Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet
Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address...
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| Published in | Sensors (Basel, Switzerland) Vol. 20; no. 3; p. 718 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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28.01.2020
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| Abstract | Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A ‘neutral + target’ pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China’s standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of ‘neutral + target’ video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods. |
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| AbstractList | Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A 'neutral + target' pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China's standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of 'neutral + target' video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods. Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A 'neutral + target' pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China's standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of 'neutral + target' video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A 'neutral + target' pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China's standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of 'neutral + target' video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods. |
| Author | Chen, Wenzhuo Hua, Haoqiang Shu, Lin Yu, Yang Xu, Xiangmin Jin, Jianxiu Li, Qin |
| AuthorAffiliation | 3 School of Software Engineering, the Shenzhen Institute of Information Technology, Shenzhen 518172, China; liqin@sziit.edu.cn 1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China; shul@scut.edu.cn (L.S.); 201720212333@mail.scut.edu.cn (Y.Y.); 201821011745@mail.scut.edu.cn (W.C.); 201810101923@mail.scut.edu.cn (H.H.); xmxu@scut.edu.cn (X.X.) 2 Institute of Modern Industrial Technology of SCUT in Zhongshan, Zhongshan 528400, China |
| AuthorAffiliation_xml | – name: 3 School of Software Engineering, the Shenzhen Institute of Information Technology, Shenzhen 518172, China; liqin@sziit.edu.cn – name: 2 Institute of Modern Industrial Technology of SCUT in Zhongshan, Zhongshan 528400, China – name: 1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China; shul@scut.edu.cn (L.S.); 201720212333@mail.scut.edu.cn (Y.Y.); 201821011745@mail.scut.edu.cn (W.C.); 201810101923@mail.scut.edu.cn (H.H.); xmxu@scut.edu.cn (X.X.) |
| Author_xml | – sequence: 1 givenname: Lin surname: Shu fullname: Shu, Lin – sequence: 2 givenname: Yang surname: Yu fullname: Yu, Yang – sequence: 3 givenname: Wenzhuo surname: Chen fullname: Chen, Wenzhuo – sequence: 4 givenname: Haoqiang surname: Hua fullname: Hua, Haoqiang – sequence: 5 givenname: Qin surname: Li fullname: Li, Qin – sequence: 6 givenname: Jianxiu surname: Jin fullname: Jin, Jianxiu – sequence: 7 givenname: Xiangmin surname: Xu fullname: Xu, Xiangmin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32012920$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Accuracy Adult Algorithms China emotion recognition Emotions Emotions - physiology Endocrine system Experiments Facial Expression Female Happiness Heart rate Heart Rate - physiology Humans Male Nervous system Physiology Recognition, Psychology - physiology Sensors smart bracelet wearable Wearable computers Wearable Electronic Devices Young Adult |
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| Title | Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/32012920 https://www.proquest.com/docview/2550453242 https://www.proquest.com/docview/2350909152 https://pubmed.ncbi.nlm.nih.gov/PMC7038485 https://doaj.org/article/90f7db60d8e640bfb3dd725a4d1e4da9 |
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