{"title":"摘要:测量:使用带有消费级加速度计的智能设备作为精确的测量尺度","authors":"Vivek Chandel, Avik Ghose","doi":"10.1109/ipsn.2018.00020","DOIUrl":null,"url":null,"abstract":"Calculating accurate distance from an accelerometer during motion involves integrating its raw data and it has been well-established that when the motion is imparted by humans, consumer-grade MEMS accelerometers are rendered unsuitable for this task due to their high error-profiles even for short-interval applications. This work presents 'EMeasure', a step towards addressing this problem with a completely sensor-agnostic and elegantly accurate error-mitigating model using temporal parameters for modeling the cumulated error in acceleration and velocity, yielding accurate distance. Inherent gravity is removed using a novel latency-free method using a gyroscope. The method has been tested on stand-alone MEMS sensor boards and multiple smart devices, in both phone and wrist-watch form factor with varied IMU sensor sets. Lengths up to 5 m have been measured with a mean measurement error of less than 3 cm. As a demo, we introduce EMeasure as an immensely useful and highly accurate length-measuring utility both on smartphones and smartwatches.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Demo Abstract: EMeasure: Using a Smart Device with Consumer-Grade Accelerometer as an Accurate Measuring Scale\",\"authors\":\"Vivek Chandel, Avik Ghose\",\"doi\":\"10.1109/ipsn.2018.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Calculating accurate distance from an accelerometer during motion involves integrating its raw data and it has been well-established that when the motion is imparted by humans, consumer-grade MEMS accelerometers are rendered unsuitable for this task due to their high error-profiles even for short-interval applications. This work presents 'EMeasure', a step towards addressing this problem with a completely sensor-agnostic and elegantly accurate error-mitigating model using temporal parameters for modeling the cumulated error in acceleration and velocity, yielding accurate distance. Inherent gravity is removed using a novel latency-free method using a gyroscope. The method has been tested on stand-alone MEMS sensor boards and multiple smart devices, in both phone and wrist-watch form factor with varied IMU sensor sets. Lengths up to 5 m have been measured with a mean measurement error of less than 3 cm. As a demo, we introduce EMeasure as an immensely useful and highly accurate length-measuring utility both on smartphones and smartwatches.\",\"PeriodicalId\":358074,\"journal\":{\"name\":\"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ipsn.2018.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ipsn.2018.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demo Abstract: EMeasure: Using a Smart Device with Consumer-Grade Accelerometer as an Accurate Measuring Scale
Calculating accurate distance from an accelerometer during motion involves integrating its raw data and it has been well-established that when the motion is imparted by humans, consumer-grade MEMS accelerometers are rendered unsuitable for this task due to their high error-profiles even for short-interval applications. This work presents 'EMeasure', a step towards addressing this problem with a completely sensor-agnostic and elegantly accurate error-mitigating model using temporal parameters for modeling the cumulated error in acceleration and velocity, yielding accurate distance. Inherent gravity is removed using a novel latency-free method using a gyroscope. The method has been tested on stand-alone MEMS sensor boards and multiple smart devices, in both phone and wrist-watch form factor with varied IMU sensor sets. Lengths up to 5 m have been measured with a mean measurement error of less than 3 cm. As a demo, we introduce EMeasure as an immensely useful and highly accurate length-measuring utility both on smartphones and smartwatches.