Using electrodermal activity to recognize ease of engagement in children during social interactions

The recent emergence of comfortable wearable sensors has focused almost entirely on monitoring physical activity, ignoring opportunities to monitor more subtle phenomena, such as the quality of social interactions. We argue that it is compelling to address whether physiological sensors can shed light on quality of social interactive behavior. This work leverages the use of a wearable electrodermal activity (EDA) sensor to recognize ease of engagement of children during a social interaction with an adult. In particular, we monitored 51 child-adult dyads in a semi-structured play interaction and used Support Vector Machines to automatically identify children who had been rated by the adult as more or less difficult to engage. We report on the classification value of several features extracted from the child's EDA responses, as well as several other features capturing the physiological synchrony between the child and the adult.

[1]  P. Venables,et al.  Direct measurement of skin conductance: a proposal for standardization. , 1971, Psychophysiology.

[2]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .

[3]  J. Gottman,et al.  Marital interaction: physiological linkage and affective exchange. , 1983, Journal of personality and social psychology.

[4]  Annie Lang.  Involuntary Attention and Physiological Arousal Evoked by Structural Features and Emotional Content in TV Commercials , 1990 .

[5]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[6]  P. Mundy.  Joint attention and social-emotional approach behavior in children with autism , 1995, Development and Psychopathology.

[7]  E. Hatfield,et al.  Emotional Contagion , 1995 .

[8]  R. Levenson,et al.  Physiological aspects of emotional knowledge and rapport. , 1997 .

[9]  Michel Wedel,et al.  Eye Fixations on Advertisements and Memory for Brands: A Model and Findings , 2000 .

[10]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[11]  P. Mundy,et al.  Joint Attention and Neurodevelopmental Models of Autism , 2005 .

[12]  Yi-Min Huang,et al.  Weighted support vector machine for classification with uneven training class sizes , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[13]  K. Poels,et al.  How to Capture the Heart? Reviewing 20 Years of Emotion Measurement in Advertising , 2006 .

[14]  Yuxiao Hu,et al.  Head Pose Estimation in Seminar Room Using Multi View Face Detectors , 2006, CLEAR.

[15]  R. Feldman.  Parent-infant synchrony and the construction of shared timing; physiological precursors, developmental outcomes, and risk conditions. , 2007, Journal of child psychology and psychiatry, and allied disciplines.

[16]  C. Marci,et al.  Physiologic Correlates of Perceived Therapist Empathy and Social-Emotional Process During Psychotherapy , 2007, The Journal of nervous and mental disease.

[17]  Rainer Stiefelhagen,et al.  Deducing the visual focus of attention from head pose estimation in dynamic multi-view meeting scenarios , 2008, ICMI '08.

[18]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[19]  M. Benedek,et al.  Decomposition of skin conductance data by means of nonnegative deconvolution , 2010, Psychophysiology.

[20]  Deborah F. Deckner,et al.  Early Interests and Joint Engagement in Typical Development, Autism, and Down Syndrome , 2010, Journal of autism and developmental disorders.

[21]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[22]  Kang Ryoung Park,et al.  Vision-based method for detecting driver drowsiness and distraction in driver monitoring system , 2011 .

[23]  Javier Hernandez,et al.  Call Center Stress Recognition with Person-Specific Models , 2011, ACII.

[24]  Barbara C. N. Müller,et al.  Neural correlates of emotional synchrony. , 2011, Social cognitive and affective neuroscience.

[25]  W. Boucsein.  Electrodermal activity, 2nd ed. , 2012 .

[26]  M. Salminen,et al.  Social Interaction in Games , 2012 .

[27]  Matthew S. Goodwin,et al.  MEASURING AUTONOMIC AROUSAL DURING THERAPY , 2012 .

[28]  Fernando Silveira,et al.  Predicting audience responses to movie content from electro-dermal activity signals , 2013, UbiComp.

[29]  James M. Rehg,et al.  Decoding Children's Social Behavior , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Geoff Hulten,et al.  Measuring the engagement level of TV viewers , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[31]  Matthew S. Goodwin,et al.  A non-homogeneous poisson process model of Skin Conductance Responses integrated with observed regulatory behaviors for Autism intervention , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).