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Multimodal Biosignal Sensor Data Handling for Emotion Recognition

Canento, F. A. L. ; Fred, A. L. N. ; Silva, H. ; Gamboa, H. ; Lourenço, A.

Multimodal Biosignal Sensor Data Handling for Emotion Recognition, Proc IEEE Sensors, Limerick, Ireland, Vol. , pp. 647 - 651, October, 2011.

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We present an experimental setup, sensor data handling, and evaluation framework for emotion recognition, based on multimodal biosignal sensor data. For labeled data acquisition we developed an emotion elicitation block, with a bank of labeled videos containing different triggering stimuli. A biosignal acquisition apparatus was used to collect multimodal data, namely: Electromyography (EMG); Electrocardiography (ECG); Electrodermal Activity (EDA); Blood Volume Pulse (BVP); Peripheral Temperature (SKT); and Respiration (RESP). An automated biosignal processing and feature extraction toolbox was developed to convert raw data into meaningful parameters. Experimental results revealed trends associated with triggering events, providing a baseline for emotion recognition. Through LOOCV with a k-NN classifier, we obtained recognition rates of 81% to distinguish between positive and negative emotions, and of 70% to distinguish between positive, neutral, and negative emotions.