Controlling and Assisting Manual Assembly Processes by Automated Progress and Gesture Recognition

Hogreve, Sebastian; Kaczmarek, Simon; Adam, Jannes; Franz, L.; Döllen, Tim; Paulus, Henning; Reinkemeyer, Vincent; Tracht, Kirsten

doi:10.4028/www.scientific.net/amm.840.50

Cited by 6 publications

(3 citation statements)

References 7 publications

(9 reference statements)

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“…From the above, machine vision technology is used to capture the real-time position coordinates of the worker’s hand in the smart assembly workbench. By comparing the position coordinates of the worker’s hand with the position coordinates of the accessory box, it can be judged whether the needed part is taken from the corresponding accessory box (Hogreve et al , 2016).…”

Section: Application Of the Key Technology In The Smart Assembly Workbenchmentioning

confidence: 99%

Human-computer interaction based on machine vision of a smart assembly workbench

Wang

Shen

et al. 2020

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Purpose The purpose of this study is to achieve multi-variety and small-batch assembly through direct cooperation between equipment and people and to improve assembly efficiency as well as flexibility. Design/methodology/approach Firstly, the concept of the human–computer interaction is designed. Secondly, the machine vision technology is studied theoretically. Skin color filter based on hue, saturation and value color model is put forward to screen out images that meet the skin color characteristics of the worker, and a multi-Gaussian weighted model is built to separate moving objects from its background. Both of them are combined to obtain the final images of the target objects. Then, the key technology is applied to the smart assembly workbench. Finally, experiments are conducted to evaluate the role of the human–computer interaction features in improving productivity for the smart assembly workbench. Findings The result shows that multi-variety and small-batch considerable increases assembly time and the developed human–computer interaction features, including prompting and introduction, effectively decrease assembly time. Originality/value This study proves that the machine vision technology studied in this paper can effectively eliminate the interferences of the environment to obtain the target image. By adopting the human–computer interaction features, including prompting and introduction, the efficiency of manual operation is improved greatly, especially for multi-variety and small-batch assembly.

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Section: Application Of the Key Technology In The Smart Assembly Workbenchmentioning

confidence: 99%

Human-computer interaction based on machine vision of a smart assembly workbench

Wang

Shen

et al. 2020

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“…2. After the pretreatment, the following is obtained according to the calculation formula (Hogreve et al, 2016):…”

Section: Gesture Feature Extractionmentioning

confidence: 99%

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

Zhang¹

2020

Ergonomics in Design: The Quarterly of Human Factors Applicatio

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With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

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“…As an active way of human-computer interaction, gesture recognition has been developed from the era of data gloves with assistive devices to the stage of machine vision recognition [1]. Machine vision recognition is divided into three stages: gesture segmentation, gesture feature extraction and gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Cai

2017

J. Eng. Technol. Sci.

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Abstract.A static gesture recognition algorithm is proposed based on a recursive graph of the upper triangular image texture, motivated by the low accuracy and robustness of existing algorithms. Firstly, the fingertip localization method based on contour curvature is used to obtain the palm region and then the gesture contour model is established. Secondly, a recurrence plot of the gesture contour sequence is built, which is constructed using the central point and the starting point coordinates. Finally, the texture recognition algorithm is applied to calculate the normalized distance between the recurrence plots of the gesture. The experimental results show that the proposed algorithm can achieve higher recognition accuracy under varying complex backgrounds and illumination. At the same time, when the gesture is in rotation, translation, or scaling, the algorithm has high robustness with a small amount of computation and high efficiency.

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Controlling and Assisting Manual Assembly Processes by Automated Progress and Gesture Recognition

Cited by 6 publications

References 7 publications

Human-computer interaction based on machine vision of a smart assembly workbench

Human-computer interaction based on machine vision of a smart assembly workbench

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

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