Sistem Deteksi Jatuh Lansia Real-Time Berbasis YOLOv8 dengan Notifikasi Telegram dan Dashboard Web

Claudio Syanu Mareta Dinata; Rina  Firliana; Arie  Nugroho

doi:10.47065/mis.v4i3.2885

Claudio Syanu Mareta Dinata * Universitas Nusantara PGRI Kediri, Indonesia
Rina Firliana Universitas Nusantara PGRI Kediri, Indonesia
Arie Nugroho Universitas Nusantara PGRI Kediri, Indonesia

DOI: https://doi.org/10.47065/mis.v4i3.2885

Keywords: fall detection; YOLOv8; real-time; dashboard monitoring; Telegram notification; computer vision

Abstract

Falls in the elderly represent a serious global health problem. According to the WHO, one in three elderly people aged over 65 years experiences a fall each year. In Panti Werda Kediri, monitoring of elderly activities is still performed manually by staff, causing fall incidents to go undetected quickly. Previous YOLO-based fall detection studies generally produce models without integrating them into monitoring platforms usable by non-technical end users and without automatic notification. This research aims to determine the effectiveness of a monitoring system in detecting normal activities and fall incidents in elderly residents in real time at Panti Werda Kediri using YOLOv8. The system was developed using the Waterfall method through stages of requirements analysis, system design, implementation, and testing. The detection component uses a retrained YOLOv8 model to recognize two classes: normal and fall. The backend is built with FastAPI and PostgreSQL, equipped with a web-based monitoring dashboard and automatic notifications via Telegram Bot. A fall confirmation mechanism based on 3 consecutive frames with a 1.5-second cooldown suppresses false positives. Blackbox testing conducted at Panti Werda Kediri shows all 10 test scenarios passed. The system successfully sends real-time Telegram notifications in under 2 seconds with visual evidence each time a fall is confirmed, provides live camera streaming, and displays complete detection history through a web dashboard accessible to non-technical staff.

References

World Health Organization, "Falls," WHO Fact Sheet, 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/falls

Badan Pusat Statistik, Statistik Penduduk Lanjut Usia 2023. Jakarta: BPS Republik Indonesia, 2023.

Kementerian Kesehatan Republik Indonesia, Profil Kesehatan Indonesia 2024. Jakarta: Kemenkes RI, 2024.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. Hoboken: Pearson Education, 2021.

G. Jocher, A. Chaurasia, and J. Qiu, "Ultralytics YOLO (Version 8.0.0)," Computer Software, Ultralytics, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

H. Hwang, D. Kim, and H. Kim, "FD-YOLO: A YOLO network optimized for fall detection," Applied Sciences, vol. 15, no. 1, p. 453, 2025. https://doi.org/10.3390/app15010453

E. Tirziu, A. M. Vasilevschi, A. Alexandru, and E. Tudora, "Enhanced fall detection using YOLOv7-W6-Pose for real-time elderly monitoring," Future Internet, vol. 16, no. 12, p. 472, 2024. https://doi.org/10.3390/fi16120472

M. Syarif, C. Setianingsih, and A. Novianty, "Pengembangan sistem monitoring perawatan lanjut usia menggunakan webcam dan algoritma YOLOv7," e-Proceeding of Engineering, Telkom University, 2024.

B. Luo, "Human fall detection for smart home caring using YOLO networks," International Journal of Advanced Computer Science and Applications, vol. 14, no. 4, 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140409

H. Wang, S. Xu, Y. Chen, and C. Su, "LFD-YOLO: A lightweight fall detection network with enhanced feature extraction and fusion," Scientific Reports, vol. 15, no. 1, p. 5069, 2025. https://doi.org/10.1038/s41598-025-89214-7

A. Junito and S. Budilaksono, "Rancang bangun sistem deteksi jatuh pada penghuni rumah berbasis convolutional neural network," Tekinfo, vol. 26, no. 1, 2025. https://doi.org/10.37817/Tekinfo.v26i1

M. Y. Efendi, R. Wulanningrum, and A. B. Setiawan, "Rancang bangun sistem deteksi manusia dengan YOLO pada video CCTV," Prosiding Seminar Nasional, vol. 8, 2024.

Z. Ye, "Elderly fall detection based on YOLO and pose estimation," in Proc. International Conference on Pattern Recognition and Machine Learning, INSTICC, 2024, pp. 10-17.

M. I. M. Abu.Zaid, R. B. Abdullah, S. Izawana, and N. N. S. Nik Dzulkefli, "IoT-based emergency alert system integrated with Telegram Bot," in 2023 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2023. https://doi.org/10.1109/I2CACIS57635.2023.10193550

S. Ramirez, "FastAPI (Version 0.95.0)," Computer Software, 2021. [Online]. Available: https://fastapi.tiangolo.com

R. Firliana, A. S. Wardani, M. N. Muzzaki, H. Stiawan, and A. W. M. Gamas, "Implementasi manajemen proyek pada pengembangan website pemetaan biodiversitas tanaman obat di Kabupaten Kediri," Bulletin of Information Technology (BIT), vol. 3, no. 4, pp. 289-293, 2022.

I. Zamarli, H. Kurniawan, and W. Darwin, "Implementasi algoritma YOLOv8 untuk mendeteksi objek manusia secara real-time menggunakan IP camera," Jurnal Penelitian Nusantara, vol. 1, pp. 432-441, 2025.

R. N. Zakaria, R. Wulanningrum, and A. B. Setiawan, "Penerapan segmentasi wajah menggunakan YOLOv8 untuk presensi mata kuliah," Prosiding SEMNAS INOTEK, vol. 8, 2024.