{"title":"Drive Safe Inspector: A Wearable-Based Fine-Grained Technique for Driver Hand Position Detection","authors":"Huangxun Chen, Zhice Yang, Chenyu Huang, Qian Zhang","doi":"10.1109/GLOCOM.2018.8647653","DOIUrl":null,"url":null,"abstract":"This paper presents DriveSafe Inspector, a fine-grained driver hand position monitoring system, which continuously detects a driver's hand position on the steering wheel. The steering wheel is divided into twelve 30° sectors like a clock. Our system can be applied on off-the-shelf hardware and works without extra modification to vehicles. In our system, sensor readings from both a wearable and its paired smartphone are fused to infer hand posture and turning angle between static holding states. With both static holding and dynamic turning information, our system achieves fine-grained hand position prediction in the presence of diverse road conditions and inter-individual differences. The on-road evaluation shows that our system can achieve an average 91.59% hand position detection accuracy with only static information, and can be further improved to 94.63% accuracy combined with dynamic turning information.","PeriodicalId":201848,"journal":{"name":"2018 IEEE Global Communications Conference (GLOBECOM)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Global Communications Conference (GLOBECOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOCOM.2018.8647653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents DriveSafe Inspector, a fine-grained driver hand position monitoring system, which continuously detects a driver's hand position on the steering wheel. The steering wheel is divided into twelve 30° sectors like a clock. Our system can be applied on off-the-shelf hardware and works without extra modification to vehicles. In our system, sensor readings from both a wearable and its paired smartphone are fused to infer hand posture and turning angle between static holding states. With both static holding and dynamic turning information, our system achieves fine-grained hand position prediction in the presence of diverse road conditions and inter-individual differences. The on-road evaluation shows that our system can achieve an average 91.59% hand position detection accuracy with only static information, and can be further improved to 94.63% accuracy combined with dynamic turning information.