Stabilization of Distance Measurement Between Landmarks for Gesture Recognition Using Polynomial Regression
DOI:
https://doi.org/10.62951/ijamc.v1i3.120Keywords:
Gesture Recognition, Hand Landmark Detection, MediaPipe, Polynomial Regression, Sign Language RecognitionAbstract
Gesture recognition technology enables computers and digital devices to detect, understand, and interpret human body movements through image processing techniques. This technology has significant potential to facilitate communication between individuals with hearing impairments and those without, thereby improving interaction and mutual understanding. However, the accuracy of gesture recognition systems is often influenced by variations in the distances between hand landmark points, which can introduce instability and reduce recognition performance. To address this issue, this study proposes a polynomial regression-based approach to stabilize distance measurements between hand landmarks in gesture recognition tasks. The proposed method calculates and normalizes landmark distances using polynomial regression to minimize measurement fluctuations and improve recognition accuracy. The system is implemented using the MediaPipe framework for real-time hand detection and tracking, while OpenCV is utilized for video processing and management. Experimental results demonstrate that the proposed approach significantly enhances the stability and accuracy of gesture detection. The developed system successfully recognizes hand gestures representing the letters A through F with an average accuracy exceeding 98.3%. Furthermore, the application of polynomial regression effectively reduces noise in landmark data, contributing to more reliable and accurate gesture recognition performance.
References
P. Srinil and P. Thongnim, “Deep Learning Enhanced Hand Gesture Recognition for Efficient Drone Use in Agriculture,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 5, 2024, doi: 10.14569/IJACSA.2024.01505127.
M. Oudah, A. Al-Naji, and J. Chahl, “Hand Gesture Recognition Based on Computer Vision: A Review of Techniques,” J. Imaging, vol. 6, no. 8, p. 73, 2020, doi: 10.3390/jimaging6080073.
N. P. A. Perdana, N. D. Kirana, N. C. Iroth, A. Salsabila, and R. A. F. B., “Fenomena Penggunaan Bahasa Isyarat Bagi Penyandang Tuna Rungu di Sekolah Inklusi,” Hasanuddin J. Sociol., vol. 4, no. 2, pp. 120–134, 2022.
R. P. Adi, “Fungsi Bahasa Isyarat terhadap Kemudahan Akses Informasi Bagi Siswa Tunarungu di Perpustakaan SLB N 1 Bantul,” Universitas Islam Negeri Sunan Kalijaga, Yogyakarta, Indonesia, 2019.
A. Sonawane, A. Bhoskar, M. Azam, S. Belhim, and N. Zaman, “Sign Language Detection Without Sensors,” J. Basic Sci., vol. 22, no. 11, pp. 278–284, 2022.
J. Li et al., “Sign Language Recognition and Translation: A Multi-Modal Approach Using Computer Vision and Natural Language Processing,” in Proceedings of the International Conference Recent Advances in Natural Language Processing, Varna, Bulgaria, 2023. doi: 10.26615/978-954-452-092-2_071.
D. I. Mulyana, M. B. Yel, and A. Sitohang, “Detection of the Deaf Signal Language Using the Single Shot Detection (SSD) Method,” J. Appl. Eng. Technol. Sci., vol. 4, no. 1, pp. 215–222, 2022, doi: 10.37385/jaets.v4i1.966.
M. H. Abidi, M. K. Muhammad, and H. Alkhalefah, “Deteksi Bahasa Sinyal Tuli Menggunakan Metode Single Shot Detection (SSD),” J. Tek. Terap. dan Ilmu Teknol., vol. 4, no. 1, pp. 215–222, 2022.
A. Tayade and A. Halder, “Real-Time Vernacular Sign Language Recognition Using MediaPipe and Machine Learning,” Int. J. Res. Publ. Rev., vol. 2, no. 5, pp. 9–17, 2021, doi: 10.13140/RG.2.2.32364.03203.
P. B. Utomo, R. A. Ramadhani, and H. Kurniawan, “Deteksi Gerak Tangan sebagai Pengenal Bahasa Isyarat Menggunakan MediaPipe dan Long Short-Term Memory,” J. Simetris, vol. 15, no. 1, pp. 1–8, 2024.
J. Medellu, A. Sambul, and A. S. M. Lum, “Hand Gesture Detection Application in Sign Language,” J. Tek. Inform., vol. 17, no. 4, pp. 285–296, 2022.
A. Sani and S. Rahmadinni, “Deteksi Gestur Tangan Berbasis Pengolahan Citra,” J. Rekayasa Elektr., vol. 18, no. 2, pp. 115–124, 2022.
H. Budiati and A. R. Himamunanto, “Metode Subtraksi Citra Sebagai Upaya Deteksi Gerakan Tangan,” Pros. SAINTEK Sains dan Teknol., vol. 1, no. 1, 2022.
Abhigyan, “Understanding Polynomial Regression,” 2020, Medium. [Online]. Available: https://medium.com/analytics-vidhya/understanding-polynomial-regression-5ac25b970e18
T. C. A. Zulkhaidi, E. Maria, and Y. Yulianto, “Pengenalan Pola Bentuk Wajah dengan OpenCV,” J. Rekayasa Teknol. Inf., vol. 3, no. 2, pp. 181–185, 2019.
A. H. Nur’azizan, A. R. Ardiansyah, and R. Fernandis, “Implementasi Deteksi Bahasa Isyarat Tangan Menggunakan OpenCV dan MediaPipe,” in Prosiding Seminar Nasional Teknologi dan Sains, 2024.
R. Soebiartika and I. Rindaningsi, “Systematic Literature Review (SLR): Implementasi Sistem Kompensasi dan Penghargaan Terhadap Kinerja Guru SD Muhammadiyah Sidoarjo,” J. Manaj., vol. 2, no. 1, pp. 171–185, 2023.
I. W. Ningsi and others, “Metode Systematic Literature Review untuk Identifikasi Metode Pengembangan Sistem Informasi di Indonesia,” J. Sist. Inf. dan Manaj., vol. 10, no. 3, pp. 204–209, 2022.
K. Widiarsa, “Kajian Pustaka (Literature Review) sebagai Layanan Intim Pustakawan Berdasarkan Kepakaran dan Minat Pemustaka,” Media Inf., vol. 28, no. 1, pp. 111–124, 2019.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Applied Mathematics and Computing
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
