An automatic human fall detection approach using RGBD cameras

Unexpected fall remains the major threat for elderly since it can cause severe consequences. In this paper, an automatic human fall detection approach is proposed using RGB-Depth (RGBD) cameras. Based on the skeleton and joints data, three velocity features and a head-hip height difference feature are extracted within each sliding window. The features are then gradually analyzed and compared to adaptive thresholds by the proposed multi-step fall detection method. We also conducted a series evaluation on the determination of thresholds and the whole approach. The experiments show a good result with a high F1-measure of 0.944.