Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System
Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features c...
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Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2017-02, Vol.17 (2) |
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creator | Ahn, DaeHan Park, Homin Hwang, Seokhyun Park, Taejoon |
description | Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types. |
doi_str_mv | 10.3390/s17020333 |
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In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. 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In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types.</description><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpVjstKw0AYhQdBbK0ufIN5gehcMklmI7TF1kBBsOo2_Jn5px3JjUwqpE9vRDeuDpzv43AIuePsXkrNHgJPmWBSygsy57GIo0wINiPXIXwyJqY-uyIzkQmWpVrNyf4VKw9lhTS32AzeeQODbxvaOvqBR28qjFYt9NY3B7o0PyjQFQS0dJI2p_N5pHnjsMfGIN2PYcD6hlw6qALe_uWCvG-e3tbP0e5lm6-Xu6gTnA2RyngiDJbKgtGxZeCcNGASJTSHWINLDEustI5zrqwDVLbMSpmkiUEOLpUL8vi7253KGq2Z_vdQFV3va-jHogVf_CeNPxaH9qtQUiqtmfwGoqldNQ</recordid><startdate>20170209</startdate><enddate>20170209</enddate><creator>Ahn, DaeHan</creator><creator>Park, Homin</creator><creator>Hwang, Seokhyun</creator><creator>Park, Taejoon</creator><general>MDPI</general><scope>5PM</scope></search><sort><creationdate>20170209</creationdate><title>Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System</title><author>Ahn, DaeHan ; Park, Homin ; Hwang, Seokhyun ; Park, Taejoon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p210t-58162ceb5dac94d0aff3cac65291a49af6c06d3df1115dfae5db8b3676ce1af73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ahn, DaeHan</creatorcontrib><creatorcontrib>Park, Homin</creatorcontrib><creatorcontrib>Hwang, Seokhyun</creatorcontrib><creatorcontrib>Park, Taejoon</creatorcontrib><collection>PubMed Central (Full Participant titles)</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahn, DaeHan</au><au>Park, Homin</au><au>Hwang, Seokhyun</au><au>Park, Taejoon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><date>2017-02-09</date><risdate>2017</risdate><volume>17</volume><issue>2</issue><eissn>1424-8220</eissn><abstract>Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types.</abstract><pub>MDPI</pub><pmid>28208795</pmid><doi>10.3390/s17020333</doi><oa>free_for_read</oa></addata></record> |
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title | Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System |
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