Ahmed Hamza, Santosh Kumar Prabhulingaiah, Pegah Pezeshkpour, Bastian E. Rapp
{"title":"Hand Gesture Recognition Using Frequency-Modulated Continuous Wave Radar on Tactile Displays for the Visually Impaired","authors":"Ahmed Hamza, Santosh Kumar Prabhulingaiah, Pegah Pezeshkpour, Bastian E. Rapp","doi":"10.1002/aisy.202570009","DOIUrl":null,"url":null,"abstract":"<p><b>Hand Gesture Recognition</b>\n </p><p>In article number 2400663, Pegah Pezeshkpour and co-workers present a hand gesture recognition application using a radar sensor for Braille displays. The range-time and velocity-time maps were extracted from the raw data and were processed in the form of spectrogram images. Using support vector machines and convolutional neural network approaches, ten hand gestures were successfully classified with high detection accuracy.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 2","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202570009","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202570009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Hand Gesture Recognition
In article number 2400663, Pegah Pezeshkpour and co-workers present a hand gesture recognition application using a radar sensor for Braille displays. The range-time and velocity-time maps were extracted from the raw data and were processed in the form of spectrogram images. Using support vector machines and convolutional neural network approaches, ten hand gestures were successfully classified with high detection accuracy.