Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model
Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5220 |
| _version_ | 1797596706966077440 |
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| author | Kamana Dahal Brian Bogue-Jimenez Ana Doblas |
| author_facet | Kamana Dahal Brian Bogue-Jimenez Ana Doblas |
| author_sort | Kamana Dahal |
| collection | DOAJ |
| description | Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high stress contributes to major health issues such as heart disease, high blood pressure, diabetes, depression, and anxiety. Several researchers have focused on detecting stress through combining many features with machine/deep learning models. Despite these efforts, our community has not agreed on the number of features to identify stress conditions using wearable devices. In addition, most of the reported studies have been focused on person-specific training and testing. Thanks to our community’s broad acceptance of wearable wristband devices, this work investigates a global stress detection model combining eight HRV features with a random forest (RF) algorithm. Whereas the model’s performance is evaluated for each individual, the training of the RF model contains instances of all subjects (i.e., global training). We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases) and their combination. The eight HRV features with the highest classifying power are selected using the minimum redundancy maximum relevance (mRMR) method, reducing the training time of the global stress platform. The proposed global stress monitoring model identifies person-specific stress events with an accuracy higher than 99% after a global training framework. Future work should be focused on testing this global stress monitoring framework in real-world applications. |
| first_indexed | 2024-03-11T02:56:50Z |
| format | Article |
| id | doaj.art-93840b912c9e46a08c5073f6629a2c96 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:56:50Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-93840b912c9e46a08c5073f6629a2c962023-11-18T08:34:11ZengMDPI AGSensors1424-82202023-05-012311522010.3390/s23115220Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest ModelKamana Dahal0Brian Bogue-Jimenez1Ana Doblas2Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USADepartment of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USADepartment of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USAApproximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high stress contributes to major health issues such as heart disease, high blood pressure, diabetes, depression, and anxiety. Several researchers have focused on detecting stress through combining many features with machine/deep learning models. Despite these efforts, our community has not agreed on the number of features to identify stress conditions using wearable devices. In addition, most of the reported studies have been focused on person-specific training and testing. Thanks to our community’s broad acceptance of wearable wristband devices, this work investigates a global stress detection model combining eight HRV features with a random forest (RF) algorithm. Whereas the model’s performance is evaluated for each individual, the training of the RF model contains instances of all subjects (i.e., global training). We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases) and their combination. The eight HRV features with the highest classifying power are selected using the minimum redundancy maximum relevance (mRMR) method, reducing the training time of the global stress platform. The proposed global stress monitoring model identifies person-specific stress events with an accuracy higher than 99% after a global training framework. Future work should be focused on testing this global stress monitoring framework in real-world applications.https://www.mdpi.com/1424-8220/23/11/5220stress detectionwearable deviceECGHRV featuresfeature selectionglobal training |
| spellingShingle | Kamana Dahal Brian Bogue-Jimenez Ana Doblas Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model Sensors stress detection wearable device ECG HRV features feature selection global training |
| title | Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model |
| title_full | Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model |
| title_fullStr | Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model |
| title_full_unstemmed | Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model |
| title_short | Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model |
| title_sort | global stress detection framework combining a reduced set of hrv features and random forest model |
| topic | stress detection wearable device ECG HRV features feature selection global training |
| url | https://www.mdpi.com/1424-8220/23/11/5220 |
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