Abstract
Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.
Highlights
Researchers in autism spectrum disorders (ASD) fields have attempted several techniques to improve attention assessment for effective learning outcomes
The first is based on geometric feature transformation using an support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach for automated feature extraction and classification
The first is based on geometric-based feature transformation using an SVM classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a CNN approach for automated feature extraction and classification
Summary
Researchers in autism spectrum disorders (ASD) fields have attempted several techniques to improve attention assessment for effective learning outcomes. Attention involves the cognitive and behavioral processing of discrete information while ignoring other information [1]. It is described as the behavioral engagement [2] or cognitive engagement [3] of participants in a learning task. Video data analysis is a common strategy for attention assessment This analysis requires subjective annotation of observed attentional behaviors of participants at the end of a learning session. The method makes real-time attentional support impossible for children with ASD This assessment method is tedious and requires people with high expertise in ASD fields [7,8,9]
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