This paper proposes an algorithm for fall detection using 2D RGB camera. Occlusion, fall, and common daily activities are separated from each other by machine learning algorithms, which were trained on features extracted by a deep learning-based computer vision algorithm. This later is used for person detection. The experimental validation of the proposed approach was conducted on two datasets, one public, and the second created by experiments. For evaluation, several assessment measures are computed. This evaluation shown effectiveness of the proposed solution.


Fall detection, Camra, Machine learning, Deep learning, e-health