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Open Access
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Abhirami J.S, Dharunya V, Subhadharuna R, Tirukkala Gnana Sindhuja,
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- Assistant Professor,, UG Student,, UG Student,, UG Student, Nehru Institute of Engineering and Technology, Anna University,, Nehru Institute of Engineering and Technology, Anna University,, Nehru Institute of Engineering and Technology, Anna University,, Nehru Institute of Engineering and Technology, Anna University, Coimbatore,, Coimbatore,, Coimbatore,, Coimbatore, India, India, India, India
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Abstract
nThis project proposes a novel approach for gesture recognition using key point extraction and neural networks. Our proposed system leverages key point extraction techniques to capture fine-grained spatial information from input gestures. These key points are then fed into a neural network model, allowing for automatic feature learning and robust gesture classification. The goal of this project is to integrate OpenCV’s computer vision capabilities to build a flexible and effective home automation system. The system uses cameras to process images, which enables it to identify and react to different environmental cues in the house. Using a mobile or web application, a user-friendly interface makes it easier to remotely monitor and operate household appliances. A clever and flexible way to improve security, energy efficiency, and general convenience in a home is this home automation system, which combines the capabilities of OpenCV for image processing with Arduino for real-time control. The modular design of this project ensures scalability and relevance in the quickly developing field of smart home automation by allowing future extensions and integration with upcoming technologies.
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Keywords: Gesture recognition, Virtual reality control system, robust gesture recognition, Sign language recognition, CNN
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Microcontroller Engineering and Applications(jomea)]
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Browse Figures
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References
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Al-Jarrah, Omar, and AlaaHalawani.(2021), “Recognition of gestures in Arabic sign language using neuro-fuzzy systems.” Artificial Intelligence 133.1-2: 117- 138 [2] Alom, MdZahangir, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, PahedingSidike, MstShamimaNasrin, Brian C. Van Esesn, Abdul A. S. Awwal, and Vijayan K. Asari. (2018),”The history began from alexnet: A comprehensive survey on deep learning approaches.” arXiv preprint arXiv:1803.01164 . [3] Al-Qizwini, Mohammed, ImanBarjasteh, Hothaifa Al-Qassab, and HayderRadha. (2017), “Deep learning algorithm for autonomous driving using googlenet.” In 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 89-96. IEEE. [4] Aujeszky, Tamás, and Mohamad Eid.(2016), “A gesture recogintion architecture for Arabic sign language communication system.” Multimedia Tools and 12 75.14: 8493-8511. [5] Ayshee, TanzilaFerdous, Sadia Afrin Raka, QuaziRidwanHasib, Md Hossain, and Rashedur M. Rahman.(2014). “Fuzzy rule-based hand gesture recognition for bengali characters.” In 2019 IEEE International Advance Computing Conference (IACC), pp. 484-489. IEEE. [6] Doe J, Thorson E, Smith J. Employing Bayesian Inference Models to Bolster The Robustness of Graph Neural Networks. [7] Ibrahim NB, Zayed HH, Selim MM. Advances, challenges and opportunities in continuous sign language recognition. Journal of Engineering and Applied Sciences. 2020;15(5):1205-27. [8] Karmel A, Sharma A, Garg D. IoT based assistive device for deaf, dumb and blind people. Procedia Computer Science. 2019 Jan 1;165:259-69. [9] Triwijoyo BK, Karnaen LY, Adil A. Deep learning approach for sign language recognition. JITEKI: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika. 2023;9(1). [10] Cubo J, Nieto A, Pimentel E. A cloud-based Internet of Things platform for ambient assisted living. Sensors. 2014 Aug 4;14(8):14070-105.
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Journal of Microcontroller Engineering and Applications
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | May 7, 2024 | |
| Accepted | May 18, 2024 | |
| Published | August 14, 2024 |
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