Preliminary classification of cognitive load states in a human machine interaction scenario

In this work, different cognitive load situations are examined and classified in the context of a Human Computer Interaction (HCI) scenario. Machine learning methods were used to detect three cognitive load states (overload, underload, normal load) with the help of five different psychophysiological signals (ECG, EMG, Respiration, GSR, Temperature). At first it is shown, that the three regarded states can be clearly distinguished in the Valence-Arousal-Dominance space (VAD). After this comparisons between a 10-fold-valdidation and a batch-validation as well as three different classifiers (k-Nearest-Neighbour, Naive Bayes, Random Forest) are accomplished. At last the influence of gender in contrast to an overall analysis is shown.

[1]  Andreas Wendemuth,et al.  Companion-Technology for Cognitive Technical Systems , 2011, KI - Künstliche Intelligenz.

[2]  Stefanie Rukavina,et al.  Affective Computing and the Impact of Gender and Age , 2016, PloS one.

[3]  A. Kistler,et al.  Fingertip temperature as an indicator for sympathetic responses. , 1998, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[4]  Frank Honold,et al.  In-Depth Analysis of Multimodal Interaction: An Explorative Paradigm , 2016, HCI.

[5]  P. Ekman.  An argument for basic emotions , 1992 .

[6]  Patrick Thiam,et al.  Active Learning for Speech Event Detection in HCI , 2016, ANNPR.

[7]  Hugo Silva,et al.  Multimodal biosignal sensor data handling for emotion recognition , 2011, 2011 IEEE SENSORS Proceedings.

[8]  Jonghwa Kim,et al.  Transsituational Individual-Specific Biopsychological Classification of Emotions , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Andreas Wendemuth,et al.  Von kognitiven technischen Systemen zu Companion-Systemen , 2010, KI - Künstliche Intelligenz.

[11]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[12]  M. Bradley,et al.  Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.

[13]  H. Traue,et al.  Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines , 2015, PloS one.