Citation

Trisanto A, Hanafi L, Rohadi N, Syafei NS. ARPN J. Eng. Appl. Sci. 2023; 18(14): 1609-1614.

Copyright

(Copyright © 2023, Asian Research Publishing Network)

DOI

10.59018/0723200

PMID

unavailable

Abstract

In this study, fall detection was developed for the elderly or patients with balance problems who require monitoring all the time to reduce the impact of a greater fall. The system tracks the movement of the human body, identifies falls from normal daily activities by calculating acceleration and orientation, and then sends requests for help to nurses or their families via. Telegram messages and fall locations using the Blynk application. The results show that the system can distinguish between normal activities and falls. The system can detect falls forward, backward, and sideways to the left and right with an accuracy of 95%, 80%, 100%, and 75%, respectively.

Language: en