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迈向智能手机在医疗保健领域的移动众包采集的声音测量的解释:使用 Android 设备的实验。

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

机构信息

Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.

Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany.

出版信息

Sensors (Basel). 2021 Dec 28;22(1):170. doi: 10.3390/s22010170.


DOI:10.3390/s22010170
PMID:35009713
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8749792/
Abstract

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.

摘要

移动设备的普及促进了生态瞬时评估(EMA)和移动众包感知(MCS)在医疗保健领域的结合使用。这种结合不仅使研究人员能够收集具有生态有效性的数据,还能使用智能手机传感器来捕捉数据收集时的上下文。TrackYourTinnitus(TYT)平台使用 EMA 来跟踪用户的个体主观耳鸣感知,使用 MCS 在填写 EMA 问卷时捕获客观环境声音水平。然而,由于存储了未经校准的原始值,TYT 用户使用的不同智能手机之间的声音水平数据无法直接使用。本工作描述了一种使这些值具有可比性的方法。在所描述的环境中,对来自不同智能手机用户的传感器测量值的评估变得越来越普遍。因此,所展示的方法不仅可以作为一种更通用的解决方案,因为它不仅展示了如何帮助解释 TYT 声音水平数据,而且还可以激励其他研究人员,特别是那些需要在类似环境中解释传感器数据的研究人员。总的来说,该方法将表明使用移动设备测量声音水平在医疗保健场景中是可行的,但确保可解释测量值存在许多挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/822aaaf2d243/sensors-22-00170-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/148fc2bf9ce5/sensors-22-00170-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/a0dbc9a7c5f0/sensors-22-00170-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/b787feaca978/sensors-22-00170-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/822aaaf2d243/sensors-22-00170-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/148fc2bf9ce5/sensors-22-00170-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/a0dbc9a7c5f0/sensors-22-00170-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/b787feaca978/sensors-22-00170-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bda/8749792/822aaaf2d243/sensors-22-00170-g004.jpg

相似文献

[1]
Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices.

Sensors (Basel). 2021-12-28

[2]
Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth.

IEEE J Biomed Health Inform. 2022-11

[3]
Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study.

J Med Internet Res. 2020-6-30

[4]
Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform.

Annu Int Conf IEEE Eng Med Biol Soc. 2019-7

[5]
Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Sensors (Basel). 2020-6-18

[6]
Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain.

Front Neurosci. 2020-2-28

[7]
Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis.

Sensors (Basel). 2024-1-12

[8]
Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform.

Sci Rep. 2021-9-15

[9]
Predicting the presence of tinnitus using ecological momentary assessments.

Sci Rep. 2023-6-2

[10]
Measuring the Moment-to-Moment Variability of Tinnitus: The TrackYourTinnitus Smart Phone App.

Front Aging Neurosci. 2016-12-15

引用本文的文献

[1]
Global 10 year ecological momentary assessment and mobile sensing study on tinnitus and environmental sounds.

NPJ Digit Med. 2025-3-13

[2]
Editorial: Smart mobile data collection in the context of neuroscience, volume II.

Front Neurosci. 2023-7-31

本文引用的文献

[1]
Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Sensors (Basel). 2020-6-18

[2]
Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain.

Front Neurosci. 2020-2-28

[3]
Evaluation of smartphone sound level meter applications as a reliable tool for noise monitoring.

J Occup Environ Hyg. 2019-7-29

[4]
Assimilation of mobile phone measurements for noise mapping of a neighborhood.

J Acoust Soc Am. 2018-9

[5]
The Accuracy of Smartphone Sound Level Meter Applications With and Without Calibration.

Am J Speech Lang Pathol. 2018-11-21

[6]
Measuring environmental noise from airports, oil and gas operations, and traffic with smartphone applications: laboratory and field trials.

J Expo Sci Environ Epidemiol. 2018-10-3

[7]
Kriging-based spatial interpolation from measurements for sound level mapping in urban areas.

J Acoust Soc Am. 2018-5

[8]
Hearing Tests Based on Biologically Calibrated Mobile Devices: Comparison With Pure-Tone Audiometry.

JMIR Mhealth Uhealth. 2018-1-10

[9]
Evaluation and calibration of mobile phones for noise monitoring application.

J Acoust Soc Am. 2017-11

[10]
Accurate Ambient Noise Assessment Using Smartphones.

Sensors (Basel). 2017-4-21

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