Ms. Fei Chen Profile

Fei Chen

at Beijing Institute of Technology

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 August 2020 Presentation + Paper

Non-contact measurement of mental stress via heart rate variability

Fei Chen, Lingqin Kong, Yuejin Zhao, Liquan Dong, Ming Liu, Mei Hui

Proceedings Volume 11510, 115101I (2020) https://doi.org/10.1117/12.2567245

KEYWORDS: Heart, Skin, Video, Photoplethysmography, Signal processing, Cameras, Tissues, Pattern recognition, Nervous system

Read Abstract +

Non-contact detection of mental stress based on physiological parameters has many potential application areas, such as measuring stress in athletic contest. Non-contact detection could measure mental stress without drawing the attention of subjects. And compared with questionnaire survey, mental stress measurement based on physiological parameters is more objective. In this paper, we introduced a non-contact method to measure mental stress via heart rate variability (HRV). We conducted an experiment with 29 participants at rest and under stress. And a mental arithmetic test was employed to induce stress. To extract HRV, we recorded videos on subjects’ faces by a color CCD camera. HRV was extracted from these videos by imaging photoplethysmography (IPPG). The results showed that HRV was significantly different between normal and stressed conditions. Then we performed significance test and independence test to select the features which could be used in mental stress measurement. Finally, nine features were used to measure mental stress. In order to establish a stress measurement model, support vector machines (SVM) was used to establish a binary classifier for stress detection and the accuracy of the model was 78.2%. Compared with other methods, our method took non-linear features of HRV into consideration. The method we proposed supports the application of non-contact mental stress detection.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years