Exercise monitoring on consumer smart phones using ultrasonic sensing. Fu, B., Gangatharan, D., Kuijper, A., Kirchbuchner, F., & Braun, A. In Proceedings of the 4th international Workshop on Sensor-based Activity Recognition and Interaction, volume Part F1319, 2017.
abstract   bibtex   
Copyright © 2017 Association for Computing Machinery. 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.
@inProceedings{
 title = {Exercise monitoring on consumer smart phones using ultrasonic sensing},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Doppler effect,Human activity recognition,Mobile applications,Ultrasound sensing},
 volume = {Part F1319},
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 created = {2017-12-27T11:54:58.926Z},
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 abstract = {Copyright © 2017 Association for Computing Machinery. 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.},
 bibtype = {inProceedings},
 author = {Fu, B. and Gangatharan, D.V. and Kuijper, A. and Kirchbuchner, F. and Braun, A.},
 booktitle = {Proceedings of the 4th international Workshop on Sensor-based Activity Recognition and Interaction}
}

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