## 1 Introduction  
It was reported by the World Health Organization (WHO) that 30% of the population aged 65 or older suffer from falls more than once a year and this percentage increases to 50% in the population aged 80 or older [1, 2]. The fall-related consequences are serious injuries (e.g., femoral neck fracture, brain damage, skin burns), which in most cases lead to physical and cognitive disability or death in cases of undetected falls [2, 3, 4]. To reduce these risks, such as death rate and severity of injuries, by detecting falls as quickly as possible, researchers are now focusing on developing automatic fall detection systems that generate alerts when events occur [5, 6, 7]. Studies on fall detection of the elderly with wearable devices [8, 9, 10] or smartphones [11, 12, 13, 14, 15] using 3-axial accelerometer have been reported. The devices with 3-axial accelerometer are attached to the body to distinguish between falls and activities of daily living (ADL). Among the existing studies, a simple threshold method (STM) and machine learning methods for fall detection of the elderly were reported [16, 17, 18, 19, 20, 21, 22, 23]. The STM is weak against noise, and it is difficult to distinguish it from patterns relatively similar to falls such as lying or sitting. Machine learning methods, such as hidden Markov model (HMM) [17, 18], layered hidden Markov model (LHMM) [19], decision tree (DT) [20], K-nearest neighbor (KNN) [21, 22], and support vector machine (SVM) [22, 23], provided a higher accuracy in fall detection than the STM. In the case of HMM, there are many problems in obtaining a temporally aligned sequence and ensuring that the data satisfy a fixed distribution [24]. SVM has the disadvantage of increasing resource consumption and slowing down as the number of classifications increases [25]. To compensate for these shortcomings, research using deep neural networks was needed. Accordingly, there is an increasing number of reports on research on fall detection systems that apply deep neural networks to wearable devices [26, 27, 28, 29, 30, 31]. Gated recurrent unit (GRU) [27], recurrent neural network (RNN), long short-term memory (LSTM) [26, 27, 28, 29, 30, 31], and LSTM combined with convolutional neural network (LSTM-CNN) [30, 31] have been studied using 3-axis accelerometer data for fall detection in the elderly. In most studies using LSTM, and GRU, attempts have been made to adjust the LSTM network, combine various networks, and change the type of sensors [32, 33, 34]. In LSTM-CNN, methods, such as 1-dimensional (1D) or 2-dimensional (2D) convolution depth and layer addition, were used [35, 36, 37, 34]. RNNs suffer from the gradient vanishing problem [38], and LSTM and GRU are variants of traditional RNN, which is proposed for solving the problem of RNN. As GRU does not contain many trainable parameters, accuracy and computation speed of GRU are relatively lower and faster than those of LSTM, respectively [27]. As the network of LSTM-CNN is relatively complicated, the computation speed can be slower than that of LSTM and GRU [32, 33, 34, 35]. Therefore, because LSTM is a simple model to predict time-series fall data in terms of accuracy and computation speed, LSTM-based fall detection systems have been reported [26, 27, 28, 29, 30, 31]. Although rapid detection is required due to the nature of fall, several LSTM fall detection models [26, 27, 28, 29, 30, 31] have relatively slow fall detection. Moreover, several LSTM-based fall detection systems have not performed any normalization [27, 28, 29, 30, 31]. In addition, there are concerns about actual operation without any validation process [26, 27, 28, 29, 31], and thus there is a possibility that the training data will overfit.