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Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

1
Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
2
Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany
3
Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Annie Lanzolla
Sensors 2022, 22(1), 170; https://doi.org/10.3390/s22010170
Received: 7 October 2021 / Revised: 26 November 2021 / Accepted: 23 December 2021 / Published: 28 December 2021
(This article belongs to the Section Sensing and Imaging)
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable. View Full-Text
Keywords: mHealth; crowdsensing; tinnitus; noise measurement; environmental sound mHealth; crowdsensing; tinnitus; noise measurement; environmental sound
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MDPI and ACS Style

Kraft, R.; Reichert, M.; Pryss, R. Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. Sensors 2022, 22, 170. https://doi.org/10.3390/s22010170

AMA Style

Kraft R, Reichert M, Pryss R. Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. Sensors. 2022; 22(1):170. https://doi.org/10.3390/s22010170

Chicago/Turabian Style

Kraft, Robin, Manfred Reichert, and Rüdiger Pryss. 2022. "Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices" Sensors 22, no. 1: 170. https://doi.org/10.3390/s22010170

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