# Fall Recognition System to Determine the Point of No Return in Real-Time
## 1 Introduction  
Falls have long been researched from various perspectives [1; 2; 3; 4; 5]. Falls among the elderly have emerged as a social issue owing to an exponential increase in the elderly population [6]. A survey on the welfare and living conditions of the elderly (2017), published by the Ministry of Health and Welfare (MOHW), showed that the fall rate of seniors was 15.9% in 2017 [6]. Existing studies on falls can be mainly classified according to whether the fall detection systems used are based on camera images or inertial measurement units (IMUs). When protective devices for preventing falls--such as airbags--are used, fall detection systems based on IMUs are generally utilized. Previous studies on fall detection systems that apply IMUs focused on calculating a threshold based on the significant impacts generated by falls or a rapid change in acceleration or developing measures or services that can instantly cope with falls [7; 8; 9]. With the development of learning-based algorithms since 2010, research [10; 11; 12] on utilizing fall data obtained based on computer vision and data obtained by IMU sensors [13; 14; 15; 16] has been actively conducted. To utilize a learning-based algorithm, a data set containing the characteristics of the problem to be solved is required; more datasets provide better results. The sisFall dataset, which reflects 15 types of falls based on five cases for each fall type, was recently released [17]. However, the volume of fall data in this dataset was insufficient. In some studies, falls were determined by considering areas related to the center of mass (COM) and the base of support (BOS) of human beings as feature points based on data collected by Kinect cameras [18; 19]. [PERSON] (2017) predicted falls based on data obtained by Kinect cameras and a long short-term memory (LSTM) algorithm. Specifically, falls were predicted under the condition of 5 and 10 frames before the standard momentof collision with the floor. However, the prediction result (75%) obtained when applying the condition of five frames before the standard moment was less accurate than under the condition of 10 frames (91.7%) [19]. Similar to the public sisFall data set, the fall data set collected in existing fall studies was collected by designing and mimicking the fall situation in several cases and the data set quantity was insufficient. To reflect the characteristics of falls that occur in real situations, a method is needed for reproducing a realistic fall situation. In addition, a method for collecting a large amount of data and for determining the progress of the fall in real-time is needed. Thus, this study proposes a real-time fall progress detection system that can reflect practical falls that occur in daily life, e.g., when walking or standing. Hence, fall data were obtained via a fall implementation device that can reproduce realistic fall situations to reduce artificial elements that might be generated in a laboratory and maximize the volume of fall data obtained. Moreover, a learning model was operated based on a unit of 10 ms to monitor the progress of falls and ultimately determine falls. In addition, IMU sensor data were utilized for wearable situations. We thus developed a lower limb motion caption device, visualization S/W monitoring motion data, and a fall-reproducing device that induces falls. These devices were utilized to collect fall data and the fall data collection experiment was conducted with the approval of the Institutional Review Board (IRB) at Konyang University (project no. : KYU-2020-055-02). The remainder of this paper is organized as follows. In Section 2, the critical phase of falls is divided into an imbalance phase and a falling phase to facilitate real-time fall recognition, and COM-based labels are described. In Section 3, the devices and methods used for data collection are described. The learning and recognition results are presented and analyzed in Section 4.