## 1 Introduction  
Recently, Wi-Fi fingerprinting has been used to build indoor positioning systems [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. In this approach, a dataset from a Wi-Fi fingerprinting system uses received signal strength indication (RSSI) information from access points (APs) collected at a specific location point as data to determine location. We calculate the location by comparing the surrounding RSSI values of the location determination point with the previously collected fingerprinting data as input data. However, the RSSI value of a Wi-Fi AP contains irregularities in the noise caused by obstacles, which can lead to erroneous results. Therefore, many studies are conducted to improve the location accuracy [12, 13, 14]. Location determination methods are studied for Wi-Fi signals collected by the built-in sensors of smart devices [15]. But the approaches in many studies suffer from the disadvantages of requiring assistive devices and complex configuration [16]. Therefore, another approach to improve location accuracy is to use RSSI along with the mobile terminal's movement path [17; 18; 19]. For trajectory estimation along the path of movement, approaches have been proposed that either use dead reckoning trajectories generated from a mounted inertial measurement unit (IMU) or fuse RSSI from Wi-Fi signals and position estimation from a global positioning system (GPS) [18; 20]. There is also a study that presents a Wi-Fi RSSI dataset that includes sequentially collected trajectories [19]. Although datasets with trajectories can enhance location accuracy, their practical application in real world environments is challenging due to the substantial additional costs associated with dataset preparation and processing. Additionally, these datasets are limited by the fact that they solely represent the paths travelled during the collection process. In datasets where trajectories are not available (e.g., collected Wi-Fi fingerprints), it is difficult to estimate the mobile device's path of movement. It is possible to create a movement path by using a Recurrent Neural Network (RNN) model that can learn time-series information from a machine learning algorithm [12; 13]. RNNs extract features from high-dimensional time-series input data and can be applied to classification and regression problems. RNN models are supervised learning algorithms that can take into account the continuity of the data [21; 22; 23]. However, RNNs do not have long-term memory and suffer from the vanishing gradient problem. To solve this problem, variants models such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are used [24; 25; 26; 27]. These networks can take into account the continuous movement of the terminal in the indoor localization to more accurately determine its current position or predict its next movement. The use of LSTMs in indoor positioning systems requires sequential RSSI inputs along the terminal's path of movement. However, the datasets provided for most indoor positioning studies do not contain sequential path information. In this paper, we propose an enhanced Wi-Fi fingerprinting-based method for generating movement path data, building upon previous research. Visualize a Wi-Fi fingerprint dataset and use BB and a grid cell to create clusters that separate indoor location areas. A grid cell is a cluster formed from a dataset. This adjacency was determined by calculating the state transition probability for the BBs within the grid cell, which was then used to generate path data. In the process, we improved upon the problems with parameterization for thresholding in our previous paper. Instead of solely relying on Wi-Fi fingerprinting information, the proposed machine learning model utilizes the movement path data generated through the proposed method. This allows the RSSI of the previous location to influence the positioning of the current location.