# An Image-Based Fall Detection System for the Elderly
## 5 Experiment
### Performance Comparison  
We compare IFADS with the method that [PERSON] et al. [6] has proposed, which is the most intuitive method. The method detects falls by the distance of the human centroid from the floor plane, and the  
Figure 19: The results of the video case studies. authors consider that 0.4 m provides the best choice reduce false alarms. Thus, we recognize falls when the distance of the tester's centroid from the floor plane is less than 0.4 m manually. We test the method without using the video case studies, since we cannot know the real height of the people in the videos. Table 3 shows the results of that method. A true positive (TP) outcome occurs when the method correctly detects a fall case. A false positive (FP) outcome occurs when the method creates a false alarm. A false negative (FN) outcome occurs when the method misses a fall case. A true negative (TN) outcome occurs when the method correctly detects a non-fall case. When the tester falls while sitting down or trying to stand up, as in Case 10 to Case 16 in the common situation, and when the tester keeps his back straight or does not look down, it is difficult for the method to detect a fall. If the tester is taller than 180 cm, it is more difficult to detect a fall. On the other hand, if the person falls while standing up from a chair, as in Case 17 to Case 23 in the common situation, the method is likely to detect the fall. In addition, as the existing image-based method detects falls by recognizing the posture of the person in the current frame, we compare IFADS with the machine learning method. We consider that the method cannot detect a fall when the method cannot detect the person. As Figure 20 shows, the person may be unable to be detected or may be detected as another object. Thus, we adopted the TensorFlow object detection approgramming interface (API) [48], which contains various models and neural networks (e.g., single shot detector (SSD) [49], MobileNet [50], region-based convolutional neural network (faster_RCNN) [51], inception [52], region-based fully convolutional networks (RFCN) [53], resnet101 (deep residual network) [54], and neural architecture search (NAS) [55]) to detect objects. IFADS adopts YOLO to detect falls. Table 4 shows the results of a falling person detected by the machine learning methods. In the fall cases, YOLO and ssd_mobilenet_v1_coco are faster than the other models, but the accuracy is only up to 59.7%. The accuracy of faster_RCNN_inception_v2_coco, RFCN_resnet101_coco, and faster_RCNN_NAS is up to 80.6%; however, they spend at least 10.03 s per frame, so they should not be adopted in real time. In summary, as Table 3 shows, the recall of the method that was proposed by [PERSON] et al. [6] was only 58.70%. Their method did not perform well in detecting falls while sitting down due to the poor prediction. Also, their method cannot identify the relationship between the person and environmental objects and may lead to an inaccurate result. Further, multiple cameras are required to determine the status of a walking person. As Table 4 shows, although YOLO and ssd_mobilenet_v1_coco can complete the computation quickly (0.13 s and 6.22 s, respectively), the accuracy was very low. Other methods, such as faster_RCNN_inception_v2_coco, RFCN_resnet101_coco, and faster_RCNN_NAS, can achieve a higher accuracy but require a longer computation time. Compared with the above-mentioned methods, IFADS performed better in detecting falls that occur while forward walking, sitting down, and standing up. IFADS can be used by not only nursing homes but also hospitals to improve the quality of health care facilities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Actual: Fall** & **Actual: Non-Fall** & \\ \hline Predicted: Fall & 27 (TP) & 0 (FP) & Precision = \(27/27=100\%\) \\ \hline Predicted: Non-fall & 19 (FN) & 37 (TN) & - \\ \hline Recall = \(27/46=58.70\%\) & - & Accuracy = \(64/83=77.11\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results from the method [6].