Finger-Counting-Based Gesture Recognition within Cars Using Impulse Radar with Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahmed, Shahzad | - |
dc.contributor.author | Khan, Faheem | - |
dc.contributor.author | Ghaffar, Asim | - |
dc.contributor.author | Hussain, Farhan | - |
dc.contributor.author | Cho, Sung Ho | - |
dc.date.accessioned | 2021-08-02T12:26:18Z | - |
dc.date.available | 2021-08-02T12:26:18Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/14994 | - |
dc.description.abstract | The diversion of a driver's attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative interface inside cars. Given that cars are the targeted application area, we determined the optimal location for the radar sensor, so that the signal reflected from the driver's hand during gesturing is unaffected by interference from the motion of the driver's body or other motions within the car. We implemented a Convolutional Neural Network-based technique to recognize the finger-counting-based hand gestures using an Impulse Radio (IR) radar sensor. The accuracy of the proposed method was sufficiently high for real-world applications. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Finger-Counting-Based Gesture Recognition within Cars Using Impulse Radar with Convolutional Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cho, Sung Ho | - |
dc.identifier.doi | 10.3390/s19061429 | - |
dc.identifier.scopusid | 2-s2.0-85063784781 | - |
dc.identifier.wosid | 000464525000004 | - |
dc.identifier.bibliographicCitation | SENSORS, v.19, no.6 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Human computer interaction | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Palmprint recognition | - |
dc.subject.keywordPlus | Radar | - |
dc.subject.keywordPlus | Radar equipment | - |
dc.subject.keywordPlus | Application area | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Finger counting | - |
dc.subject.keywordPlus | Hand-gesture recognition | - |
dc.subject.keywordPlus | Impulse radars | - |
dc.subject.keywordPlus | Learning classifiers | - |
dc.subject.keywordPlus | Optimal locations | - |
dc.subject.keywordPlus | Touch-based interface | - |
dc.subject.keywordPlus | Gesture recognition | - |
dc.subject.keywordAuthor | impulse radar sensor | - |
dc.subject.keywordAuthor | gesture recognition | - |
dc.subject.keywordAuthor | finger counting | - |
dc.subject.keywordAuthor | deep learning classifier | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/19/6/1429 | - |
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