Edge impulse-based convolutional neural network for Hand Posture Recognition

Research Article
Open access

Edge impulse-based convolutional neural network for Hand Posture Recognition

Yiwei Gui 1*
  • 1 University of Electronic Science and Technology of China    
  • *corresponding author 20204321371@sr.gxmu.edu.cn
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/40/20230636
ACE Vol.40
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-305-0
ISBN (Online): 978-1-83558-306-7

Abstract

Hand Posture Recognition (HPR) plays a crucial role in enabling effective human-computer interaction, particularly for individuals with hearing disabilities. The study compares five models, including MobileNetV2 96x96 0.35, MobileNetV1 96x96 0.25, MobileNetV1 96x96 0.1, self-designed Network 1, and self-designed Network 2, based on the Sébastien Marcel Static Hand Posture Database. Evaluation metrics - infserencing time, peak RAM usage, flash usage, and accuracy - are used to analyze the performance. The experiment workflow for each model comprises five major steps. Firstly, a random selection of 120 images from the Sébastien Marcel Static Hand Posture Database is converted to JPG format. Then, the images are divided into 80% training data and 20% testing data. Subsequently, the original images are normalized, and features are extracted for further processing. Subsequently, the models are individually trained using the preprocessed data, optimizing their parameters. Finally, the trained models are evaluated using the testing data set to assess their performance in hand posture recognition. The results indicate that MobileNetV2 96x96 0.35 achieves the highest accuracy of 96.69% while consuming fewer hardware resources compared to other models. MobileNetV1 96x96 0.1 demonstrates the lowest inferencing time and peak RAM usage, making it suitable for real-time applications. Furthermore, self-designed Model 1 exhibits the lowest flash usage, making it a viable option for resource-constrained devices. This study provides valuable insights into the selection of CNN architectures for HPR, offering guidance for practitioners to choose models based on specific application requirements.

Keywords:

Computer Vision, Machine Learning, Image Classification, Hand Posture Recognition

Gui,Y. (2024). Edge impulse-based convolutional neural network for Hand Posture Recognition. Applied and Computational Engineering,40,115-119.
Export citation

References

[1]. World Health Organization 2023 Deafness and hearing loss https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss.

[2]. Zhang Z 2018 Research on vision-based real-time dynamic gesture segmentation method (in Chinese) master thesis Henan University.

[3]. Wan K 2013 Research and Application of Gesture Recognition System (in Chinese) master thesis Guangdong University of Technology.

[4]. Qi B 2011 Research on gesture recognition algorithm based on dynamic fuzzy neural network (in Chinese) master thesis Southwest University.

[5]. Xu X 2018 Research on the method of dynamic gesture recognition based on 3D deep neural network (in Chinese) master thesis Xidian University.

[6]. Malassiotis S and Strintzis M G 2008 Real-time hand posture recognition using range data Image and Vision Computing p 1027-1037.

[7]. Marcel S 1999 Hand posture recognition in a body-face centered space CHI’99 Extended Abstracts on Human Factors in Computing Systems p 302-303.

[8]. Howard A G et al 2017 Mobilenets: Efficient convolutional neural networks for mobile vision applications arXiv preprint arXiv:1704.04861.

[9]. Sandler M et al 2018 Mobilenetv2: Inverted residuals and linear bottlenecks In Proceedings of the IEEE conference on computer vision and pattern recognition pp 4510-4520.

[10]. Srinivasu P N et al 2021 Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM Sensors 21(8) p 2852.

[11]. Qiu Y Wang J Jin Z et al 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72: 103323.

[12]. Nair R R Singh T Basavapattana A Pawar M M 2022 Multi-layer, multi-modal medical image intelligent fusion Multimedia Tools and Applications 81(29) 42821-42847.

Cite this article

Gui,Y. (2024). Edge impulse-based convolutional neural network for Hand Posture Recognition. Applied and Computational Engineering,40,115-119.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-305-0(Print) / 978-1-83558-306-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.40
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. World Health Organization 2023 Deafness and hearing loss https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss.

[2]. Zhang Z 2018 Research on vision-based real-time dynamic gesture segmentation method (in Chinese) master thesis Henan University.

[3]. Wan K 2013 Research and Application of Gesture Recognition System (in Chinese) master thesis Guangdong University of Technology.

[4]. Qi B 2011 Research on gesture recognition algorithm based on dynamic fuzzy neural network (in Chinese) master thesis Southwest University.

[5]. Xu X 2018 Research on the method of dynamic gesture recognition based on 3D deep neural network (in Chinese) master thesis Xidian University.

[6]. Malassiotis S and Strintzis M G 2008 Real-time hand posture recognition using range data Image and Vision Computing p 1027-1037.

[7]. Marcel S 1999 Hand posture recognition in a body-face centered space CHI’99 Extended Abstracts on Human Factors in Computing Systems p 302-303.

[8]. Howard A G et al 2017 Mobilenets: Efficient convolutional neural networks for mobile vision applications arXiv preprint arXiv:1704.04861.

[9]. Sandler M et al 2018 Mobilenetv2: Inverted residuals and linear bottlenecks In Proceedings of the IEEE conference on computer vision and pattern recognition pp 4510-4520.

[10]. Srinivasu P N et al 2021 Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM Sensors 21(8) p 2852.

[11]. Qiu Y Wang J Jin Z et al 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72: 103323.

[12]. Nair R R Singh T Basavapattana A Pawar M M 2022 Multi-layer, multi-modal medical image intelligent fusion Multimedia Tools and Applications 81(29) 42821-42847.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.