Altinbas University, Graduate School of Science and Engineering, Istanbul, Turkey.
University of Diyala, College of Engineering, Diyala, Iraq.
Comput Intell Neurosci. 2018 Oct 8;2018:6973103. doi: 10.1155/2018/6973103. eCollection 2018.
Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.
最近，社会触摸手势识别已被视为触摸模态的一个重要课题，它可以实现高效、逼真的人机交互。在本文中，选择了深度卷积神经网络来实现仅使用原始输入样本（传感器数据）的社会触摸识别系统。使用以前由许多执行不同社会手势的主体测量的数据集来执行触摸手势识别。该数据集被称为社会触摸语料库，其中在人体模型手臂上执行了触摸。采用留一受试者交叉验证方法来评估系统性能。在采集最小帧数后，该方法可以近乎实时地识别手势（从原始帧数中获取的帧数长度的平均范围为 0.2%至 4.19%），分类准确率为 63.7%。在现有算法的性能方面，所实现的分类准确率具有竞争力。此外，与其他分类算法相比，在不进行数据预处理的情况下，对于相同的数据集，该系统在分类比和触摸识别时间方面表现更优。
