{"title":"基于虚拟现实和手势识别的时间测量识别方法研究","authors":"Abdelkader Bellarbi, J. Jessel, Laurent Da Dalto","doi":"10.1109/AIVR46125.2019.00040","DOIUrl":null,"url":null,"abstract":"Methods-Time Measurement (MTM) is a predetermined motion time system that is used primarily in industrial settings to analyze the methods used to perform any manual operation. In this paper, we introduce a system for automatic generation of MTM codes using only head and both hands 3D tracking. Our approach relies on the division of gestures into small elementary movements. Then, we built a decision tree to aggregate these elementary movements in order to generate the realized MTM code. The proposed system does not need any pre-learning step, and it can be useful in both virtual environments to train technicians and in real cases with industrial workshops to assist experts for MTM code identification. Obtained results are satisfying and promising. This work is in progress, we plan to improve it in the near future.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Towards Method Time Measurement Identification Using Virtual Reality and Gesture Recognition\",\"authors\":\"Abdelkader Bellarbi, J. Jessel, Laurent Da Dalto\",\"doi\":\"10.1109/AIVR46125.2019.00040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Methods-Time Measurement (MTM) is a predetermined motion time system that is used primarily in industrial settings to analyze the methods used to perform any manual operation. In this paper, we introduce a system for automatic generation of MTM codes using only head and both hands 3D tracking. Our approach relies on the division of gestures into small elementary movements. Then, we built a decision tree to aggregate these elementary movements in order to generate the realized MTM code. The proposed system does not need any pre-learning step, and it can be useful in both virtual environments to train technicians and in real cases with industrial workshops to assist experts for MTM code identification. Obtained results are satisfying and promising. This work is in progress, we plan to improve it in the near future.\",\"PeriodicalId\":274566,\"journal\":{\"name\":\"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIVR46125.2019.00040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIVR46125.2019.00040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards Method Time Measurement Identification Using Virtual Reality and Gesture Recognition
Methods-Time Measurement (MTM) is a predetermined motion time system that is used primarily in industrial settings to analyze the methods used to perform any manual operation. In this paper, we introduce a system for automatic generation of MTM codes using only head and both hands 3D tracking. Our approach relies on the division of gestures into small elementary movements. Then, we built a decision tree to aggregate these elementary movements in order to generate the realized MTM code. The proposed system does not need any pre-learning step, and it can be useful in both virtual environments to train technicians and in real cases with industrial workshops to assist experts for MTM code identification. Obtained results are satisfying and promising. This work is in progress, we plan to improve it in the near future.