Multiclass emotion prediction using heart rate and virtual reality stimuli
Background: Emotion prediction is a method that recognizes the human emotion derived from the subject’s psychological data. The problem in question is the limited use of heart rate (HR) as the prediction feature through the use of common classifiers such as Support Vector Machine (SVM), K-Nearest...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Springer Open
2021
|
| Online Access: | https://eprints.ums.edu.my/id/eprint/26840/1/Multiclass%20emotion%20prediction%20using%20heart%20abstract.pdf https://eprints.ums.edu.my/id/eprint/26840/2/Multiclass%20emotion%20prediction%20using%20heart.pdf |
| _version_ | 1825714063901982720 |
|---|---|
| author | Aaron Frederick Bulagang James Mountstephens Jason Teo |
| author_facet | Aaron Frederick Bulagang James Mountstephens Jason Teo |
| author_sort | Aaron Frederick Bulagang |
| collection | UMS |
| description | Background: Emotion prediction is a method that recognizes the human emotion
derived from the subject’s psychological data. The problem in question is the limited
use of heart rate (HR) as the prediction feature through the use of common classifiers
such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest
(RF) in emotion prediction. This paper aims to investigate whether HR signals can be
utilized to classify four-class emotions using the emotion model from Russell’s in a
virtual reality (VR) environment using machine learning.
Method: An experiment was conducted using the Empatica E4 wristband to acquire
the participant’s HR, a VR headset as the display device for participants to view the
360° emotional videos, and the Empatica E4 real-time application was used during the
experiment to extract and process the participant’s recorded heart rate.
Findings: For intra-subject classification, all three classifiers SVM, KNN, and RF
achieved 100% as the highest accuracy while inter-subject classification achieved
46.7% for SVM, 42.9% for KNN and 43.3% for RF.
Conclusion: The results demonstrate the potential of SVM, KNN and RF classifiers
to classify HR as a feature to be used in emotion prediction in four distinct emotion
classes in a virtual reality environment. The potential applications include interactive
gaming, affective entertainment, and VR health rehabilitation.
Keywords: Four-class emotion classification, Electrocardiography, Machine learning,
Virtual reality, HR, Empatica E4, KNN, RF, SVM |
| first_indexed | 2024-03-06T03:06:14Z |
| format | Article |
| id | ums.eprints-26840 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:06:14Z |
| publishDate | 2021 |
| publisher | Springer Open |
| record_format | dspace |
| spelling | ums.eprints-268402021-04-28T07:38:11Z https://eprints.ums.edu.my/id/eprint/26840/ Multiclass emotion prediction using heart rate and virtual reality stimuli Aaron Frederick Bulagang James Mountstephens Jason Teo Background: Emotion prediction is a method that recognizes the human emotion derived from the subject’s psychological data. The problem in question is the limited use of heart rate (HR) as the prediction feature through the use of common classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) in emotion prediction. This paper aims to investigate whether HR signals can be utilized to classify four-class emotions using the emotion model from Russell’s in a virtual reality (VR) environment using machine learning. Method: An experiment was conducted using the Empatica E4 wristband to acquire the participant’s HR, a VR headset as the display device for participants to view the 360° emotional videos, and the Empatica E4 real-time application was used during the experiment to extract and process the participant’s recorded heart rate. Findings: For intra-subject classification, all three classifiers SVM, KNN, and RF achieved 100% as the highest accuracy while inter-subject classification achieved 46.7% for SVM, 42.9% for KNN and 43.3% for RF. Conclusion: The results demonstrate the potential of SVM, KNN and RF classifiers to classify HR as a feature to be used in emotion prediction in four distinct emotion classes in a virtual reality environment. The potential applications include interactive gaming, affective entertainment, and VR health rehabilitation. Keywords: Four-class emotion classification, Electrocardiography, Machine learning, Virtual reality, HR, Empatica E4, KNN, RF, SVM Springer Open 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/26840/1/Multiclass%20emotion%20prediction%20using%20heart%20abstract.pdf text en https://eprints.ums.edu.my/id/eprint/26840/2/Multiclass%20emotion%20prediction%20using%20heart.pdf Aaron Frederick Bulagang and James Mountstephens and Jason Teo (2021) Multiclass emotion prediction using heart rate and virtual reality stimuli. Journal Of Big Data, 8 (12). pp. 1-12. ISSN 2196-1115 https://doi.org/10.1186/s40537-020-00401-x |
| spellingShingle | Aaron Frederick Bulagang James Mountstephens Jason Teo Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title | Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title_full | Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title_fullStr | Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title_full_unstemmed | Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title_short | Multiclass emotion prediction using heart rate and virtual reality stimuli |
| title_sort | multiclass emotion prediction using heart rate and virtual reality stimuli |
| url | https://eprints.ums.edu.my/id/eprint/26840/1/Multiclass%20emotion%20prediction%20using%20heart%20abstract.pdf https://eprints.ums.edu.my/id/eprint/26840/2/Multiclass%20emotion%20prediction%20using%20heart.pdf |
| work_keys_str_mv | AT aaronfrederickbulagang multiclassemotionpredictionusingheartrateandvirtualrealitystimuli AT jamesmountstephens multiclassemotionpredictionusingheartrateandvirtualrealitystimuli AT jasonteo multiclassemotionpredictionusingheartrateandvirtualrealitystimuli |