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  • EHB_2015_paper_166

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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Mobile app for stress monitoring using voice features

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

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Mobile app for stress monitoring using voice features. / Sandulescu, Virginia; Andrews, Sally; Ellis, David et al.

E-Health and Bioengineering Conference (EHB), 2015. IEEE, 2015. p. 1-4.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Sandulescu, V, Andrews, S, Ellis, D, Dobrescu, R & Martinez-Mozos, O 2015, Mobile app for stress monitoring using voice features. in E-Health and Bioengineering Conference (EHB), 2015. IEEE, pp. 1-4. https://doi.org/10.1109/EHB.2015.7391411

APA

Sandulescu, V., Andrews, S., Ellis, D., Dobrescu, R., & Martinez-Mozos, O. (2015). Mobile app for stress monitoring using voice features. In E-Health and Bioengineering Conference (EHB), 2015 (pp. 1-4). IEEE. https://doi.org/10.1109/EHB.2015.7391411

Vancouver

Sandulescu V, Andrews S, Ellis D, Dobrescu R, Martinez-Mozos O. Mobile app for stress monitoring using voice features. In E-Health and Bioengineering Conference (EHB), 2015. IEEE. 2015. p. 1-4 doi: 10.1109/EHB.2015.7391411

Author

Sandulescu, Virginia ; Andrews, Sally ; Ellis, David et al. / Mobile app for stress monitoring using voice features. E-Health and Bioengineering Conference (EHB), 2015. IEEE, 2015. pp. 1-4

Bibtex

@inproceedings{119c679ef89b4c23a8a717f0f475a60b,
title = "Mobile app for stress monitoring using voice features",
abstract = "The paper describes the steps involved in designing and implementing a mobile app for real time monitoring of mental stress using voice features and machine learning techniques. The app is easy to use and completely non-invasive. It is called StressID and it is available in the Google Play store. With the use of a server application presenting a web interface, interested parties may remotely monitor the stress states detected by the mobile app, enlarging the number of use case scenarios.",
author = "Virginia Sandulescu and Sally Andrews and David Ellis and Radu Dobrescu and Oscar Martinez-Mozos",
note = "{\textcopyright}2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2015",
month = nov,
day = "19",
doi = "10.1109/EHB.2015.7391411",
language = "English",
isbn = "9781467375450 ",
pages = "1--4",
booktitle = "E-Health and Bioengineering Conference (EHB), 2015",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Mobile app for stress monitoring using voice features

AU - Sandulescu, Virginia

AU - Andrews, Sally

AU - Ellis, David

AU - Dobrescu, Radu

AU - Martinez-Mozos, Oscar

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2015/11/19

Y1 - 2015/11/19

N2 - The paper describes the steps involved in designing and implementing a mobile app for real time monitoring of mental stress using voice features and machine learning techniques. The app is easy to use and completely non-invasive. It is called StressID and it is available in the Google Play store. With the use of a server application presenting a web interface, interested parties may remotely monitor the stress states detected by the mobile app, enlarging the number of use case scenarios.

AB - The paper describes the steps involved in designing and implementing a mobile app for real time monitoring of mental stress using voice features and machine learning techniques. The app is easy to use and completely non-invasive. It is called StressID and it is available in the Google Play store. With the use of a server application presenting a web interface, interested parties may remotely monitor the stress states detected by the mobile app, enlarging the number of use case scenarios.

U2 - 10.1109/EHB.2015.7391411

DO - 10.1109/EHB.2015.7391411

M3 - Conference contribution/Paper

SN - 9781467375450

SP - 1

EP - 4

BT - E-Health and Bioengineering Conference (EHB), 2015

PB - IEEE

ER -