Fu, Biying ; Gangatharan, Dinesh Vaithyalingam ; Kuijper, Arjan ; Kirchbuchner, Florian ; Braun, Andreas (2017)
Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing.
iWOAR '17 - 4th international Workshop on Sensor-based Activity Recognition and Interaction. Rostock, Germany (21. - 22. September 2017)
doi: 10.1145/3134230.3134238
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Fu, Biying ; Gangatharan, Dinesh Vaithyalingam ; Kuijper, Arjan ; Kirchbuchner, Florian ; Braun, Andreas |
| Art des Eintrags: | Bibliographie |
| Titel: | Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Veranstaltungstitel: | iWOAR '17 - 4th international Workshop on Sensor-based Activity Recognition and Interaction |
| Veranstaltungsort: | Rostock, Germany |
| Veranstaltungsdatum: | 21. - 22. September 2017 |
| DOI: | 10.1145/3134230.3134238 |
| URL / URN: | https://doi.org/10.1145/3134230.3134238 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Mobile applications, User interfaces, Input devices, Human activity recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Mai 2020 12:09 |
| Letzte Änderung: | 04 Mai 2020 12:09 |
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