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Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet.

Lin Shu1, Yang Yu1, Wenzhuo Chen1

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.

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This study introduces a novel method for emotion recognition using heart rate data from smart bracelets. The approach effectively monitors emotional states, paving the way for advanced wearable psychological health tools.

Keywords:
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Area of Science:

  • Wearable technology
  • Affective computing
  • Psychological monitoring

Background:

  • Wearable devices offer potential for psychological health monitoring and human-computer interaction.
  • Current emotion monitoring methods are not suitable for daily use with common smart bracelets or watches.

Purpose of the Study:

  • To develop an effective method for emotion recognition using heart rate data from wearable smart bracelets.
  • To establish a reliable experimental paradigm for emotion stimulation and data collection.

Main Methods:

  • Utilized a 'neutral + target' emotion stimulation paradigm with video stimuli (happy, sad).
  • Collected heart rate data from 25 subjects, establishing a dataset.
  • Employed normalized features of target emotions relative to neutral mood baseline data.
  • Applied Adaboost and Gradient Boosting Decision Tree (GBDT) classifiers.

Main Results:

  • The 'neutral + target' video pair stimulation paradigm proved effective for emotion recognition.
  • The baseline setting using neutral mood data and normalized features demonstrated validity.
  • Adaboost and GBDT classifiers showed effectiveness on the established dataset.

Conclusions:

  • The proposed method successfully enables emotion recognition using heart rate data from wearable smart bracelets.
  • This research supports the development of consumer electronic devices for real-time human emotional mood monitoring.
  • The findings contribute to advancing wearable technology for mental wellness applications.