Key result
An eleven-feature ECG approach improved the correct classification ratio of six emotional states to 61.44% compared to 37.23% with a conventional three-feature approach.
Absolute Event Rate: 61.44% vs 37.23%
An 11-feature ECG-based emotion recognition system significantly improves classification accuracy over conventional methods, enabling wireless emotion monitoring for service robots.
May advance multi-feature ECG emotion detection; leaves open clinical or robotic utility pending validation.
This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG’s inter-beat features together with within-beat features in our recognition system. In order to reduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject’s skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject’s activities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.
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Rattanyu et al. (2011) studied Emotion recognition (n=12). Eleven feature ECG approach vs. Conventional three feature approach was evaluated on Correct Classification Ratio (CCR) for simultaneous classification of six emotional states. An eleven-feature ECG approach improved the correct classification ratio of six emotional states to 61.44% compared to 37.23% with a conventional three-feature approach.
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