Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables
Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-...
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MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/13/1550 |
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doaj-a81da155ed074d50adb323f87f7a23922021-07-15T15:32:28ZengMDPI AGElectronics2079-92922021-06-01101550155010.3390/electronics10131550Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical VariablesAlexandros Liapis0Evanthia Faliagka1Christos P. Antonopoulos2Georgios Keramidas3Nikolaos Voros4Electrical & Computer Engineering Department, University of Peloponnese, 26 334 Patras, GreeceElectrical & Computer Engineering Department, University of Peloponnese, 26 334 Patras, GreeceElectrical & Computer Engineering Department, University of Peloponnese, 26 334 Patras, GreeceSchool of Informatics, Aristotle University of Thessaloniki, 54 124 Thessaloniki, GreeceElectrical & Computer Engineering Department, University of Peloponnese, 26 334 Patras, GreecePhysiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.https://www.mdpi.com/2079-9292/10/13/1550stress detectionUX evaluationelectrodermal activitydeep learningentity embeddings |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Alexandros Liapis Evanthia Faliagka Christos P. Antonopoulos Georgios Keramidas Nikolaos Voros |
| spellingShingle |
Alexandros Liapis Evanthia Faliagka Christos P. Antonopoulos Georgios Keramidas Nikolaos Voros Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables Electronics stress detection UX evaluation electrodermal activity deep learning entity embeddings |
| author_facet |
Alexandros Liapis Evanthia Faliagka Christos P. Antonopoulos Georgios Keramidas Nikolaos Voros |
| author_sort |
Alexandros Liapis |
| title |
Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables |
| title_short |
Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables |
| title_full |
Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables |
| title_fullStr |
Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables |
| title_full_unstemmed |
Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables |
| title_sort |
advancing stress detection methodology with deep learning techniques targeting ux evaluation in aal scenarios: applying embeddings for categorical variables |
| publisher |
MDPI AG |
| series |
Electronics |
| issn |
2079-9292 |
| publishDate |
2021-06-01 |
| description |
Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation. |
| topic |
stress detection UX evaluation electrodermal activity deep learning entity embeddings |
| url |
https://www.mdpi.com/2079-9292/10/13/1550 |
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