Intelligent Fall Alert System for Identification and Fall Detection

Main Article Content

Narote Nilsukhum
Wiyada Yawai


At present, accidents caused by falls causing loss in the elderly and those living alone. The resulting loss can lead to disability or even death if not treated in time. Therefore, the developers propose a new technique for detecting falls. It uses image processing from IP cameras with MediaPipe and bounding box technology to help fall and gesture detection. And also adds to the identification of the person who was involved in the accident, which uses LBPH technology to know who the accident victim is. As for notifications, Line Notify is used to allow program users to access notifications easily and quickly. The accuracy of the test was 93.1% for identifying faces and 90.5% for detecting falling gestures.


Download data is not yet available.

Article Details

How to Cite
Nilsukhum, N., & Yawai, W. (2023). Intelligent Fall Alert System for Identification and Fall Detection. Journal of Applied Informatics and Technology, 6(1), 65–83. Retrieved from
Research Article


Buabok, K., Chinbunmee, S., Sansrimahachai, W., & Toahchoodee, M. (2016). A real-time mobility-related activity tracking system for mobility and fall risk assessment in elderly people. Journal of Information Science and Technology. 6(1), 16-24. [In Thai]

Bumrungrad International Hospital. (2022). Allergic contact dermatitis. Retrieved 12 January 2023. Retrieved from [In Thai]

Grishchenko, I. & Bazarevsky, V. (2020). MediaPipe holistic - Simultaneous face, hand and pose prediction, on device. Retrieved 10 January 2023. Retrieved from

iTecNote. (2022). Using atan2 to find the angle between two vectors. Retrieved 20 February 2023. Retrieved from

LINE Notify. (2020). สอนใช้ “LINE Notify” สร้างการแจ้งเตือนส่งตรงถึงแชทคุณ [How to use “LINE Notify” to create notifications sent directly to your chats]. Retrieved 30 January 2023. Retrieved fromสอนใช้-LINE-Notify-สร้างการแจ้งเตือนส่งตรงถึงแชทคุณ-9a43196756d2 [In Thai]

Rama Channel. (2018). หกล้มในผู้สูงอายุ [Falls in the elderly]. Retrieved 5 February 2023. Retrieved fromหกล้มในผู้สูงอายุ-อันตร/ [In Thai]

Somphaeng, P. & Jaruvitayakovit, T. (2017). 2D fall detection system with Bluetooth accelerometer sensor. Proceedings of National Conference on Information Technology (NCIT). [In Thai]

Srenual, P. & Kanokthet, T. (2021) Accidental fall in elderly: Thai elderly confidence dangers. 22(2), Journal of the Royal Thai Army Nurses. 65-70. [In Thai]

Tayanupap, A. & Yawai, W. (2022). Fall alarm notification system for the elderly via Line. Proceedings of The Asia Undergraduate Conference on Computing (AUCC), Chonburi, Thailand, Online, 2537. [In Thai]

Thawornwong, N., Akarasatthung, A., & Makasorn, P. (2011). Designing of falls detection for elderly by using tilt sensor. Naresuan University Journal: Science and Technology. Special 2011, 43-46. [In Thai]

Treeprapin, K., Naudom, P., & Kongchai, P. (2018). Attendance monitoring system with face recognition technologies. Journal of Science and Technology, Ubon Ratchathani University. 20(2), 92-105. [In Thai]

Wayalun, S. (2017). 3D image processing for indoor fall detection of elderly. Academic Journal of Phetchaburi Rajabhat university. 7(2), 108-118. [In Thai]

Wongyai, C., Puckdeevongs, A., & Buayamsang, S. (2020). A development of fall detection systems using wristband. Proceedings of RSU International Research Conference, Bangkok, Thailand, 249-257. [In Thai]

Wuttisit, C. (2020). Fall detection for elderly and data classification movement activity using weighted K-nearest neighbor algorithm on a IoT-based portable embedded system. Journal of Engineering, RMUTT. 18(1). [In Thai]

Yajai, A., Rodtook, A., Chinnasarn, K., & Rasmequan, S. (2015). Fall detection using directional bounding box. Proceedings of 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), Songkhla, Thailand, July 22-24, 2015, 52-57.