We report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27% improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments.
10 aAlgorithms 10 aautonomously operated robot 10 aEarly Childhood Education Center 10 aEducational institutions 10 aEducational robots 10 aGames 10 ahuman factors 10 aHuman-robot interaction 10 aintervention period 10 aPediatrics 10 aRobot sensing systems 10 arobotics 10 asociable robot 10 asocial aspects of automation 10 atime 2 week 10 atoddler vocabulary skills 10 aUbiquitous computering 10 aVocabulary 1 aMovellan, J. 1 aEckhardt, M. 1 aVirnes, M. 1 aRodriguez, A uhttps://rubi.ucsd.edu/content/sociable-robot-improves-toddler-vocabulary-skills 03033nas a2200565 4500008004100000022001400041245003700055210003700092260001200129300001400141490000700155520134500162653001501507653002801522653001401550653005301564653003501617653001301652653002401665653002001689653002201709653000901731653003301740653002101773653002701794653004801821653002801869653002001897653002201917653002501939653004401964653003902008653003202047653002802079653001302107653002102120653003002141653003102171653003202202653001202234653002602246653002202272653002102294100001802315700001902333700001402352700001702366700001702383856006702400 2009 eng d a0162-8828 00 aToward Practical Smile Detection 0 aToward Practical Smile Detection c11/2009 a2106-2111 0 v31 3 aMachine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.
10 aAlgorithms 10 aArtificial intelligence 10 aAutomated 10 aautomatic facial expression recognition research 10 aBiological Pattern Recognition 10 aBiometry 10 aComputer simulation 10 aComputer vision 10 aComputer-Assisted 10 aFace 10 aFace and gesture recognition 10 aface recognition 10 afeature representation 10 ahuman-level expression recognition accuracy 10 aillumination conditions 10 aImage databases 10 aImage Enhancement 10 aImage Interpretation 10 aimage registration image representation 10 alearning (artificial intelligence) 10 amachine learning approaches 10 aMachine Learning Models 10 an Humans 10 aobject detection 10 apractical smile detection 10 aReproducibility of Results 10 aSensitivity and Specificity 10 aSmiling 10 aSubtraction Technique 10 atraining data set 10 avisual databases 1 aWhitehill, J. 1 aLittlewort, G. 1 aFasel, I. 1 aBartlett, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/toward-practical-smile-detection