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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In this study, we focus on emotion recognition for service robots in the living space based on Electrocardiogram (ECG). An emotional state is important information that allows a robot system to provide appropriate services in way that are more in tune with users' needs and preferences. Moreover, the users' emotional state can be feedbacks to evaluate user's level of satisfaction in the services. We apply a diagnosis method that uses both interbeat and within-beat features of ECG. The post hoc tests in Analysis of Variance (ANOVA) showed that our approach satisfies more confidence level of difference between emotions than conventional methods. Our system design was based on wireless and wearable biological sensor for mobility and convenience of users' daily lifestyle.

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