Eight-Emotion Sentics Data (2000)
This was the first data set generated as part of the MIT Affective Computing Group's research. The research question motivating the collection of this particular data was: Will physiological signals exhibit characteristic patterns when a person experiences different kinds of emotional feelings? In particular, we wanted to know if patterns could be found day-in day-out, for a single individual, that could distinguish a set of eight affective states (in contrast with prior emotion-physiology research, which focused on averaging results of lots of people over a single session of less than an hour.) We wanted to know if it might be possible to build a wearable computer system that could learn how to discriminate an individual's affective patterns, based on skin-surface sensing. We did build such a system, which attained 81% classification accuracy among the eight states studied for this data set. The pattern recognition aspects of this work are described in more detail in the article:
Rosalind W. Picard, Elias Vyzas, and Jennifer Healey (2001), "Toward Machine Emotional Intelligence: Analysis of Affective Physiological State," IEEE Transactions Pattern Analysis and Machine Intelligence, Vol 23, No. 10, Oct. 2001.
These data are now available to other researchers. The data consist of measurements of four physiological signals and eight affective states, taken once a day, in a session lasting around 25 minutes, for over twenty days of recordings. The four physiological signals are: blood volume pulse, electromyogram, respiration and skin conductance. The eight states (from the Clynes sentograph protocol) are: neutral, anger, hate, grief, love, romantic love, joy, and reverence. The data are divided into two overlapping sets, which are described in more detail in Healey's PhD thesis (2000): Wearable and Automotive Systems for the Recognition of Affect from Physiology.
The data may be downloaded here: