Accidental Fall Detection Based on Skeleton Joint Correlation and Activity Boundary

We propose a system to detect accidental fall from walking or sitting activity in a nursing home. Differing from the trajectory tracing techniques, which detects periodic movements, our algorithm explores secondary features (angle and distance), focusing on the correlation between joints and the boundary of this correlation. We generated skeleton joint data using the Kinect sensor because it is affordable and supports sufficiently large capture space. However, other similar smart sensors can also be used. The angle feature denotes the correlation between the normal vector of the floor and the vector formed by linking the knee and ankle (on the left and right leg separately). The distance feature denotes the correlation between the floor and each of several important joints. A fall is reported when the angle is greater than and the distance is less than the respective threshold value. We created an activity database to evaluate our technique. The activities simulate elderly people walking, sitting and falling. Experimental results show that our algorithm is simple to implement, has low computational cost and is able to detect 36/37 falling events, and 57/57 walking and sitting activities accurately.