# Fall Recognition System to Determine the Point of No Return in Real-Time
## 6 Conclusions and Further Research  
A fall goes through the stages of fall progression, as shown in Figure 1. If the instantaneous scene can be mapped to a label value representing the fall progression stage, then the fall progression is expressed as a sequence of label values. The scene at every moment during the fall was mapped to sensor data comprising window_size \(\times\) input_num. In this study, considering the input data and real-time execution time, the length of each moment was set to 10 ms. The stage at which the instantaneous scene was made up of sensor data for every moment 10 ms belongs to the subdivided fall progression stage. The progress of the fall could be judged by the continuous label values determined in this way and it could also be determined whether the person was passing the PONR point. The major factors in achieving this were that more accurate labeling was possible based on the COM and that the inference execution time of the learned model was within 10 ms. To estimate the inference execution time of the learned model, the multi-process structure was utilized as much as possible and learning was performed by setting a small window size of the input data.  
However, this study has several limitations. First, collecting an enormous volume of fall data that completely reflected practical situations was difficult. Furthermore, the standards used to collect such data may vary. In this regard, further research should be conducted to increase the data volume through a method [22] that can modify sensor data while preserving the labels. Another approach would be to apply a method that will enable the learning model to perform reinforcement learning of the simulated results for the human body based on sensor data obtained by the fall implementation device and generate new fall data through the learning process. Specifically, simulation based on reinforcement learning can be useful for simulating risky situations in which a fall test cannot be performed several times.  
Conceptualization, B.S.K., J.J., D.-W.L., Y.K.S. and H.C.S.; methodology, B.S.K., D.-W.L., Y.K.S. and J.J.; software, B.S.K., D.-W.L. and Y.K.S.; validation, B.S.K., J.J. and D.-W.L.; formal analysis, B.S.K., J.J. and D.-W.L.; investigation, Y.K.S., J.J. and D.-W.L.; resources, B.S.K., Y.K.S. and J.J.; data curation, B.S.K. and Y.K.S.; writing--original draft preparation, B.S.K.; writing--review and editing, B.S.K., D.-W.L., Y.K.S., J.J. and H.C.S.; visualization, B.S.K. and Y.K.S.; supervision, Y.K.S. and H.C.S.; project administration, Y.K.S. and H.C.S.; funding acquisition, Y.K.S. and H.C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (2017-0-01724, Development of A soft wearable suit using intelligent information and meta-material/structure technology for fall prediction and prevention) and by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (2017-0-00050, Development of Human Enhancement Technology for auditory and muscle support).  
The fall data used in this study were obtained with the approval of the IRB at Konyang University (project no.: KYU-2020-055-02).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.