Hand Gestures Recognition Model for Augmented Reality Robotic Applications

Murhij, Youshaa; Serebrenny, Vladimir

doi:10.1007/978-981-15-5580-0_15

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Subsequently, the motion-based model [25] was proposed, which uses the motion of the hand as a feature via hand trajectory tracking. Finally, the deep-based model [26][27][28][29][30][31][32][33][34][35] has been proposed, which uses a novel deep-learning-based architecture to learn spatial hierarchies of features automatically and adaptively for 2D images, such as the convolutional neural network model (CNN), the convolutional neural network-long short-term memory model (CNN-LSTM), and recurrent neural networks (RNN). However, the 2D approach has less information than the 3D approach, which makes it hard to solve the problem of similar gestures based on a backhand view.…”

Section: Related Workmentioning

confidence: 99%

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Chophuk

Chamnongthai

Chinnasarn

2022

Sensors

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Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial–temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial–temporal finger joint angle patterns. The third model consists of the spatial–temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial–temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.

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Section: Related Workmentioning

confidence: 99%

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Chophuk

Chamnongthai

Chinnasarn

2022

Sensors

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“…Ref. [ 2 ] provides a comprehensive overview of augmented reality applications in smart facility management, offering valuable information and analysis for this evolving field.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Real-Time Gestural Recognition and Augmented Reality for Industrial Applications

Torres,

Santos,

Melo

et al. 2024

Sensors

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In recent decades, technological advancements have transformed the industry, highlighting the efficiency of automation and safety. The integration of augmented reality (AR) and gesture recognition has emerged as an innovative approach to create interactive environments for industrial equipment. Gesture recognition enhances AR applications by allowing intuitive interactions. This study presents a web-based architecture for the integration of AR and gesture recognition, designed to interact with industrial equipment. Emphasizing hardware-agnostic compatibility, the proposed structure offers an intuitive interaction with equipment control systems through natural gestures. Experimental validation, conducted using Google Glass, demonstrated the practical viability and potential of this approach in industrial operations. The development focused on optimizing the system’s software and implementing techniques such as normalization, clamping, conversion, and filtering to achieve accurate and reliable gesture recognition under different usage conditions. The proposed approach promotes safer and more efficient industrial operations, contributing to research in AR and gesture recognition. Future work will include improving the gesture recognition accuracy, exploring alternative gestures, and expanding the platform integration to improve the user experience.

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