## 6 Conclusions  
In this paper, the STM-LSTM-based fall detection system that combines the simple threshold method and the LSTM was proposed. The proposed system was based on the single and multiple parameters calculated from the three-axial acceleration data. In training mode, the parameters were normalized and then the parameters were learned using LSTM. In test mode, the first possible fall was primarily determined when both \(P_{S}\) and \(P_{\theta}\) exceeded the thresholds of 2.5g and 46.42\({}^{\circ}\), respectively. The first possible fall data were just classified using LSTM learned in training mode. To examine validity of the proposed STM-LSTM fall detection system, it was compared with the previously reported STM-HMM-based fall detection system. The best training accuracy by the STM-LSTM-based fall detection system is 100% for \(P_{G}\), 0.5% higher than that of the STM-HMM-based system. However, since training data accuracy for this comparison is a result of training data only without validity data, the risk of overfitting may be occurred. To solve the overfitting problem and find optimal values of the parameters used in LSTM, the validation data performance calculated from the validation data, using the network parameters obtained from the training data, was investigated with respect to normalization method, sampling, regularization rate, and hidden layer node. For normalization and sampling, it showed the best performance in no normalization and no sampling. The computation time by 128 hidden layer nodes was significantly shorter than that of 256 nodes although the same best accuracy was 98.21% for both 128 and 256 nodes. As the number of hidden layer nodes or sampling interval increase, the regularization rate at the highest value of accuracy increases. This can be interpreted as suppressing overfitting by increasing the regularization rate. Therefore, the fall detection efficiency can be improved by selecting an appropriate number of hidden layer nodes and regularization rate.  
However, this paper still has many directions worthy of improvement. The proposed STM-LSTM fall detection system was compared with the previously reported STM-HMM fall detection system, and the data set of the previously reported STM-HMM fall detection system was used and it has the following limitations: First, it is necessary to verify a reliability through comparison with the data set acquired from the self-developed embedded edge device and that from commercial device. Second, it is very simple because it consists only of four types of ADL and three types of fall gestures. Third, as the subject population consists of four men and two women, it has a gender and age imbalance. To solve this dataset problem, it is necessary to apply this proposed STM-LSTM fall detection system to public datasets verified for fall detection. When public datasets are applied to the proposed system in the future, they are pre-processed with single, double, and multiple parameters,and optimal network parameters can be newly and easily found using the optimal network parameters and optimization method introduced in this paper.  
Conceptualization, S.S.J., N.H.K., and Y.S.Y.; methodology, S.S.J., N.H.K., and Y.S.Y.; investigation, S.S.J. and Y.S.Y.; data curation, S.S.J.; writing--original draft preparation, S.S.J. and Y.S.Y.; writing--review and editing, S.S.J. and Y.S.Y.; supervision, Y.S.Y.; project administration, Y.S.Y.; funding acquisition, Y.S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through NRF of Korea funded by the Ministry of Education (NRF-2019R1F1A1060383).  
Not applicable.  
The authors declare no conflict of interest.