{"title":"利用超声波感应在消费者智能手机上进行运动监测","authors":"Biying Fu, Dinesh Vaithyalingam Gangatharan, Arjan Kuijper, Florian Kirchbuchner, Andreas Braun","doi":"10.1145/3134230.3134238","DOIUrl":null,"url":null,"abstract":"Quantified self has been a trend over the last several years. An increasing number of people use devices, such as smartwatches or smartphones to log activities of daily life, including step count or vital information. However, most of these devices have to be worn by the user during the activities, as they rely on integrated motion sensors. Our goal is to create a technology that enables similar precision with remote sensing, based on common sensors installed in every smartphone, in order to enable ubiquitous application. We have created a system that uses the Doppler effect in ultrasound frequencies to detect motion around the smartphone. We propose a novel use case to track exercises, based on several feature extraction methods and machine learning classification. We conducted a study with 14 users, achieving an accuracy between 73 % and 92% for the different exercises.","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":"17","resultStr":"{\"title\":\"Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing\",\"authors\":\"Biying Fu, Dinesh Vaithyalingam Gangatharan, Arjan Kuijper, Florian Kirchbuchner, Andreas Braun\",\"doi\":\"10.1145/3134230.3134238\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantified self has been a trend over the last several years. An increasing number of people use devices, such as smartwatches or smartphones to log activities of daily life, including step count or vital information. However, most of these devices have to be worn by the user during the activities, as they rely on integrated motion sensors. Our goal is to create a technology that enables similar precision with remote sensing, based on common sensors installed in every smartphone, in order to enable ubiquitous application. We have created a system that uses the Doppler effect in ultrasound frequencies to detect motion around the smartphone. We propose a novel use case to track exercises, based on several feature extraction methods and machine learning classification. We conducted a study with 14 users, achieving an accuracy between 73 % and 92% for the different exercises.\",\"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\":\"17\",\"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/10.1145/3134230.3134238\",\"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/10.1145/3134230.3134238","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing
Quantified self has been a trend over the last several years. An increasing number of people use devices, such as smartwatches or smartphones to log activities of daily life, including step count or vital information. However, most of these devices have to be worn by the user during the activities, as they rely on integrated motion sensors. Our goal is to create a technology that enables similar precision with remote sensing, based on common sensors installed in every smartphone, in order to enable ubiquitous application. We have created a system that uses the Doppler effect in ultrasound frequencies to detect motion around the smartphone. We propose a novel use case to track exercises, based on several feature extraction methods and machine learning classification. We conducted a study with 14 users, achieving an accuracy between 73 % and 92% for the different exercises.