Advancing human wellbeing by developing new ways to communicate, understand, and respond to emotion

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: 

Permission is granted to use these data for research purposes. If your research with this data is published, please reference the data as: "Jennifer Healey and Rosalind W. Picard (2002), Eight-emotion Sentics Data, MIT Affective Computing Group, https://www.media.mit.edu/groups/affective-computing."

Earlier publications where we used these data are: 

• Elias Vyzas (1999), "Recognition of Emotional and Cognitive States Using Physiological Data", Mechanical Engineer's Degree Thesis, MIT, June 1999. TR 510.
• Elias Vyzas and Rosalind W. Picard (1999), "Offline and Online Recognition of Emotion Expression from Physiological Data", Workshop on Emotion-Based Agent Architectures, Third Int. Conf. on Autonomous Agents, May 1, 1999, Seattle WA. TR 488.
• Elias Vyzas and Rosalind W. Picard (1998), "Affective Pattern Classification", 1998 AAAI Fall Symposium Series: Emotional and Intelligent: The Tangled Knot of Cognition, October 23-25, 1998, Orlando, Florida. TR 473.
• Jennifer Healey and Rosalind Picard (1997), "Digital Processing of Affective Signals", Proceedings of ICASSP, Seattle, Washington, May 12-15, 1998. TR 444 .

Driver Stress Data (2000)

Four types of physiological sensors were used to monitor drivers in natural driving situations that included rest, city driving and highway driving tasks. Each drive lasted for approximately ninety minutes. Data was recorded using an electromyogram placed on the shoulder, an electrocardiograph rhythm trace, a sensor for detecting respiration through chest cavity expansion and skin conductance sensors on both the hand and the foot. During the drive, several video cameras were used to record the driver's facial expression, body motion and road conditions. These videos were synchronized to the physiological records to provide ground truth for analysis. A set of hand annotations of these videos has been partly created. This data has been given to be placed for free use by researchers on PhysioNet, as will the annotations when they are completed.

These data are described in more detail in Healey's PhD thesis (2000): Wearable and Automotive Systems for the Recognition of Affect from Physiology.

The data can be downloaded from: PhysioNet. The data includes recordings of the "marker" signal, which reflects the road type annotation: city, highway, and parking.  More details about the time intervals  corresponding to the 10 complete datasets can be found in the work of Akbas (2011). The time intervals were double-checked as part of  El Haouij's PhD thesis (2018). 

Permission is granted to use these data for research purposes. If your research with this data is published, please reference the data as: "Jennifer Healey and Rosalind W. Picard (2002), Driver Stress Data, MIT Affective Computing Group, https://www.media.mit.edu/groups/affective-computing."

AffectiveROAD Data (2018)

A real world driving protocol, inspired from the work of Healey (2000), was conducted in Grand Tunis. The collected data resulted in different physiological signals issued from 2 Empatica E4 sensors placed on the right and left wrists of the driver and from Zephyr Bioharness 3.0 chest belt. The annotation of the different driving areas is provided with the physiological data. A subjective continuous stress metric, real-time built while drive performance and validated post experience, is also included in the dataset.

The sets of data, named AffectiveROAD, are described in more detail in this paper and El Haouij's PhD thesis (2018): Biosignals for driver’s stress level assessment: functional variable selection and fractal characterization.

The data can be downloaded from AffectiveROAD_Data.zip.

Permission is granted to use these data for research purposes. If your research with this data is published, please reference the data as: "Neska El Haouij, Jean-Michel Poggi, Sylvie Sevestre-Ghalila, Raja Ghozi, and Mériem Jaïdane. 2018. AffectiveROAD system and database to assess driver's attention. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC '18). ACM, New York, NY, USA, 800-803. DOI: https://doi.org/10.1145/3167132.3167395”