IPRA—ENHANCING THE SENSING ABILITIES OF AMBIENT INTELLIGENCE

The ability of ambient intelligence to serve a person could be considerably enhanced, if intelligent devices would take into account the person's current cognitive and affective state. One primary source for state information are physiological measures. However, obtaining state information requires concertedly employing a multitude of different techniques. To facilitate the use of physiological signals as sources of state information and thereby allow improving ambient intelligence, we developed Ipra, an integrated pattern recognition approach which (a) employs all techniques necessary to appropriately analyze physiological data, (b) is broadly applicable, and (c) is easy to use even for nonexperts. This approach together with results of its first application are presented in this contribution.

[1]  Bruno Verschuere,et al.  Psychophysiological Analysis (PSPHA): A modular script-based program for analyzing psychophysiological data , 2006, Behavior research methods.

[2]  A. Kramer,et al.  Physiological metrics of mental workload: A review of recent progress , 1990, Multiple-task performance.

[3]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[4]  Nilanjan Sarkar,et al.  Anxiety-based affective communication for implicit human–machine interaction , 2022 .

[5]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Kara A. Latorella,et al.  The Scope and Importance of Human Interruption in Human-Computer Interaction Design , 2002, Hum. Comput. Interact..

[7]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[8]  Graham Clarke,et al.  A user-independent real-time emotion recognition system for software agents in domestic environments , 2007, Eng. Appl. Artif. Intell..

[9]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[10]  U. Erdmann,et al.  Auditory probe sensitivity to mental workload changes - an event-related potential study. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[11]  Satosi Watanabe,et al.  Pattern Recognition: Human and Mechanical , 1985 .

[12]  Anthony Jameson,et al.  Assessing Cognitive Load in Adaptive Hypermedia Systems: Physiological and Behavioral Methods , 2004, AH.

[13]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[14]  C McFarlaneDaniel,et al.  The scope and importance of human interruption in human-computer interaction design , 2002 .

[15]  Anthony Jameson,et al.  An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks , 2004, Int. J. Intell. Syst..

[16]  Robert P. W. Duin,et al.  PRTools - Version 3.0 - A Matlab Toolbox for Pattern Recognition , 2000 .

[17]  Dana Kulic,et al.  Physiological and subjective responses to articulated robot motion , 2006, Robotica.

[18]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[19]  Cullen Schaffer,et al.  A Conservation Law for Generalization Performance , 1994, ICML.

[20]  Daniel Gopher,et al.  Workload: An examination of the concept. , 1986 .

[21]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[22]  Sergio Bermejo,et al.  Learning with Nearest Neighbour Classifiers , 2001, Neural Processing Letters.

[23]  Gerald Sommer,et al.  Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  F. Thomas Eggemeier,et al.  Workload assessment methodology. , 1986 .

[25]  Frank H. Wilhelm,et al.  Continuous electronic data capture of physiology, behavior and experience in real life: towards ecological momentary assessment of emotion , 2006, Interact. Comput..

[26]  Barry H. Kantowitz,et al.  Mental Workload , 2020, Encyclopedia of Behavioral Medicine.

[27]  Lluís A. Belanche Muñoz,et al.  Evaluating Feature Selection Algorithms , 2002, CCIA.

[28]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[29]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[30]  Gerard V. Trunk,et al.  A Problem of Dimensionality: A Simple Example , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Christine L. Lisetti,et al.  Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals , 2004, EURASIP J. Adv. Signal Process..

[32]  Graham Wilson,et al.  Psychophysiological assessment of workload in multi-task environments , 2020 .

[33]  Ron Kohavi,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998 .

[34]  A Dynamic LVQ Algorithm for Improving the Generalisation of Nearest Neighbour Classifiers , 2000 .

[35]  Juha Reunanen,et al.  Overfitting in Making Comparisons Between Variable Selection Methods , 2003, J. Mach. Learn. Res..

[36]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[37]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[38]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[39]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[40]  Rosalind W. Picard.  Toward computers that recognize and respond to user emotion , 2000, IBM Syst. J..

[41]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[42]  Jan M. Van Campenhout,et al.  On the Possible Orderings in the Measurement Selection Problem , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[43]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[44]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[45]  A. K. Jain,et al.  A critical evaluation of intrinsic dimensionality algorithms. , 1980 .

[46]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[47]  Robert P. W. Duin,et al.  An Evaluation of Intrinsic Dimensionality Estimators , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  T. Scherer,et al.  Constraints for emotion specificity in fear and anger: the context counts. , 2001, Psychophysiology.

[49]  Sergio Bermejo,et al.  A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification , 2004, Neural Processing Letters.

[50]  E Donchin,et al.  Resource reciprocity: an event-related brain potentials analysis. , 1989, Acta psychologica.

[51]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  T. D. Clark,et al.  Remote detection of human electroencephalograms using ultrahigh input impedance electric potential sensors , 2002 .