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Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges Cover

Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges

Cybernetics and Information Technologies
Volume 24 (2024): Issue 3 (September 2024)
By: Aws Saood Mohamed,  Nidaa Flaih Hassan and  Abeer Salim Jamil  
Open Access
|Sep 2024

References

  1. Bavelas, J. B. The Social Life of Hand Gestures. – In: Face-to-Face Dialogue: Theory, Research, and Applications. Oxford Academic, 2022.
    Search in Google ScholarBack to article
  2. Athavale, S., M. Deshmukh. Dynamic Hand Gesture Recognition for Human-Computer Interaction. A Comparative Study. – International Journal of Engineering Research and General Science, Vol. 2, 2014, No 2, pp. 38-55.
    Search in Google ScholarBack to article
  3. Baumgartl, H., D. Sauter, C. Schenk, C. Atik, R. Buettner. Vision-Based Hand Gesture Recognition for Human-Computer Interaction Using Mobile Net V2. – In: Proc. of 45th IEEE Annual Computers, Software, and Applications Conference (COMPSAC’21), IEEE, 2021, pp. 1667-1674.
    Search in Google ScholarBack to article
  4. Griffiths, D., J. Boehm. A Review on Deep Learning Techniques for 3D Sensed Data Classification. – Remote Sensing, Vol. 11, 2019, No 12, 1499.
    Search in Google ScholarBack to article
  5. Dhande, A., S. Mantri, H. Pande. Comparative Analysis of Human Hand Gesture Recognition in Real-Time Healthcare Applications. – In: Proc. of International Conference on Expert Clouds and Applications, Springer, Singapore, 2022, pp. 461-475.
    Search in Google ScholarBack to article
  6. Ameur, S., A. B. Khalifa, M. S. Bouhlel. A Novel Hybrid Bidirectional Unidirectional LSTM Network for Dynamic Hand Gesture Recognition with Leap Motion. – Entertainment Computing, Vol. 35, 2020, 100373.
    Search in Google ScholarBack to article
  7. Han, D., B. Mulyana, V. Stankovic, S. Cheng. A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation. – Sensors, Vol. 23, 2023, No 7, 3762.
    Search in Google ScholarBack to article
  8. Wu, J., P. Ren, B. Song, R. Zhang, C. Zhao, X. Zhang. Data Glove-Based Gesture Recognition Using CNN-BiLSTM Model with Attention Mechanism. – Plos One, Vol. 18, 2023, No 11, e0294174.
    Search in Google ScholarBack to article
  9. Jiang, S., B. Lv, W. Guo, C. Zhang, H. Wang, X. Sheng, P. B. Shull. Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing. – IEEE Transactions on Industrial Informatics, Vol. 14, 2017, No 8, pp. 3376-3385.
    Search in Google ScholarBack to article
  10. Chen, X., Y. Li, R. Hu, X. Zhang, X. Chen. Hand Gesture Recognition Based on Surface Electromyography Using Convolutional Neural Network with Transfer Learning Method. – IEEE Journal of Biomedical and Health Informatics, Vol. 25, 2020, No 4, pp. 1292-1304.
    Search in Google ScholarBack to article
  11. Jaramillo-Yánez, A., M. E. Benalcázar, E. Mena-Maldonado. Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review. – Sensors, Vol. 20, 2020, No 9, 2467.
    Search in Google ScholarBack to article
  12. Choi, J. W., S. J. Ryu, J. H. Kim. Short-Range Radar Based Real-Time Hand Gesture Recognition Using LSTM Encoder. – IEEE Access, Vol. 7, 2019, pp. 33610-33618.
    Search in Google ScholarBack to article
  13. Dang, L. M., K. Min, H. Wang, M. J. Piran, C. H. Lee, H. Moon. Sensor-Based and Vision-Based Human Activity Recognition: A Comprehensive Survey. – Pattern Recognition, Vol. 108, 2020, 107561.
    Search in Google ScholarBack to article
  14. De, O. P., S. Mukherjee, S. Nandy, T. Chakraborty, S. Saha. Computer Vision Based Framework for Digit Recognition by Hand Gesture Analysis. – In: Proc. of 7th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON’16), IEEE, 2016, pp. 1-5.
    Search in Google ScholarBack to article
  15. Gupta, A., S. Kumar. Review for Optimal Human-Gesture Design Methodology and Motion Representation of Medical Images Using Segmentation from Depth Data and Gesture Recognition. – Current Medical Imaging, Vol. 20, 2024.
    Search in Google ScholarBack to article
  16. Leon, D. G., J. Gröli, S. R. Yeduri, D. Rossier, R. Mosqueron, O. J. Pandey, L. R. Cenkeramaddi. Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN. – IEEE Sensors Journal, Vol. 22, 2022, No 14, pp. 14610-14619.
    Search in Google ScholarBack to article
  17. Dosovitskiy, A., et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. – arXiv preprint arXiv:2010.11929, 2020.
    Search in Google ScholarBack to article
  18. Ewe, E. L., C. P. Lee, L. C. Kwek, K. M. Lim. Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier. – Applied Sciences, Vol. 12, 2022, No 15, 7643.
    Search in Google ScholarBack to article
  19. Mukthineni, V., R. Mukthineni, O. Sharma, S. Jamjala Narayanan. Face Authenticated Hand Gesture Based Human Computer Interaction for Desktops. – Cybernetics and Information Technologies, Vol. 20, 2020, No 4, pp. 74-89.
    Search in Google ScholarBack to article
  20. Nguyen, T. N., H. H. Huynh, J. Meunier. Static Hand Gesture Recognition Using Artificial Neural Network. – Journal of Image and Graphics, Vol. 1, 2013, No 1, pp. 34-38.
    Search in Google ScholarBack to article
  21. Köpüklü, O., A. Gunduz, N. Kose, G. Rigoll. Real-Time Hand Gesture Detection and Classification Using Convolutional Neural Networks. – In: Proc. of 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG’2019), IEEE, 2019, pp. 1-8.
    Search in Google ScholarBack to article
  22. Lei, Q., H. Zhang, Z. Xia, Y. Yang, Y. He, S. Liu. Applications of Hand Gestures Recognition in Industrial Robots: A Review. – In: Proc. of 11th International Conference on Machine Vision (ICMV’2018), SPIE, Vol. 11041, 2019, pp. 455-465.
    Search in Google ScholarBack to article
  23. Siriak, R., I. Skarga-Bandurova, Y. Boltov. Deep Convolutional Network with Long Short-Term Memory Layers for Dynamic Gesture Recognition. – In: Proc. of 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’19), IEEE, 2019, pp. 158-162.
    Search in Google ScholarBack to article
  24. Alonso, D. G., A. Teyseyre, L. Berdun, S. Schiaffino. A Deep Learning Approach for Hybrid Hand Gesture Recognition. – In: Proc. of 18th Mexican International Conference on Artificial Intelligence (MICAI’2019), Advances in Soft Computing, Xalapa, Mexico, 27 October – 2 November 2019, Proceedings 18, Springer International Publishing, 2019, pp. 87-99.
    Search in Google ScholarBack to article
  25. Abdulrazzaq, H. I., N. F. Hassan. Modified Siamese Convolutional Neural Network for Fusion Multimodal Biometrics at Feature Level. – In: Proc. of 2nd Scientific Conference of Computer Sciences (SCCS’19), IEEE, 2019, pp. 12-17.
    Search in Google ScholarBack to article
  26. Yasen, M., S. Jusoh. A Systematic Review on Hand Gesture Recognition Techniques, Challenges and Applications. – PeerJ Computer Science, Vol. 5, 2019, e218.
    Search in Google ScholarBack to article
  27. Bhuvanya, R., M. Kavitha. Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning Approach. – Cybernetics and Information Technologies, Vol. 23, 2023, No 2, pp. 3-19.
    Search in Google ScholarBack to article
  28. Moin, A., A. Zhou, A. Rahimi, A. Menon, S. Benatti, G. Alexandrov, J. M. Rabaey. A Wearable Biosensing System with In-Sensor Adaptive Machine Learning for Hand Gesture Recognition. – Nature Electronics, Vol. 4, 2021, No 1, pp. 54-63.
    Search in Google ScholarBack to article
  29. Si, G., Z. Gu, H. Zheng. Duet of ViT and CNN: Multi-Scale Dual-Branch Network for Fine-Grained Image Classification of Marine Organisms. – Intelligent Marine Technology and Systems, Vol. 2, 2024, No 1.
    Search in Google ScholarBack to article
  30. Haq, M. A., M. Ridlwan, I. Naila. Leveraging Self-Attention Mechanism for Deep Learning in Hand-Gesture Recognition System. – In: E3S Web of Conferences. Vol. 500. 2024.
    Search in Google ScholarBack to article
  31. Kumaran, N., M. S. Anurag, M. Sampath. Hand Gesture Recognition Using Transfer Learning Techniques. – Journal of Current Research in Engineering and Science (JCRES), Vol. 4, 2021, No 1.
    Search in Google ScholarBack to article
  32. Rong, Y., G. Gu. Deep Transfer Learning-Based Adaptive Gesture Recognition of a Soft e-Skin Patch with Reduced Training Data and Time. – Sensors and Actuators. A: Physical, Vol. 363, 2023, 114693.
    Search in Google ScholarBack to article
  33. Ageishi, N., F. Tomohide, A. B. Abdallah. Real-Time Hand-Gesture Recognition Based on Deep Neural Network. – In: SHS Web of Conferences. EDP Sciences, 2021, 04009.
    Search in Google ScholarBack to article
  34. Obaida, T. H., A. S. Jamil, N. F. Hassan. Real-Time Face Detection in Digital Video-Based on Viola-Jones Supported by Convolutional Neural Networks. – International Journal of Electrical and Computer Engineering (IJECE), Vol. 12, 2022, No 3, pp. 3083-3091.
    Search in Google ScholarBack to article
  35. Molchanov, P., X. Yang, S. Gupta, K. Kim, S. Tyree, J. Kautz. Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Network. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4207-4215.
    Search in Google ScholarBack to article
  36. Huang, J., W. Zhou, H. Li, W. Li. Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition. – IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, 2018, No 9, pp. 2822-2832.
    Search in Google ScholarBack to article
  37. Zhang, Y., L. Shi, Y. Wu, K. Cheng, J. Cheng, H. Lu. Gesture Recognition Based on Deep Deformable 3D Convolutional Neural Networks. – Pattern Recognition, Vol. 107, 2020, 107416.
    Search in Google ScholarBack to article
  38. Long, D. T. Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition. – Cybernetics and Information Technologies, Vol. 24, 2024, No 1, pp. 171-189.
    Search in Google ScholarBack to article
  39. Sutskever, I., O. Vinyals, Q. V. Le. Sequence to Sequence Learning with Neural Networks. – Advances in Neural Information Processing Systems, Vol. 27, 2014.
    Search in Google ScholarBack to article
  40. Ordóñez, F. J., D. Roggen. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. – Sensors, Vol. 16, 2016, No 1, 115.
    Search in Google ScholarBack to article
  41. Mittal, A., P. Kumar, P. P. Roy, R. Balasubramanian, B. B. Chaudhuri. A Modified LSTM Model for Continuous Sign Language Recognition Using Leap Motion. – IEEE Sensors Journal, Vol. 19, 2019, No 16, pp. 7056-7063.
    Search in Google ScholarBack to article
  42. Tan, C. K., K. M. Lim, R. K. Chang, C. P. Lee, A. Alqahtani. HGR-ViT: Hand Gesture Recognition with Vision Transformer. – Sensors, Vol. 23, 2023, No 12, 5555.
    Search in Google ScholarBack to article
  43. Xie, Z., Y. Lin, Z. Yao, Z. Zhang, Q. Dai, Y. Cao, H. Hu. Self-Supervised Learning with Swin Transformers. – arXiv e-prints, arXiv:2105.04553, 2021.
    Search in Google ScholarBack to article
  44. Tavakoli, M., C. Benussi, P. A. Lopes, L. B. Osorio, A. T. de Almeida. Robust Hand Gesture Recognition with a Double Channel Surface EMG Wearable Armband and SVM Classifier. – Biomedical Signal Processing and Control, Vol. 46, 2018, pp. 121-130.
    Search in Google ScholarBack to article
  45. Verma, B., A. Choudhary. Unsupervised Learning Based Static Hand Gesture Recognition from RGB-D Sensor. – In: Proc. of 8th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Springer International Publishing, 2018, pp. 304-314.
    Search in Google ScholarBack to article
  46. Zhang, Y., Y. Chen, H. Yu, X. Yang, R. Sun, B. Zeng. A Feature Adaptive Learning Method for High-Density Semg-Based Gesture Recognition. – Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 5, 2021, No 1, pp. 1-26.
    Search in Google ScholarBack to article
  47. Sarma, D., M. K. Bhuyan. Methods, Databases and Recent Advancement of Vision-Based Hand Gesture Recognition for Hci Systems: A Review. – SN Computer Science, Vol. 2, 2021, No 6, 436.
    Search in Google ScholarBack to article
  48. Jiang, S., P. Kang, X. Song, B. P. Lo, P. B. Shull. Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey. – IEEE Reviews in Biomedical Engineering, Vol. 15, 2021, pp. 85-102.
    Search in Google ScholarBack to article
  49. Pyun, K. R., et al. Machine-Learned Wearable Sensors for Real-Time Hand-Motion Recognition: toward Practical Applications. – National Science Review, Vol. 11, 2024, No 2, nwad298.
    Search in Google ScholarBack to article
  50. Kaur, H., J. Rani. A Review: Study of Various Techniques of Hand Gesture Recognition. – In: Proc. of 1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES’16), IEEE, 2016, pp. 1-5.
    Search in Google ScholarBack to article
  51. Wang, F., R. Hu, Y. Jin. Research on Gesture Image Recognition Method Based on Transfer Learning. – Procedia Computer Science, Vol. 187, 2021, pp. 140-145.
    Search in Google ScholarBack to article
  52. Saiful Islam, M., W. Rahman, A. Abdelkader, P. T. Yang, S. Lee, J. L. Adams, E. Hoque. Using AI to Measure Parkinson’s Disease Severity at Home. – arXiv e-prints, arXiv-2303, 2023.
    Search in Google ScholarBack to article
  53. Sikkandar, M. Y. Design a Contactless Authentication System Using Hand Gestures Technique in COVID-19 Panic Situation. – Annals of the Romanian Society for Cell Biology, 2021, pp. 2149-2159.
    Search in Google ScholarBack to article
  54. Rautaray, S. S., A. Agrawal. Vision Based Hand Gesture Recognition for Human Computer Interaction: A Survey. – Artificial Intelligence Review, Vol. 43, 2015, pp. 1-54.
    Search in Google ScholarBack to article
  55. Satybaldina, D., G. Kalymova. Deep Learning Based Static Hand Gesture Recognition. – Indonesian Journal of Electrical Engineering and Computer Science, Vol. 21, 2021, No 1, pp. 398-405.
    Search in Google ScholarBack to article
  56. Zhu, G., L. Zhang, P. Shen, J. Song. Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM. – IEEE Access, Vol. 5, 2017, pp. 4517-4524.
    Search in Google ScholarBack to article
  57. Al-Dori, A. S. M., J. M. Kadhim. Touchscreen-Based Smartphone Continuous Authentication System (SCAS) Using Deep Neural Network. – Turkish Journal of Computer and Mathematics Education, Vol. 12, 2021, No 11, pp. 2382-2391.
    Search in Google ScholarBack to article
  58. Ketan Chakraborty, B., D. Sarma, M. K. Bhuyan, K. F. MacDorman. Review of Constraints on Vision-Based Gesture Recognition for Human-Computer Interaction. – IET Computer Vision (Wiley-Blackwell), Vol. 12, 2018, No 1.
    Search in Google ScholarBack to article
  59. Lai, B. W., C. C. Li, E. Jeng. Dual-Handed Dynamic Gesture Recognition Using Inertial Sensors. – In: Proc. of 3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB’23), IEEE, 2023, pp. 223-225.
    Search in Google ScholarBack to article
  60. Rastgoo, R., K. Kiani, S. Escalera. Hand Sign Language Recognition Using Multi-View Hand Skeleton. – Expert Systems with Applications, Vol. 150, 2020, 113336.
    Search in Google ScholarBack to article
  61. Bambach, S., S. Lee, D. J. Crandall, C. Yu. Lending a Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions. – In: Proc. of IEEE International Conference on Computer Vision, IEEE, 2015, pp. 1949-1957.
    Search in Google ScholarBack to article
  62. Zeng, W., C. Wang, Q. Wang. Hand Gesture Recognition Using Leap Motion via Deterministic Learning. – Multimedia Tools and Applications, Vol. 77, 2018, pp. 28185-206.
    Search in Google ScholarBack to article
  63. Kumaran, N., M. S. Anurag, M. Sampath. Hand Gesture Recognition Using Transfer Learning Techniques. – Journal of Current Research in Engineering and Science (JCRES), Vol. 4, 2021, No 1.
    Search in Google ScholarBack to article
  64. Obaida, T. H., N. F. Hassan, A. S. Jamil. Comparative of Viola-Jones and YOLO V3 for Face Detection in Real Time. – Iraqi Journal of Computers, Communications, Control & Systems Engineering, Vol. 22, 2022, No 2, pp. 63-72.
    Search in Google ScholarBack to article
  65. Zhang, Y., C. Cao, J. Cheng, H. Lu. EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition. – IEEE Transactions on Multimedia, Vol. 20, 2018, No 5, pp. 1038-1050.
    Search in Google ScholarBack to article
  66. Miah, A. S. M., M. A. M. Hasan, Y. Tomioka, J. Shin. Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network. – IEEE Open Journal of the Computer Society, Vol. 5, 2024, pp. 144-155.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/cait-2024-0031 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 163 - 181
Submitted on: Jul 16, 2024
Accepted on: Aug 28, 2024
Published on: Sep 19, 2024
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Computer vision,
Hand gesture recognition,
Real-time systems,
Deep learning,
Transformers
Related subjects:
Computer sciences,
Information technology

© 2024 Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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