一种用于儿科人群的舌检测的深度学习方法
A Deep Learning Approach to Tongue Detection for Pediatric Population
摘要：
有严重残疾和复杂沟通需求的儿童在使用接入技术（AT）设备方面面临限制。常规AT（例如，机械开关）对于不会说话的儿童和那些自主运动控制有限的儿童可能是不够的。自动技术检测舌头的姿态是一个很有前途的途径。以前的研究已经表明了舌头检测算法对成年参与者的鲁棒性，但需要进一步的研究将这些方法用于儿童。在这项研究中，舌头出手势识别的网络架构的实现和评估的视频记录在一个自然的设置，当孩子们玩视频游戏。级联对象检测器算法用于检测参与者的面部，并且使用卷积神经网络（CNN）开发了用于舌头手势检测的自动分类方案。在进行的评估实验中，使用成人和儿童的图像训练网络。使用留一主题交叉验证来评估网络分类精度。通过分析五个典型发育儿童的视频获得的初步分类结果显示，在预测吐舌手势方面的准确率高达99%。此外，我们证明了只使用儿童数据训练分类器比成人数据训练分类器的性能更好，这支持了对儿科舌头手势数据集的需求。
英文摘要：
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.
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