Mobile app for stress monitoring using voice features

Virginia Sandulescu; Sally Andrews; David Ellis; Radu Dobrescu; Oscar Martinez-Mozos

doi:10.1109/EHB.2015.7391411

Mobile app for stress monitoring using voice features

Virginia Sandulescu, Sally Andrews, David Ellis, Radu Dobrescu, Oscar Martinez-Mozos

Management

Research output: Other contribution

11 Citations (SciVal)

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.

Original language	English
Publisher	IEEE
Number of pages	4
ISBN (Print)	9781467375450
DOIs	https://doi.org/10.1109/EHB.2015.7391411
Publication status	Published - 19 Nov 2015

Bibliographical note

©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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10.1109/EHB.2015.7391411

Cite this

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AU - Dobrescu, Radu

AU - Martinez-Mozos, Oscar

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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.

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Mobile app for stress monitoring using voice features

Abstract

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