Pemodelan Alat Vein Finder dengan Penerapan Teknologi Berbasis Raspberry Pi dan Fitur Otomatis Penandaan Vena (VenaMark)
DOI:
https://doi.org/10.59841/intellektika.v3i3.2651Keywords:
vein finder, Raspberry Pi, IR camera, image processing, pen actuator, automatic vein markerAbstract
Vein finder is a crucial visualization tool in the medical field, especially for patients with difficult vein access. This study presents the development of a vein finder device named VenaMark, utilizing Raspberry Pi 4 Model B. The system not only displays real-time vein images using an infrared camera but is also equipped with an innovative feature: an automatic ink pen that directly marks the vein location on the patient’s skin. This feature aims to assist medical personnel in accurately determining injection or blood draw points without guessing. The system integrates digital image processing technology and precise mechanical actuators to move the marker pen based on the vein detection output. Testing results show increased efficiency and accuracy in medical procedures, along with reduced patient discomfort caused by failed needle insertions.
References
Bhattacharya, S., Ranjan, A., & Reza, M. (2022). A portable biometrics system based on forehead subcutaneous vein pattern and periocular biometric pattern. IEEE Sensors Journal, 22(7), 7022–7033.
Biglari, A., & Tang, W. (2023). A review of embedded machine learning based on hardware, application, and sensing scheme. Sensors, 23(4), 2131.
Chandra, G., Gultom, M. A., Wahyuningtyas, R., Dwiprasetijo, Z. A., Basari, & Rahman, S. F. (2023). Design and implementation of ‘Vein Finder’: An LED light-based vein detection system for medical applications. In International Conference on Biomedical Engineering of the Universiti Malaysia Perlis (pp. 41–52). Springer Nature Switzerland.
Godoy, R. I. U., Panzo, E. G. V., & Cruz, J. C. D. (2021, September). Vein location and feature detection using image analysis. In 2021 5th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)(Vol. 5, pp. 33–37). IEEE.
Ibrahim, N., Liang, L. K., Zheng, L. Z., Ling, L. Y., Khairulbadri, K. H., Mohamad, M., ... & Sari, S. (2022). Visualization of hand vein using Raspberry Pi images in contactless vein detector. In Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020: NUSYS’20 (pp. 273–281). Springer Singapore.
Roopashree, S., Anitha, J., Mahesh, T. R., Kumar, V. V., Viriyasitavat, W., & Kaur, A. (2022). An IoT based authentication system for therapeutic herbs measured by local descriptors using machine learning approach. Measurement, 200, 111484.
Saeed, A., Chaudhry, M. R., Khan, M. U. A., Saeed, M. A., Ghfar, A. A., Yasir, M. N., & Ajmal, H. M. S. (2024). Simplifying vein detection for intravenous procedures: A comparative assessment through near‐infrared imaging system. International Journal of Imaging Systems and Technology, 34(3), e23068.
Szymkowski, M. (2021). Raspberry Pi-based device for finger veins collection and the image processing-based method for minutiae extraction. In Computer Information Systems and Industrial Management: 20th International Conference, CISIM 2021, Ełk, Poland, September 24–26, 2021, Proceedings 20 (pp. 55–65). Springer International Publishing.
Tun, H. M. (2021). Photoplethysmography (PPG) scheming system based on finite impulse response (FIR) filter design in biomedical applications. International Journal of Electrical and Electronic Engineering & Telecommunications, 10(4), 272–282.
Bachrudin, Z., Widodo, C. E., & Adi, K. (2017). Simulator input-output sistem kontrol menggunakan Raspberry Pi. Dalam Youngster Physics Journal (Vol. 6, Nomor 3).
Gunawan, I., & Yelmi, Y. (2021). Rancang Bangun Robot Pengawas Dokumen Berbasis Raspberry Pi2 dengan Pemrograman Python. Jurnal Ilmu Komputer dan Bisnis, 12(1), 144–149. https://doi.org/10.47927/jikb.v12i1.99
Muchtar, H., & Apriadi, R. (2020). Implementasi Pengenalan Wajah Pada Sistem Penguncian Rumah dengan Metode Template Matching Menggunakan Open Source Computer Vision Library (Opencv). 2(1).
Yunus, M. (2011). Perbandingan Metode-Metode Egde Detection untuk Proses Segmentasi Citra Digital.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Intellektika : Jurnal Ilmiah Mahasiswa

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.