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Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing

Fu, Biying and Gangatharan, Dinesh Vaithyalingam and Kuijper, Arjan and Kirchbuchner, Florian and Braun, Andreas (2017):
Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing.
pp. 1-6, iWOAR '17 - 4th international Workshop on Sensor-based Activity Recognition and Interaction, Rostock, Germany, 21. - 22. September 2017, DOI: 10.1145/3134230.3134238,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Fu, Biying and Gangatharan, Dinesh Vaithyalingam and Kuijper, Arjan and Kirchbuchner, Florian and Braun, Andreas
Title: Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing
Language: English
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.

Uncontrolled Keywords: Mobile applications, User interfaces, Input devices, Human activity recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: iWOAR '17 - 4th international Workshop on Sensor-based Activity Recognition and Interaction
Event Location: Rostock, Germany
Event Dates: 21. - 22. September 2017
Date Deposited: 04 May 2020 12:09
DOI: 10.1145/3134230.3134238
Official URL: https://doi.org/10.1145/3134230.3134238
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