Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics
In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration an...
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| Veröffentlicht in: | Journal of sensors 2023-04, Vol.2023 (1) |
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| creator | Ding, Yafei |
| description | In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields. |
| doi_str_mv | 10.1155/2023/3132863 |
| format | Article |
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Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2023/3132863</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Classification ; Computers ; Data analysis ; Data collection ; Datasets ; Deep learning ; Heart rate ; Human behavior ; Human motion ; Human-computer interaction ; Internet of Things ; Machine vision ; Motion perception ; Neural networks ; Recognition ; Retina ; Sensors ; Smartphones ; Smartwatches ; Support vector machines ; Wavelet transforms</subject><ispartof>Journal of sensors, 2023-04, Vol.2023 (1)</ispartof><rights>Copyright © 2023 Yafei Ding.</rights><rights>Copyright © 2023 Yafei Ding. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c291t-9ca5d01616428dcd3fc409d3d268d0a2ce42967bc53bd2a6ecf5bbfecf34f45c3</cites><orcidid>0009-0005-7193-5908</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><contributor>Mittal, Mohit</contributor><contributor>Mohit Mittal</contributor><creatorcontrib>Ding, Yafei</creatorcontrib><title>Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics</title><title>Journal of sensors</title><description>In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. 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Perception Algorithm considering Precise Classification of Human Behavior Characteristics</title><author>Ding, Yafei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-9ca5d01616428dcd3fc409d3d268d0a2ce42967bc53bd2a6ecf5bbfecf34f45c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Classification</topic><topic>Computers</topic><topic>Data analysis</topic><topic>Data collection</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Heart rate</topic><topic>Human behavior</topic><topic>Human motion</topic><topic>Human-computer interaction</topic><topic>Internet of Things</topic><topic>Machine vision</topic><topic>Motion perception</topic><topic>Neural networks</topic><topic>Recognition</topic><topic>Retina</topic><topic>Sensors</topic><topic>Smartphones</topic><topic>Smartwatches</topic><topic>Support vector 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Precise Classification of Human Behavior Characteristics</atitle><jtitle>Journal of sensors</jtitle><date>2023-04-21</date><risdate>2023</risdate><volume>2023</volume><issue>1</issue><issn>1687-725X</issn><eissn>1687-7268</eissn><abstract>In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. 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| subjects | Accuracy Algorithms Classification Computers Data analysis Data collection Datasets Deep learning Heart rate Human behavior Human motion Human-computer interaction Internet of Things Machine vision Motion perception Neural networks Recognition Retina Sensors Smartphones Smartwatches Support vector machines Wavelet transforms |
| title | Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics |
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