Image-Based Fall Detection with Human Posture Sequence Modeling

In this paper, an image-based method is presented for fall detection using statistical human posture sequence modeling. Specifically, a series of laboratory simulated falls and activities of daily living (ADLs) are performed and recorded by a Kinect sensor as training video data. The skeleton view of a human body in these video recordings is extracted using the Kinect for Windows SDK. Hidden Markov Models are used for modeling the fall posture sequences and distinguishing different fall activities and ADLs. Our experimental results demonstrate an average fall recognition rate above 80% and the capability of early warning for falls.