# Fall Recognition System to Determine the Point of No Return in Real-Time
## 4 Learning and Recognition Results  
For fall data learning, the LSTM algorithm as shown in Figure 6a and CNN as shown in Figure 6b were applied. In the LSTM model, the input sensor data were analyzed as much as the time step of the window size to classify the category to which it belongs. In the CNN model, the window size of the IMU sensor data and the number of sensor inputs were mapped to the height and width of the image data, as was reported by [PERSON] et al. in 2018 [20] and [PERSON] et al. in 2019 [21]. This was applied to as being used as the input data that was applied to the CNN algorithm as shown in Figure 6b. Softmax was used for classification in the final output. Figure 5: Dual-belt treadmill device for fall simulation experiment; (**a**) 3D-cad model of dual-belt treadmill device, (**b**) the fall simulation experimental system, and (**c**) data flow diagram of the system. Figure 6: Learning models. In the learning process, the window size was established based on the duration of the imbalance and falling down phases. The results of the fall data analysis used in the learning process revealed that the mean durations of the imbalance and falling phases were 420 and 250 ms, respectively. The fall data were input at intervals of 10 ms. Therefore, the time scale of the window was 250 ms. The mean duration of the falling down phase was calculated with a window size set to 25. The trend of recognition rates of the learning model was examined during the learning process, where the window size ranged from 8 to 24. The learning rate was set to 0.001 and the mean squared error was used as a loss function. ADAM (lr = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = \(1\times 10^{-8}\)) was utilized as the optimizer. As shown in Figure 7, tests on the learning models were conducted using data that were not applied in the learning process. A single fall case was saved as a single file. As shown in Figure 7a, the test results for each fall case were stored as CSV files. As shown in Figure 7b, the true label values and predicted values were plotted at the top and bottom, respectively, to simplify the analysis. The predicted result was correct if the colors of the bars at the top and bottom were the same (otherwise, they were incorrect). Regarding the input features indicated in Figure 8, the recognition rates of the learning models were measured with (blue line) and without (orange line) the yaw value of the gyro and the COM for comparison. Figure 8 shows that the recognition rate of the CNN model on the ground tended to decrease gradually owing to its greater window size. When the COM was input as a feature, its recognition rate increased. However, the recognition rate of the LSTM model is not affected by window size. When the COM was input as a feature, the recognition rate increased. As shown in Figure 9, when the fall progress is viewed as a data flow, the structure of the fall data has a cascading change structure (a structure in which data in the same  
Figure 8: Results of recognition rates of CNN and LSTM models. Figure 7: Recognition rates of learning models according to cases. state is continuous and then changed to another state section at one moment). When data containing a transition section were input to a model trained by the CNN, recognition errors mainly occurred near the transition section. In addition, the number of recognition errors increased owing to the larger window size. It was conjectured that a recognition error occurred because the information in the previous section and information in the next section coexist in the image information corresponding to the window size in the transition section. In contrast, the LSTM model generated a lower number of recognition errors in the transition section. It was deduced that the difference between the recognition rates of the CNN and LSTM models indicated in Figure 8 was caused by a difference in the degree of recognition errors in the transition section.