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Social Touch Gesture Recognition Using Convolutional Neural Network 时间:2018-10-13 21:21   来源:未知   作者:admin   点击: 次 Abstract: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.
1. Introduction
  Social touch is one of the basic interpersonal methods used to communicate emotions. Social touch classification is a leading research area which has great potential for further improvement and development [1]. Social touch classification can benefit human-robot interaction [2]. The identification of the type (or class) of touch when a human touches a robot’s artificial skin is a demanding yet simple question in this area [3, 4]. A human can easily distinguish and understand social touch. However, an interface with which to record social touch should be developed in human-robot interaction [5–7]. Several attempts have been made to build devices to classify human social touch and record them for the available dataset [8–15]. This paper concentrates on the existing studies that proposed a setup with which to measure the pressure of touch of recorded data and recognize the classes of social touch gesture. This setup, which is used to record the corpus of social touch (CoST), is a type of artificial skin that records the pressure applied on it.
  Previous studies have aimed to identify the touch classes using 14 predefined classes [5–10, 12, 16]. These social gestures consist of grab, hit, massage, pat, pinch, poke, press, rub, scratch, slap, stroke, squeeze, tap, and tickle, which were taken from the Yohanan dictionary [16]. However, they do not satisfy a fully real-time system. Note that even humans need to wait a certain amount of time (e.g., in the order of milliseconds) to understand social touch class [2]. Therefore, this paper aims to classify social touch in a reasonably short time. Consequently, the amount of data (number of frames) on average is necessary to recognize social gestures.
   Another issue is the avoidance of preprocessing, which develops case dependency, and, as previously discussed, prevents real-time performance (e.g., using an average or any measurement which performs temporal abstraction) [17]. This paper introduces a model for the social touch recognition which avoids the data preprocessing step. Thus, the study explores the question “How can social touch be classified by providing raw input samples (sensor data) only instead of a set of features?” Furthermore, the use of sensor data without preprocessing is a challenged task, such that a powerful approach to efficiently classify gesture classes is required.