## 1 Introduction  
The prevalence of knee osteoarthritis (OA) is on the rise as modern society ages. According to the HIRA Big Data Open Portal, the prevalence of knee osteoarthritis among patients aged 50 years and older is continuously increasing [1]. Osteoarthritis of the knee is a chronic disease that continues to be costly to treat and manage, and various studies are being conducted to reduce this cost. Among these studies, the Knee Adduction Moment (KAM) is widely used as an important indicator of knee osteoarthritis development. KAM is a mathematical model that calculates the load on the medial knee joint during walking, and the lower the KAM, the slower the progression of knee osteoarthritis and the less severe the symptoms [2]. KAM is measured in Newton-meters (N - m), which represents the rotational force applied at a distance from the knee joint. One N - m is defined as the torque produced by a one Newton force applied at a one meter distance. Therefore, accurately and cost-effectively measuring KAM is an important challenge in the management of knee osteoarthritis. Recently, there have been several studies that utilize wearable sensors and machine learning algorithms to predict KAM values [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. In this work [3], Inertial Measurement Unit (IMU) sensors and machine learning algorithms were used to measure KAM in everyday walking and to try reduce its cost. [PERSON] proposed a KAM prediction solution for three different gait patterns using a fully connected artificial neural network (FCNN) and extreme gradient boosting (XGBOCST) [20], and the model was built by training all of the data together. However, this study mainly focused on mild and moderate OA patients, and there were limited data on severe OA patients. This limits the accuracy of KAM prediction for OA patients and may limit the prediction for patients with a wider range of KAM values due to the application of fewer gait patterns. The following work [4] combined wearable sensors and machine learning techniques to develop an assistive device for OA patients. The study aimed to develop an artificial neural network (ANN) to estimate the Knee Flexion Moment (KFM) and KAM during different walking tasks using two wearable sensors. IMU signals were collected from seven participants performing six different gait tasks, which were used to train an ANN to estimate the KFM and KAM time series. However, while the KFM estimation showed a high accuracy, the KAM estimation showed a relatively low accuracy. This highlights the limitations of using only two IMUs to consistently estimate KAM. Other studies have explored various machine learning methods to enhance KAM prediction accuracy. For example, [PERSON] et al. [5] developed a diagnostic model associated with OA severity using the random forest regression method, while [PERSON] et al. [6] used 3D fully connected neural networks to predict KAM based on anatomical landmarks extracted from 2D video analysis. [PERSON] et al. [7] focused on correlating knee contact force with cartilage loss over time using random forest regression models, and [PERSON] et al. [8] employed a deep learning algorithm using convolutional layers to estimate KAM from a single IMU sensor. Additionally, [PERSON] et al. [13] combined deep learning models, including feed-forward, convolutional, and recurrent neural networks, with instrumented insoles for real-time KAM prediction, while [PERSON] et al. [16] applied a feed forward network to predict joint forces based on motion capture data. Each study demonstrates unique approaches, but they often lack the ability to account for the full diversity of gait patterns in daily life, which is a significant factor in enhancing the robustness and accuracy of gait analysis systems. The problem with existing studies is that they do not fully reflect the variety of walking patterns, and they often suffer from an insufficient number of IMU sensors, limiting the ability to capture comprehensive gait features.