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  • Utilization of Real-Time Heart Rate Changes to Classify Emotions Based on Low-Cost Device Development
  • https://doi.org/10.1109/iciibms52876.2021.9651613Copy DOI Icon

Utilization of Real-Time Heart Rate Changes to Classify Emotions Based on Low-Cost Device Development

  • Nov 25, 2021
  • Pramudya Rakhmadyansyah Sofyan +5 more
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Abstract

Emotion is a complex phenomenon and has an essential role in supporting the quality of life and affecting motivation, perception, cognition, creativity, empathy, education, and making an important decision. Also, negative emotions such as anger, shame, and anxiety usually arise from stress, and it is referring to destructive and threatening to show a relationship between them. Therefore, research on recognizing emotions is still an important issue currently. Fifteen males university students have participated in this study. A fingertip photoplethysmograph (PPG) was used to record the heart rate variable. This study used The International Affective Picture System (IAPS) to induce particular emotions. As a subjective evaluation, we employed Self-Assessment Manikin (SAM) to evaluate the subject's emotions. Then, we compared the SAM evaluation to their heart rate properties for further investigation. The FIR Band Pass Filter was established as a pre-processing method, and a single feature was extracted from a PPG recording called Heart Rate Changes (HCR). Instead of utilizing artificial intelligence or other machine learning classification techniques, we performed if-else classification to recognize negative and positive emotions as basic emotions. We found that the HCR was significantly different between the positive and negative emotions (p<0.0001). We achieved ± 50% of accuracy while classifying the two basic emotions. We validated the results by implementing a real-time scheme detection based on a low-cost device and display it on a user-built application. Current results need improvement in future studies. We concluded that a single feature without any advanced methods is still a promising approach for real-time detection. The system was also reliable in future applications on low-cost device development.

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