and culture. With the development of the economy and the improvement in living standards, rock climbing has embarked on a path of self-development and has entered the lives of urban youth at an increasingly rapid...

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Autores principales:	Yuhui Jiang, Dawei Lan
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Multi-information fusion sensor Time series Rock climbing Motion recognition Telecommunication TK5101-6720 Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/1573f46a9a164ccda9ee11e856303285
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spelling	oai:doaj.org-article:1573f46a9a164ccda9ee11e8563032852021-11-08T10:45:10ZProbability model of rock climbing recognition based on information fusion sensor time series10.1186/s13634-021-00816-51687-6180https://doaj.org/article/1573f46a9a164ccda9ee11e8563032852021-11-01T00:00:00Zhttps://doi.org/10.1186/s13634-021-00816-5https://doaj.org/toc/1687-6180Abstract Rock climbing is a sports activity that integrates competition, entertainment, and culture. With the development of the economy and the improvement in living standards, rock climbing has embarked on a path of self-development and has entered the lives of urban youth at an increasingly rapid rate. This paper studies the probabilistic model of rock climbing recognition based on time series of multi-information fusion sensors so that climbers can climb more standardized. Based on practice, this paper has conducted research and design on the hardware platform and actually applied it to the rock climbing environment. Through reasonable processing of rock climbing process data of rock climbers, a variety of rock climbing state characteristics are successfully extracted for fusion. Aiming at the quasi-periodical characteristics of acceleration changes at different points during human movement, a method for identifying human movement patterns based on gait event information is designed. This method intercepts the three-axis acceleration data collected by each accelerometer through key gait events. A data set used to identify human movement patterns is established. A corresponding LDA classifier is established for each data set to identify the current movement pattern, and finally the classification results of all the classifiers are voted on. The final experiment shows that the system can identify the climbing movement of the climber within 3 s. The method can achieve 95.84% of the comprehensive recognition accuracy of the four state modes of rock climbing.Yuhui JiangDawei LanSpringerOpenarticleMulti-information fusion sensorTime seriesRock climbingMotion recognitionTelecommunicationTK5101-6720ElectronicsTK7800-8360ENEURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multi-information fusion sensor Time series Rock climbing Motion recognition Telecommunication TK5101-6720 Electronics TK7800-8360
spellingShingle	Multi-information fusion sensor Time series Rock climbing Motion recognition Telecommunication TK5101-6720 Electronics TK7800-8360 Yuhui Jiang Dawei Lan Probability model of rock climbing recognition based on information fusion sensor time series
description	Abstract Rock climbing is a sports activity that integrates competition, entertainment, and culture. With the development of the economy and the improvement in living standards, rock climbing has embarked on a path of self-development and has entered the lives of urban youth at an increasingly rapid rate. This paper studies the probabilistic model of rock climbing recognition based on time series of multi-information fusion sensors so that climbers can climb more standardized. Based on practice, this paper has conducted research and design on the hardware platform and actually applied it to the rock climbing environment. Through reasonable processing of rock climbing process data of rock climbers, a variety of rock climbing state characteristics are successfully extracted for fusion. Aiming at the quasi-periodical characteristics of acceleration changes at different points during human movement, a method for identifying human movement patterns based on gait event information is designed. This method intercepts the three-axis acceleration data collected by each accelerometer through key gait events. A data set used to identify human movement patterns is established. A corresponding LDA classifier is established for each data set to identify the current movement pattern, and finally the classification results of all the classifiers are voted on. The final experiment shows that the system can identify the climbing movement of the climber within 3 s. The method can achieve 95.84% of the comprehensive recognition accuracy of the four state modes of rock climbing.
format	article
author	Yuhui Jiang Dawei Lan
author_facet	Yuhui Jiang Dawei Lan
author_sort	Yuhui Jiang
title	Probability model of rock climbing recognition based on information fusion sensor time series
title_short	Probability model of rock climbing recognition based on information fusion sensor time series
title_full	Probability model of rock climbing recognition based on information fusion sensor time series
title_fullStr	Probability model of rock climbing recognition based on information fusion sensor time series
title_full_unstemmed	Probability model of rock climbing recognition based on information fusion sensor time series
title_sort	probability model of rock climbing recognition based on information fusion sensor time series
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/1573f46a9a164ccda9ee11e856303285
work_keys_str_mv	AT yuhuijiang probabilitymodelofrockclimbingrecognitionbasedoninformationfusionsensortimeseries AT daweilan probabilitymodelofrockclimbingrecognitionbasedoninformationfusionsensortimeseries
_version_	1718442590030790656

Probability model of rock climbing recognition based on information fusion sensor time series

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