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      Intimate Heartbeats: Opportunities for Affective Communication Technology

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          Toward machine emotional intelligence: analysis of affective physiological state

          R.W. Picard, E. Vyzas, Kristopher J. Healey (2001)
          Article 0 comments Cited 294 times – based on 0 reviews     Review now
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            Emotion recognition system using short-term monitoring of physiological signals.

            Byoung Kim, Christine H J Kim, W. Bang (2004)
            A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.
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              Heart rate variability, trait anxiety, and perceived stress among physically fit men and women.

              Rod Dishman, Yoshio Nakamura, Melissa E. Garcia … (2000)
              It is unclear from prior reports whether the relationships between self-ratings of anxiety or emotional stress and parasympathetic nervous system components of heart rate variability are independent of personality and cardiorespiratory fitness. We examined those relationships in a clinical setting prior to a standardized exercise test. Heart rate variability (HRV) was measured during 5 min of supine rest among 92 healthy men (N=52) and women (N=40) who had above-average cardiorespiratory fitness as indicated by peak oxygen uptake measured during grade-incremented treadmill exercise. HRV datasets were decomposed into low-frequency (LF; 0.05-0.15 Hz) and high-frequency (HF; 0.15-0.5 Hz) components using spectral analysis. Self-ratings of trait anxiety and perceived emotional stress during the past week were also assessed. There was an inverse relationship between perceived emotional stress during the past week and the normalized HF component of HRV (P=0.038). This indicates a lower cardiac vagal component of HRV among men and women who perceived more stress. That relationship was independent of age, gender, trait anxiety, and cardiorespiratory fitness. It was also independent of heart rate; mean arterial blood pressure; and respiration rate, factors which can influence HRV and might be elevated among people reporting anxiety and perceived stress. We conclude that vagal modulation of heart period appears to be sensitive to the recent experience of persistent emotional stress, regardless of a person's level of physical fitness and disposition toward experiencing anxiety.
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                Author and article information

                Journal
                Title: IEEE Transactions on Affective Computing
                Abbreviated Title: IEEE Trans. Affective Comput.
                Publisher: Institute of Electrical and Electronics Engineers (IEEE)
                ISSN (Print): 1949-3045
                Publication date Created: July 2010
                Publication date (Print): July 2010
                Volume: 1
                Issue: 2
                Pages: 72-80
                Article
                DOI: 10.1109/T-AFFC.2010.13
                SO-VID: 08e4ef99-a530-43d3-a648-4e17827eb040
                Copyright © © 2010
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