Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device
This paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/24/11748 |
| _version_ | 1797506800150380544 |
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| author | Jiří Přibil Anna Přibilová Ivan Frollo |
| author_facet | Jiří Přibil Anna Přibilová Ivan Frollo |
| author_sort | Jiří Přibil |
| collection | DOAJ |
| description | This paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in laboratory conditions. The PPG and phonation signals are recorded inside the magnetic resonance imaging scanner working with a weak magnetic field up to 0.2 T in a silent state and/or with a running scan sequence during the middle two phases. From the recorded phonation signal, different speech features are determined for statistical analysis and evaluation by the Gaussian mixture models (GMM) classifier. A database of affective sounds and two databases of emotional speech were used for GMM creation and training. The second part of the developed method gives comparison of results obtained from the statistical description of the sensed PPG wave together with the determined heart rate and Oliva–Roztocil index values. The fusion of results obtained from both modalities gives the final stress level. The performed experiments confirm our working assumption that a fusion of both types of analysis is usable for this task—the final stress level values give better results than the speech or PPG signals alone. |
| first_indexed | 2024-03-10T04:37:39Z |
| format | Article |
| id | doaj.art-6b469b3ce9f640ed868ee87ccdc98a97 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:37:39Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6b469b3ce9f640ed868ee87ccdc98a972023-11-23T03:37:43ZengMDPI AGApplied Sciences2076-34172021-12-0111241174810.3390/app112411748Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI DeviceJiří Přibil0Anna Přibilová1Ivan Frollo2Institute of Measurement Science, Slovak Academy of Sciences, 841 04 Bratislava, SlovakiaInstitute of Measurement Science, Slovak Academy of Sciences, 841 04 Bratislava, SlovakiaInstitute of Measurement Science, Slovak Academy of Sciences, 841 04 Bratislava, SlovakiaThis paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in laboratory conditions. The PPG and phonation signals are recorded inside the magnetic resonance imaging scanner working with a weak magnetic field up to 0.2 T in a silent state and/or with a running scan sequence during the middle two phases. From the recorded phonation signal, different speech features are determined for statistical analysis and evaluation by the Gaussian mixture models (GMM) classifier. A database of affective sounds and two databases of emotional speech were used for GMM creation and training. The second part of the developed method gives comparison of results obtained from the statistical description of the sensed PPG wave together with the determined heart rate and Oliva–Roztocil index values. The fusion of results obtained from both modalities gives the final stress level. The performed experiments confirm our working assumption that a fusion of both types of analysis is usable for this task—the final stress level values give better results than the speech or PPG signals alone.https://www.mdpi.com/2076-3417/11/24/11748stress detection and evaluationGMM-based classificationphoto-plethysmographic wave analysis |
| spellingShingle | Jiří Přibil Anna Přibilová Ivan Frollo Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device Applied Sciences stress detection and evaluation GMM-based classification photo-plethysmographic wave analysis |
| title | Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device |
| title_full | Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device |
| title_fullStr | Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device |
| title_full_unstemmed | Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device |
| title_short | Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device |
| title_sort | stress level detection and evaluation from phonation and ppg signals recorded in an open air mri device |
| topic | stress detection and evaluation GMM-based classification photo-plethysmographic wave analysis |
| url | https://www.mdpi.com/2076-3417/11/24/11748 |
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