Fall Rate Detection, Identification and Analysis Object Oriented for Elderly Safety


Sudirman Sudirman(1*); Ansar Suyuti(2); Zahir Zainuddin(3); Arief Fauzan(4);

(1) Universitas Bosowa
(2) Universitas Hasanuddin
(3) Universitas Hasanuddin
(4) Universitas Bosowa
(*) Corresponding Author

  

Abstract


The aged population in Indonesia in 2021 is 30. Sixteen million people. The aged populace elderly 60 years and over reached 11.01% of the complete populace of Indonesia, which amounted to 273.88 million humans. There are ages who live on their own because of busy households with work. if there's an incident of falling elderly, a motion detection gadget is needed for monitoring the situation of the elderly at domestic. This takes a look at designing a visual synthetic intelligence hobby recognition gadget with entry from the digital camera to come across aged sports from video. take video records with the photograph Acquisition technique, Foreground Detection for changing photographs into binary, masks R-CNN to come to aware of detection items and discover the location of the incident, movement history photo, and C_motion to represent the placement of the detected object's body, SVM magnificence to categorize aged statistics falls or sports of every day residing. The experimental outcomes display that this device can come across the condensed-space version with an accuracy of ninety-seven, 50.

Keywords


Activity Recognition; Artificial Intelligence; Foreground Detection; Fall Detection; Machine Learning; Mask R-CNN; Motion History Image; Object Oriented Programming; SVM Classification.

  
  

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doi  https://doi.org/10.33096/ilkom.v16i1.1654.1-11 		  

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