Using Electrodermal Activity to Recognize Ease of Engagement in Children during Social Interactions Javier Work supported by the National Science Foundation Grant No. NSF CCF-1029585 1 Hernandez , Ivan 2 Riobo , Agata Experimental Setting Hello! Children’s emotional self-regulation and coregulation are key components in understanding engagement. Ball Book Hat Tickle Affectiva QTM sensor Characterization of EDA Responses 0.55 EDA levels stable Shorter sessions (N: 29) 0.65 0.6 0.55 0.5 0.5 0.4 0.35 0.25 0 40 20 60 80 100 Avg. Time (seconds) 120 0.35 0.3 0 (Responses were resampled to last the average session time) (Benedek and Kaembach, 2010) 20 40 60 80 100 Avg. Time (seconds) 120 160 140 Individual Features (IF); only from child’s EDA Standard Deviation # Peaks Average Peak Amplitude Mean Slope Position Minimum Area under the curve Position Maximum Synchrony Features (SF); from the dyad’s EDA Normalized EDA 0.75 0.5 0.25 Child Adult Tonic/Phasic Components Tonic Phasic 0 0:56 0.4 0.25 140 Processing 1. Normalize EDA values 2. Reduce noise 3. Extract tonic and phasic EDA 0:00 0.45 g EDA levels increase Longer sessions (N: 22) 0.45 0.3 1 0.7 1:12 Time (minutes:seconds) 1:68 2:34 Time Correlation between responses: • Pearson correlation • Canonical Correlation • Dynamic Time Warping Differences between features: • Means • # Peaks • Avg. peak amplitude Average AUCs (ROCs and Precision/Recall) 0.6 Limitations • Specificity • Artifacts • Wireless • Comfortable • 32 Hz • 4 sensors Easier vs Harder to Engage with SVMs Harder to Engage Children 0.75 Tonic/Phasic Components Avg. Normalized EDA (±Standard Error) 0.65 Good Indicator • Arousal • Cognitive Load 23 sessions excluded (artifacts) 51 sessions used for analysis • Easier to engage (N: 29) • Harder to engage (N: 22) Ratings per activity: • 0 – little effort • 1 – some effort • 2 – extensive effort 0.7 Rosalind W. 1 Picard Electrodermal Activity (EDA) 23 sessions exclude due to artifacts 51 clean sessions Engagement groups External coder • Easier to engage (N = 29) “Amount of effort required to get child’s attention” • Harder to engage (N = 22) Can we characterize qualitative aspects of children’s social engagement with wearable biosensors? Easier to Engage Children Gregory D. 2 Abowd , {javierhr, picard}@media.mit.edu – MIT Media Lab1 - {ivan.riobo, agata, abowd}@gatech.edu – Georgia Tech2 Motivation 0.75 2 Rozga , 90 85 Combining Features with Sequential Forward Selection 80 75 70 65 60 55 50 Top feature Top feature Diff. #peaks of dyad from phasic Position max. from tonic IF+SF IF One Feature at a Time Top feature DTW from phasic Diff. #peaks of dyad from phasic Position max. from tonic SF IF SF SF and IF similar performance IF better from tonic SF better from phasic Tonic and Phasic decomposition improved >6% Feature selection improved >11% SF slightly better than IF (>2%) Tonic and phasic equally represented SF and IF are complementary Hernandez J., Riobo I., Rozga A., Abowd G. D., Picard R. W., (2014), Using Electrodermal Activity to Recognize Ease of Engagement in Children during Social Interactions, In Proc. of Inter. Conf. on Ubiquitous Computing, 307-17