{"title":"通过深度学习从HMD运动中预测躯干方向用于原地行走导航","authors":"Juyoung Lee, Andréas Pastor, Jae-In Hwang, G. Kim","doi":"10.1145/3359996.3364709","DOIUrl":null,"url":null,"abstract":"In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.","PeriodicalId":393864,"journal":{"name":"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning\",\"authors\":\"Juyoung Lee, Andréas Pastor, Jae-In Hwang, G. Kim\",\"doi\":\"10.1145/3359996.3364709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.\",\"PeriodicalId\":393864,\"journal\":{\"name\":\"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3359996.3364709\",\"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 25th ACM Symposium on Virtual Reality Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3359996.3364709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning
In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.