The Empatica E3 is a wearable wireless multisensor device for real-time computerized biofeedback and data acquisition. The E3 has four embedded sensors: photoplethysmograph (PPG), electrodermal activity (EDA), 3-axis accelerometer, and temperature. It is small, light and comfortable and it is suitable for almost all real-life applications. The E3 operates both in streaming mode for real-time data processing using a Bluetooth low energy interface and in recording mode using its internal flash memory. With E3, it is possible to conduct research outside of the lab by acquiring continuous data for ambulatory situations in a comfortable and non-distracting way.

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Abstract. This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and selfreported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.

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