{"title":"Recognition of Hand Gestures using Visible Light and a Probabilistic-based Neural Network","authors":"Julian L. Webber, Abolfazl Mehbodniya","doi":"10.1109/MENACOMM57252.2022.9998225","DOIUrl":null,"url":null,"abstract":"Gestures by hand are an effective and efficient means to interact with machines. We present a method for recognizing gestures via the analysis of interrupted light patterns using visible light. Utilizing low-cost and easily accessible components, the system can exploit existing light communications infrastructures. A probabilistic type neural network is applied to learn the sequence of obfuscated light patterns during movement of the hands. A novel preprocessing of the received light is introduced enabling the use of an accurate but low-complexity class of neural network.","PeriodicalId":332834,"journal":{"name":"2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)","volume":"38 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MENACOMM57252.2022.9998225","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Gestures by hand are an effective and efficient means to interact with machines. We present a method for recognizing gestures via the analysis of interrupted light patterns using visible light. Utilizing low-cost and easily accessible components, the system can exploit existing light communications infrastructures. A probabilistic type neural network is applied to learn the sequence of obfuscated light patterns during movement of the hands. A novel preprocessing of the received light is introduced enabling the use of an accurate but low-complexity class of neural network.