Social Touch Gesture Recognition Using Convolutional Neural Network

• Published in: Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 10
• Main Authors: Ucan, Osman N., Al-Azawi, Saad, Bayat, Oguz, Albawi, Saad
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Artificial neural networks; Classification; Data processing; Datasets; Female; Gesture recognition; Gestures; Handwriting; Human engineering; Humans; International conferences; Interpersonal Relations; Male; Methods; Neural networks; Neural Networks (Computer); Pattern recognition; Pattern Recognition, Automated - methods; Probability distribution; Robotics; Robots; Sensors; Social Behavior; Social interactions; Social organization; Tactile stimuli; Touch
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2018/6973103

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.