## 1 Introduction  
Inertial sensors are useful for measuring and classifying motions because they are convenient to use during daily activities. They are also advantageous because they place few constraints on the environment and can be used over relatively long periods of time. In general, a commercial inertial sensor system used for monitoring motion in daily life includes software with its own embedded motion classification algorithm, and the system uses a sensor attached to a fixed location on the body of the subject to perform the monitoring (i.e., fall detection or daily life monitoring for older adults). The advantages of this approach include the relatively accurate classifications of a set of motions predefined in the system, and the classification results can be used as indicators to evaluate and compare the accuracy of each sensor [1]. However, the possible types of motion in daily life are highly varied and depending on a study's purpose, the motion to be detected may be diverse. Therefore, the algorithms of such software cannot be revised by the investigator to restrict the classification to a predefined set of motions. Moreover, the software is limited because of its inability to classify more varied types of motion. Recently, a wide range of studies has been conducted based on deep-learning models, including studies on motion classification referred to as human activity recognition (HAR) using inertial sensor data. Various models have been tried by many researchers, but the convolutional neural network (CNN), recurrent neural network (RNN) including long short-term memory (LSTM), and hybrid methods are representative of motion classification using inertial sensors depending on the data (Figure 1). In the study of [PERSON] et al. (2019), a CNN model was used to develop algorithms that can classify the motions of unrelated activities performed by the driver of a car with a free hand while driving [2]. For five motions, from taking a phone call to text messaging and eating food, the reported accuracy was 87%. In the study of [PERSON] et al. (2017), an RNN model was used to develop algorithms that can classify hand movements related to six motions including door opening and door closing, and the reported accuracy was 80.09% in the absence of noise and 74.92% in the presence of noise [3]. [PERSON] et al. (2017) developed algorithms that can classify the steps in human gait as five motions, from the loading response to the swing phase [4]. The LSTM neural network was used, and its mean classification accuracy was shown to be 92%. As such, various attempts have been to develop a suitable neural network for effective data classification, with reported accuracies in the range of 80-90%. The inertial sensor data used for such motion classification is mainly utilized in the form of image-based post-processed data and time series raw data. In the case of image-based data, CNN models are mainly used, and for time series raw data, CNN or LSTM is used according to the judgment of the researcher. A hybrid model using both of these models is also used depending on the analysis data and the purpose [5]. Since the CNN model operates based on image classification, it requires high computational resources according to feature extraction through relatively numerous filters and mappings [6; 7]. On the other hand, the LSTM model is highly competent for classification using raw sequences of time series signals [8]. In addition, LSTM is a model that solves the vanishing gradient problem in which the RNN does not remember the preceding value as the number of input increases, and it shows excellent performance when modeling long-term data such as time series data [9]. In the case of the hybrid model, there have been reports of improved accuracy compared to the single model (i.e., CNN or LSTM) depending on the conditions, but since the CNN layer is included, more training time and computational resources are required [10].