AIFMS Autonomous Intelligent Fall Monitoring System for the Elderly Persons

Elangovan Ramanujam and Thinagaran Perumal
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Elangovan Ramanujam: School of Engineering and Technology, Christ University (Deemed), India
Thinagaran Perumal: Universiti Putra Malaysia, Malaysia

International Journal of Ambient Computing and Intelligence (IJACI), 2022, vol. 13, issue 1, 1-22

Abstract: Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during day vision. However, fall occurs more at the night due to many factors such as low or zero lighting conditions, intake of medication/ drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%.

Date: 2022
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