Open AccessHUMAN ACTIVITY RECOGNITION AND FALL DETECTION
Author(s) -
Disha Deepak Jatkar,
Anil Surve
Publication year - 2021
Publication title -
international journal of engineering applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-2143
DOI - 10.33564/ijeast.2021.v06i03.042
Subject(s) - falling (accident) , unconsciousness , elderly people , physical medicine and rehabilitation , weakness , computer science , computer security , artificial intelligence , medicine , medical emergency , gerontology , environmental health , psychiatry , anatomy
Personalized monitoring and its application isincreasing with the advancement of technology. Andduring pandemics its become very essential to keep an eyeon the prone area and one of the area to identify was oldage home. Dizziness, unconsciousness, and others are thecommon problems associated with elderly people due toweakness and this was also the symptoms of covid. So anunusual activity of falling of elderly people was verydifficult to identify and also to monitor. The technologywas updated till now to identify posture of normal activitysuch as running, walking, jumping and many but revert tothat falling was an area need to explore. During the fall ofan elderly person, the injuries are very fatal, and to voidthis case we proposed a design to identify the fall and tryto notify the system about its fall. Although we try topredict the fall so that it becomes easy to monitor andprovide medical help as soon as possible. The main themeis to identify the posture activity and once identify we willcompare the activity with trained datasets and if it’snormal in vision them no notification occurred and if thepercentage of falls was high then we can predict the systemas fall video