Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis
<p>A stressor is an external stimulus that an individual might consider demanding or threatening regarding his/her safety. Stressors cause the release of stress hormones and can be categorized into two categories: physiological and psychological. Wearable technology has been on the rise in the...
محفوظ في:
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| مؤلفون آخرون: | |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513560337448960 |
|---|---|
| author | Sami Elzeiny (16891521) |
| author2 | Marwa Qaraqe (10135172) |
| author2_role | author |
| author_facet | Sami Elzeiny (16891521) Marwa Qaraqe (10135172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sami Elzeiny (16891521) Marwa Qaraqe (10135172) |
| dc.date.none.fl_str_mv | 2021-05-04T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3077358 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automatic_and_Intelligent_Stressor_Identification_Based_on_Photoplethysmography_Analysis/24042477 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Human-centred computing Machine learning Temperature sensors Temperature measurement Temperature distribution Wearable computers Time series analysis Photoplethysmography Feature extraction Stressor Stress state Convolution neural network PPG sensor Interbeat interval BVP signal Spatial domain Frequency domain Image processing Average pixel intensity |
| dc.title.none.fl_str_mv | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>A stressor is an external stimulus that an individual might consider demanding or threatening regarding his/her safety. Stressors cause the release of stress hormones and can be categorized into two categories: physiological and psychological. Wearable technology has been on the rise in the past decade, and the advancement of IoT will only further the benefits that wearable technology will bring. This work leverages the output of wearable technology to provide automatic stress and stressor identification model. In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. The time-series PPG data is transformed into 2D spatial images by first extracting the interbeat interval (IBI), and the blood volume pulse (BVP) features from the PPG, then transforming these data to 2D image data. The developed model successfully identified stress instances using IBI-BVP spatial domain images with an average accuracy of 98.10% with a convolutional neural network (CNN) and 99.18% using the average pixel intensity of these images with the extra trees classifier. It also successfully categorized stressor types into physical, cognitive, and social with an accuracy of 98.5% using the CNN and 96.45% using the extra trees classifier, outperforming state-of-the-art.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3077358" target="_blank">https://dx.doi.org/10.1109/access.2021.3077358</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_310a57cd99f6d5f88721ebbbeab6cc26 |
| identifier_str_mv | 10.1109/access.2021.3077358 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042477 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automatic and Intelligent Stressor Identification Based on Photoplethysmography AnalysisSami Elzeiny (16891521)Marwa Qaraqe (10135172)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesData management and data scienceHuman-centred computingMachine learningTemperature sensorsTemperature measurementTemperature distributionWearable computersTime series analysisPhotoplethysmographyFeature extractionStressorStress stateConvolution neural networkPPG sensorInterbeat intervalBVP signalSpatial domainFrequency domainImage processingAverage pixel intensity<p>A stressor is an external stimulus that an individual might consider demanding or threatening regarding his/her safety. Stressors cause the release of stress hormones and can be categorized into two categories: physiological and psychological. Wearable technology has been on the rise in the past decade, and the advancement of IoT will only further the benefits that wearable technology will bring. This work leverages the output of wearable technology to provide automatic stress and stressor identification model. In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. The time-series PPG data is transformed into 2D spatial images by first extracting the interbeat interval (IBI), and the blood volume pulse (BVP) features from the PPG, then transforming these data to 2D image data. The developed model successfully identified stress instances using IBI-BVP spatial domain images with an average accuracy of 98.10% with a convolutional neural network (CNN) and 99.18% using the average pixel intensity of these images with the extra trees classifier. It also successfully categorized stressor types into physical, cognitive, and social with an accuracy of 98.5% using the CNN and 96.45% using the extra trees classifier, outperforming state-of-the-art.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3077358" target="_blank">https://dx.doi.org/10.1109/access.2021.3077358</a></p>2021-05-04T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3077358https://figshare.com/articles/journal_contribution/Automatic_and_Intelligent_Stressor_Identification_Based_on_Photoplethysmography_Analysis/24042477CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240424772021-05-04T00:00:00Z |
| spellingShingle | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis Sami Elzeiny (16891521) Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Human-centred computing Machine learning Temperature sensors Temperature measurement Temperature distribution Wearable computers Time series analysis Photoplethysmography Feature extraction Stressor Stress state Convolution neural network PPG sensor Interbeat interval BVP signal Spatial domain Frequency domain Image processing Average pixel intensity |
| status_str | publishedVersion |
| title | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| title_full | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| title_fullStr | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| title_full_unstemmed | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| title_short | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| title_sort | Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Human-centred computing Machine learning Temperature sensors Temperature measurement Temperature distribution Wearable computers Time series analysis Photoplethysmography Feature extraction Stressor Stress state Convolution neural network PPG sensor Interbeat interval BVP signal Spatial domain Frequency domain Image processing Average pixel intensity |