Most of the computer vision applications for human activity recognition exploit the fact that
body features calculated from a 3D skeleton increase robustness across persons and can
lead to higher performance. However, their success in activity recognition, including falls,
depends on the correspondence between the human activities and the used joint/part
features. To provide for this correspondence, we experimentally evaluate in this paper
skeleton features-based fall detection by comparing fall detection performance for different …