{"title":"Fingers' movement analysis based on Labanotation","authors":"Liu Laiyang, Guo Junjun","doi":"10.1109/WARTIA.2014.6976523","DOIUrl":null,"url":null,"abstract":"Fingers' movement plays an important role in sign language, and this papers will have an analysis about it, we Collected a lot of data sequences about fingers' movement in sign language vocabulary through the data glove and each vocabulary in sign language need to collect several times. Then save and name these data sequences with the corresponding vocabulary in sign language, in order to reduce the effect of deviation on the data and find out the best data sequence for each vocabulary to represent the corresponding fingers movements, we use Dynamic Time Warping distance as the measurement of the similarity of two data sequences. For each sequence of finger movement, choose the sequence which has the closest DTW distance with others. Then transform Labanotation to the finger movement with these data sequences. Not only this research can benefit the theoretical research of Kinesiology, but also it can provide scientific basis for digital simulation on sign language.","PeriodicalId":288854,"journal":{"name":"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WARTIA.2014.6976523","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
Fingers' movement plays an important role in sign language, and this papers will have an analysis about it, we Collected a lot of data sequences about fingers' movement in sign language vocabulary through the data glove and each vocabulary in sign language need to collect several times. Then save and name these data sequences with the corresponding vocabulary in sign language, in order to reduce the effect of deviation on the data and find out the best data sequence for each vocabulary to represent the corresponding fingers movements, we use Dynamic Time Warping distance as the measurement of the similarity of two data sequences. For each sequence of finger movement, choose the sequence which has the closest DTW distance with others. Then transform Labanotation to the finger movement with these data sequences. Not only this research can benefit the theoretical research of Kinesiology, but also it can provide scientific basis for digital simulation on sign language.