Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Falls are a common cause of fatal injuries and hospitalization. However,
having fall detection on person, in particular for senior citizens can prove to
be critical. Presently,there are handheld, ambient detector and vision-based
detection techniques being utilized for fall detection. However, the approaches
have issues with accuracy and cost. In this regard, in this research, an
approach is proposed to detect falls in indoor environments utilizing the
handcrafted features extracted from human body skeleton. The human body
skeleton is formed using MediaPipe framework. Results on UR Fall detection show
the superiority of our model, capable of detecting falls correctly in a wide
number of settings involving people belonging to different ages and genders.
This proposed model using MediaPipe for fall classification in daily activities
achieves significant accuracy compare to the present existing approaches.
