Fall Recognition System to Determine the Point of No Return in Real-Time

In this study, we collected data on human falls, occurring in four directions while walking or standing, and developed a fall recognition system based on the center of mass (COM). Fall data were collected from a lower-body motion data acquisition device comprising five inertial measurement unit sensors driven at 100 Hz and labeled based on the COM-norm. The data were learned to classify which stage of the fall a particular instance belongs to. It was confirmed that both the representative convolutional neural network learning model and the long short-term memory learning model were performed within a time of 10 ms on the embedded platform (Jetson TX2) and the recognition rate exceeded 94%. Accordingly, it is possible to verify the progress of the fall during the unbalanced and falling steps, which are classified by subdividing the critical step in which the real-time fall proceeds with the output of the fall recognition model every 10 ms. In addition, it was confirmed that a real-time fall can be judged by specifying the point of no return (PONR) near the point of entry of the falling down stage.