A vision-based gesture recognition system for human-robot interaction

2009 ◽  
Author(s):  
Jianjie Zhang ◽  
Mingguo Zhao
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Robot Interaction

scholarly journals Online control programming algorithm for human–robot interaction system with a novel real-time human gesture recognition method

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986176 ◽  
Author(s):  
Bo Chen ◽  
Chunsheng Hua ◽  
Bo Dai ◽  
Yuqing He ◽  
Jianda Han
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Robot Control ◽  
Recognition System ◽  
Online Control ◽  
Human Robot Interaction ◽  
Programming Algorithm ◽  
Recognition Method ◽  
Robot Interaction ◽  
Real Time Processing

This article proposes an online control programming algorithm for human–robot interaction systems, where robot actions are controlled by the recognition results of gestures performed by human operators based on visual images. In contrast to traditional robot control systems that use pre-defined programs to control a robot where the robot cannot change its tasks freely, this system allows the operator to train online and replan human–robot interaction tasks in real time. The proposed system is comprised of three components: an online personal feature pretraining system, a gesture recognition system, and a task replanning system for robot control. First, we collected and analyzed features extracted from images of human gestures and used those features to train the recognition program in real time. Second, a multifeature cascade classifier algorithm was applied to guarantee both the accuracy and real-time processing of our gesture recognition method. Finally, to confirm the effectiveness of our algorithm, we selected a flight robot as our test platform to conduct an online robot control experiment based on the visual gesture recognition algorithm. Through extensive experiments, the effectiveness and efficiency of our method has been confirmed.

scholarly journals Social Touch Gesture Recognition Using Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saad Albawi ◽  
Oguz Bayat ◽  
Saad Al-Azawi ◽  
Osman N. Ucan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Sensor Data ◽  
Social Touch ◽  
Minimum Number ◽  
Original Frame

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.

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