Beschreibung
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.
Autorenporträt
Bachelor's degree in computer engineering from the Technological University/Iraq in 1998. He obtained a master's degree in computer engineering and information technology from the Technological University in Iraq in 2005. He worked as a teacher in Diyala University, Faculty of Engineering, Department of Computer Engineering from 2005 to 2013.
Herstellerkennzeichnung:
BoD - Books on Demand
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