Design and evaluation of a hand gesture recognition approach for real-time interactions

Shanthakumar, Vaidyanath Areyur; Peng, Chao; Hansberger, Jeffrey T.; Cao, Lizhou; Meacham, Sarah C.; Blakely, Victoria R.

doi:10.1007/s11042-019-08520-1

Cited by 50 publications

(13 citation statements)

References 38 publications

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“…Gesture-based interfaces are a natural and intuitive approach to HCI based on the recognition of hand gestures [27]. Many applications and devices are using gesturebased technology to replace traditional input devices, such as a keyboard and mouse, in various fields, such as entertainment, healthcare, training, and education [28] - [32].…”

Section: Related Workmentioning

confidence: 99%

A Gesture-Based Educational System that Integrates Simulation and Molecular Visualization to Teach Chemistry

Aldosari

Ghita

Marocco

2022

Int. J. Emerg. Technol. Learn.

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Technology has proven effective in enhancing teaching methods. Gesture-based applications have a high potential for use in the development of teaching methods in schools. Simulation and 3D visualization, in particular, are some of the most important technologies that positively affected education. The implementation of these technologies in the education sector can determine students’ potential and enhance their interest in learning in scientific fields. This study explored the use of a chemistry education system that integrates virtual simulation and molecular visualization and uses a Leap Motion controller to teach chemistry in secondary schools. The sample comprised 113 students enrolled in four different classes in a public secondary school in Saudi Arabia. The data were analyzed using an ANOVA test to compare the results. The results indicate that the students who used the proposed educational system achieved better learning results than the students who participated in theoretical classroom learning only. Moreover, students who used the proposed educational system learned microscopic-level concepts better than those who used a real chemistry laboratory. Finally, combining simulation and molecular visualization and using gesture-based technology has a significant and positive effect on the students’ learning experience, as demonstrated by the results of this study which contributes to the development of education through the utilization of modern technology.

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Section: Related Workmentioning

confidence: 99%

A Gesture-Based Educational System that Integrates Simulation and Molecular Visualization to Teach Chemistry

Aldosari

Ghita

Marocco

2022

Int. J. Emerg. Technol. Learn.

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“…The frame length was set to be equal for all the acquisitions for which the hand gestures were at the centre of the video [9], [33]. Following the structure of the 20BN Jester dataset, the normalisation was applied to impose a fixed length, set to be 32 frames.…”

Section: Pre-processingmentioning

confidence: 99%

A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition

Gionfrida¹,

Rusli²,

Kedgley³

et al. 2022

Preprint

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This paper introduces a multi-class hand gesture recognition model developed to identify a set of defined hand gesture sequences in two-dimensional RGB video recordings. The work presents an action detection classifier that looks at both appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model uses an available dataset to then adopt a technique known as transfer learning to fine-tune the model on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (± 0.37) with a mean Jaccard index of 0.812 (± 0.105) for 22 participants. The presented model illustrates the possibility of training a model with a small set of data (113,410 fully labelled frames). The proposed pipeline embraces a small-sized architecture that could facilitate its adoption.

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“…The air gesture is detected and recognized that can be further used for touchless interaction [14]. The major objective of the work was to develop improvised accuracy and execution time for real-time applications.…”

Section: Survey On Hand Gesture Segmentationmentioning

confidence: 99%

A Study on Hand Gesture Segmentation Approaches

Sunuwar

Borah

Agarwal

et al. 2021

Advances in Intelligent Systems and Computing

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Computer vision is the study of making machines capable of understanding the input image and video using some algorithms or techniques. As for humans, understanding a scene given in an image or video is not a difficult task. Humans are blessed to understand images and videos effortlessly without knowing the complexities behind the process of image and video understanding. Making machines also capable of understanding their input image and video is not that simple. With ongoing research for making human-computer interaction more natural and realistic, lots of advancements have been done in different components of computer vision. The paper focuses on the image segmentation process used in computer vision and image processing which is a very crucial component while processing images and video. Image segmentation is the process of extracting only relevant content or information while removing irreverent content or information. Image segmentation is the most researched topic in image processing and computer vision. It is one of the major activities that will help the later process to ease its operation by filtering unnecessary and irrelevant contents. The paper aims to describe the segmentation process and highlight different types of segmentation approaches used in recent trends comparing their performances.

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Design and evaluation of a hand gesture recognition approach for real-time interactions

Cited by 50 publications

References 38 publications

A Gesture-Based Educational System that Integrates Simulation and Molecular Visualization to Teach Chemistry

A Gesture-Based Educational System that Integrates Simulation and Molecular Visualization to Teach Chemistry

A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition

A Study on Hand Gesture Segmentation Approaches

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