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 - Other contribution
SN - 9781467375450
PB - IEEE
ER -