Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav Deep Verma, Shivaji Kasireddy, P. Scholl, Kristof Van Laerhoven
{"title":"基于imu手套的手语字母拼写实时嵌入式识别","authors":"Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav Deep Verma, Shivaji Kasireddy, P. Scholl, Kristof Van Laerhoven","doi":"10.1145/3134230.3134236","DOIUrl":null,"url":null,"abstract":"Data gloves have numerous applications, including enabling novel human-computer interaction and automated recognition of large sets of gestures, such as those used for sign language. For most of these applications, it is important to build mobile and self-contained applications that run without the need for frequent communication with additional services on a back-end server. We present in this paper a data glove prototype, based on multiple small Inertial Measurement Units (IMUs), with a glove-embedded classifier for the french sign language. In an extensive set of experiments with 57 participants, our system was tested by repeatedly fingerspelling the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, with all detections performed on the glove within 63 milliseconds.","PeriodicalId":209424,"journal":{"name":"Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Real-time Embedded Recognition of Sign Language Alphabet Fingerspelling in an IMU-Based Glove\",\"authors\":\"Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav Deep Verma, Shivaji Kasireddy, P. Scholl, Kristof Van Laerhoven\",\"doi\":\"10.1145/3134230.3134236\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data gloves have numerous applications, including enabling novel human-computer interaction and automated recognition of large sets of gestures, such as those used for sign language. For most of these applications, it is important to build mobile and self-contained applications that run without the need for frequent communication with additional services on a back-end server. We present in this paper a data glove prototype, based on multiple small Inertial Measurement Units (IMUs), with a glove-embedded classifier for the french sign language. In an extensive set of experiments with 57 participants, our system was tested by repeatedly fingerspelling the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, with all detections performed on the glove within 63 milliseconds.\",\"PeriodicalId\":209424,\"journal\":{\"name\":\"Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3134230.3134236\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3134230.3134236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time Embedded Recognition of Sign Language Alphabet Fingerspelling in an IMU-Based Glove
Data gloves have numerous applications, including enabling novel human-computer interaction and automated recognition of large sets of gestures, such as those used for sign language. For most of these applications, it is important to build mobile and self-contained applications that run without the need for frequent communication with additional services on a back-end server. We present in this paper a data glove prototype, based on multiple small Inertial Measurement Units (IMUs), with a glove-embedded classifier for the french sign language. In an extensive set of experiments with 57 participants, our system was tested by repeatedly fingerspelling the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, with all detections performed on the glove within 63 milliseconds.