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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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 20; no. 3; p. 718
Main Authors Shu, Lin, Yu, Yang, Chen, Wenzhuo, Hua, Haoqiang, Li, Qin, Jin, Jianxiu, Xu, Xiangmin
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 28.01.2020
MDPI
Subjects
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
China
heart rate
wearable
smart bracelet
emotion recognition
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s20030718