Advancing Fall Detection Utilizing Skeletal Joint Image Representation and Deformable Layers

Abstract

Falls are a significant concern among the elderly population, with 25% of individuals over 65 years old experiencing a fall severe enough to require a visit to the emergency department each year. Early detection of falls can prevent serious injuries and complications, making it an important problem to address. There are various methods for detecting falls, utilizing different types of sensor input data. However, when considering factors such as ease of setup, accessibility, and accuracy, utilizing cameras for fall detection is a highly effective approach. In this study, a novel video-based fall detection algorithm that relies on skeleton joints is introduced. The results of pose estimation are preprocessed into an image representation and ShuffleNet V2 model with the addition of a Deformable Layer is employed for classification. Experiments were carried out on four distinct datasets: URFD, UP-Fall Detection, Le2i, and NTU RGB+D 60, which encompass individuals engaged in various activities, including falls. The results showcase exceptional performance across all these datasets, affirming the efficacy of the approach in accurately detecting falls in video footage.