Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features
<p dir="ltr">Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate concl...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513509854806016 |
|---|---|
| author | Mariam Bahameish (19255789) |
| author2 | Tony Stockman (14332704) Jesús Requena Carrión (19255792) |
| author2_role | author author |
| author_facet | Mariam Bahameish (19255789) Tony Stockman (14332704) Jesús Requena Carrión (19255792) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mariam Bahameish (19255789) Tony Stockman (14332704) Jesús Requena Carrión (19255792) |
| dc.date.none.fl_str_mv | 2024-05-18T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s24103210 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Strategies_for_Reliable_Stress_Recognition_A_Machine_Learning_Approach_Using_Heart_Rate_Variability_Features/26403097 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning heart rate variability stress recognition affective computing machine learning |
| dc.title.none.fl_str_mv | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s24103210" target="_blank">https://dx.doi.org/10.3390/s24103210</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ed71fe62ed2bc524406b43ffde698770 |
| identifier_str_mv | 10.3390/s24103210 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26403097 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability FeaturesMariam Bahameish (19255789)Tony Stockman (14332704)Jesús Requena Carrión (19255792)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningheart rate variabilitystress recognitionaffective computingmachine learning<p dir="ltr">Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s24103210" target="_blank">https://dx.doi.org/10.3390/s24103210</a></p>2024-05-18T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s24103210https://figshare.com/articles/journal_contribution/Strategies_for_Reliable_Stress_Recognition_A_Machine_Learning_Approach_Using_Heart_Rate_Variability_Features/26403097CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264030972024-05-18T09:00:00Z |
| spellingShingle | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features Mariam Bahameish (19255789) Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning heart rate variability stress recognition affective computing machine learning |
| status_str | publishedVersion |
| title | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| title_full | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| title_fullStr | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| title_full_unstemmed | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| title_short | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| title_sort | Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning heart rate variability stress recognition affective computing machine learning |