Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature
This addresses fall detection for senior citizens, but it is incremental as it builds on existing vision-based methods.
The paper tackled fall detection in indoor videos by proposing a model that uses MediaPipe to extract handcrafted skeleton features, achieving significant accuracy on the UR Fall dataset.
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.