Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal af...Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning algorithm.The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015.The dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices.The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented.Findings show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same dataset.Therefore,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.展开更多
文摘Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning algorithm.The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015.The dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices.The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented.Findings show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same dataset.Therefore,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.
