  Children with severe disabilities and complex communication needs face
limitations in the usage of access technology (AT) devices. Conventional ATs
(e.g., mechanical switches) can be insufficient for nonverbal children and
those with limited voluntary motion control. Automatic techniques for the
detection of tongue gestures represent a promising pathway. Previous studies
have shown the robustness of tongue detection algorithms on adult participants,
but further research is needed to use these methods with children. In this
study, a network architecture for tongue-out gesture recognition was
implemented and evaluated on videos recorded in a naturalistic setting when
children were playing a video-game. A cascade object detector algorithm was
used to detect the participants' faces, and an automated classification scheme
for tongue gesture detection was developed using a convolutional neural network
(CNN). In evaluation experiments conducted, the network was trained using
adults and children's images. The network classification accuracy was evaluated
using leave-one-subject-out cross-validation. Preliminary classification
results obtained from the analysis of videos of five typically developing
children showed an accuracy of up to 99% in predicting tongue-out gestures.
Moreover, we demonstrated that using only children data for training the
classifier yielded better performance than adult's one supporting the need for
pediatric tongue gesture datasets.
