Enhancing Health Care Systems with Deep Learning-Based Hand Gesture Recognition

Karthikraja D.¹ , Purushothaman N.²

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.3 , pp.47-55, Jun-2024

Online published on Jun 30, 2024

Copyright © Karthikraja D., Purushothaman N. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Recent years have seen the integration of advanced technologies in healthcare, leading to innovative solutions for improving patient care and streamlining medical processes. One such innovation is the implementation of hand gesture recognition systems using deep learning. This paper explores the development and application of a hand gesture recognition system specifically designed for healthcare environments. Leveraging deep learning algorithms, the system accurately interprets hand gestures, enabling touchless interaction with medical devices and enhancing healthcare efficiency. Convolutional neural networks (CNNs) process and classify hand gestures captured through real-time video feeds. A comprehensive dataset of medical gestures was used to train the neural network, ensuring high precision and reliability. The system includes a robust preprocessing pipeline to handle variations in lighting, background, and hand orientation, improving performance in diverse clinical settings. Evaluation demonstrated significant accuracy in recognizing a wide range of gestures, outperforming traditional machine learning approaches. This touchless method reduces contamination risk in sterile environments and enhances accessibility for healthcare professionals, especially in critical situations. The successful integration of deep learning-based hand gesture recognition into healthcare systems represents a significant advancement in health technology, promising improved patient outcomes and streamlined processes.

Key-Words / Index Term :
Deep Learning, Hand Gesture Recognition, Healthcare Systems, Convolutional Neural Networks (CNNs), Human-Computer Interaction, Clinical Environment Robustness

References :
[1] S. Mitra and T. Acharya, "Gesture Recognition: A Survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol.37, Issue.3, pp.311-324, 2007.
[2] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in Proc. of the International Conference on Learning Representations (ICLR), pp.112-119, 2015.
[3] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, Vol.15, Issue.5, pp.1110-1120, 2013.
[4] M. R. Amer, S. Todorovic, and S. V. N. Vishwanathan, "A Chains Model for Localizing Participants of Group Activities in Videos," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.38, Issue.8, pp.1611-1624, 2016.
[5] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, Vol.15, Issue.5, pp.1110-1120, 2013.
[6] G. D. Clifford and D. Clifton, "Wireless Technology in Disease Management and Medicine," Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1878-188, 2012.
[7] Ahmed, M. R. Amer, and M. A. Ghonemy, "Deep Convolutional Neural Networks for Gesture Recognition Using Kinect," IEEE Transactions on Computational Imaging, Vol.3, Issue.3, pp.345-358, 2017.
[8] J. Han, G. Awad, and A. Sutherland, "Adaptive Background Modeling for Real-Time Hand Gesture Recognition," IEEE Transactions on Consumer Electronics, Vol.56, Issue.3, pp.963-970, 2010.
[9] S. Ghosh, R. Sarkar, and S. Sural, "Hand Gesture Recognition for Post-Stroke Rehabilitation Using Leap Motion," IEEE Journal of Biomedical and Health Informatics, Vol.21, Issue.4, pp.888-896, 2017.
[10] K. Wachs, H. Kölsch, M. Stern, and R. Edlinger, "Touchless Interaction with Medical Devices Using Hand Gesture Recognition," IEEE Transactions on Biomedical Engineering, Vol.61, Issue.5, pp.1544-1555, 2014.
[11] Y. Chen, Z. Yang, and G. Cheng, "Hand Gesture Recognition Using a Convolutional Neural Network Ensemble," IEEE Access, Vol.7, Issue.3, pp.28714-28723, 2019.
[12] J. Han, G. Awad, and A. Sutherland, "Adaptive Background Modeling for Real-Time Hand Gesture Recognition," IEEE Transactions on Consumer Electronics, Vol.56, Issue.3, pp.963-970, 2010.
[13] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, Vol.15, Issue.5, pp.1110-1120, 2013.
[14] K. Wachs, H. Kölsch, M. Stern, and R. Edlinger, "Touchless Interaction with Medical Devices Using Hand Gesture Recognition," IEEE Transactions on Biomedical Engineering, Vol.61, Issue.5, pp.1544-1555, 2014.
[15] J. Han, G. Awad, and A. Sutherland, "Adaptive Background Modeling for Real-Time Hand Gesture Recognition," IEEE Transactions on Consumer Electronics, Vol.56, Issue.3, pp.963-970, 2010.
[16] Ahmed, M. R. Amer, and M. A. Ghonemy, "Deep Convolutional Neural Networks for Gesture Recognition Using Kinect," IEEE Transactions on Computational Imaging, Vol.3, Issue.3, pp.345-358, 2017.
[17] Y. Chen, Z. Yang, and G. Cheng, "Hand Gesture Recognition Using a Convolutional Neural Network Ensemble," IEEE Access, Vol.7, Issue.5, pp.28714-28723, 2019.
[18] S.P. Akinrinwa, O. Olabode, O.C. Agbonifo, K.G. Akintola, "Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.9-21, 2022.
[19] Christopher Ebuka Ojukwu, "Melanoma Skin Cancer Detection Using Support Vector Machines and Convolutional Neural Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.6, pp.9-12, 2021.
[20] Harshita Kukreja, Soumya Jain, Shefali Arora, M.P.S. Bhatia, "Privacy Enhancement in Biometric Systems by Template Protection using Deep Learning", International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.14-24, 2021.