## 1 Introduction  
More than 40% of workers suffer from work-related musculoskeletal disorders (WMSD) annually due to manual materials handling (MMH) tasks despite efforts to improve the work environment [1]. It has been reported that 52% of WMSDs are caused by lifting tasks, of which 62% are low-back musculoskeletal disorders [2]. The high mechanical load applied to the lumbar spine during lifting is considered a major risk factor for back pain. Studies have been conducted to measure the risk factor by calculating low-back loading [3]. Internal moments to a body segment are caused by a combined action of muscle strength within the joint or tensile force of the skin, joint capsule, and ligaments [4]. By calculating external joint moments, internal moments acting within each joint can be estimated. The L5/S1 moment tends to increase according to the weight of the object. Repetitive moment is one of the risk factors that can increase the incidence of low-back musculoskeletal disorders [5]. The lower extremity and L5/S1 moment are also used as evaluation parameters for lifting posture [6]. Therefore, the moment is being studied as a major parameter to determine the risk of musculoskeletal disorders during lifting [3; 6; 7; 8]. Optical 3D motion tracking system (OTS) is a method of calculating the lower extremity and L5/S1 moments [9]. The OTS acquires optical marker trajectory and ground reaction force based on optical camera, force plate, and the subject's anthropometric information. It then calculates the moment of each joint using the acquired optical marker trajectory, ground reaction force, and inverse dynamic method [10]. Calculating moments using an OTS is considered as the golden standard method. Many studies have been conducted to measure the lower extremity and L5/S1 moment during symmetric and asymmetric lifting [1; 6; 8; 11; 12]. However, the OTS has a disadvantage in that it is impossible to be utilized in a working environment due to spatial constraints [13; 14]. To overcome a spatial limitation, methods using wearable sensors have been reported [15]. Wearable sensors such as inertial measurement unit (IMU), insole sensor, or portable force plate can be used to calculate joint moments [16; 17]. In the case of an IMU sensor, the center of joint can be obtained by attaching it onto the body segment, with an insole sensor or portable force plate that can be worn on shoes to obtain ground reaction force data [18]. Previous studies suggest L5/S1 moment measurement system using 12-17 IMU sensors and 2 force shoes or insole sensors [18; 19]. According to recent studies mentioned above, studies using human body modeling [16; 17; 18] based on wearable sensor data have shown the possibility of overcoming limitations of OTS using multiple wearable sensors. However, using multiple sensors showed a complicated application method, inducing unnatural movement and taking a longer time for detachment in a real working environment. In addition, a robust estimation of movement is difficult due to substantial movement variability and irregular data [20]. To overcome these limitations mentioned above, previous studies have been conducted to predict biomechanical parameters such as ground reaction force, joint torque, and joint moment by replacing it with artificial intelligence techniques [21; 22]. [PERSON] et al. (2020) have conducted a study to estimate the angle and moment of lower extremity joints during gait using IMU sensors and machine learning techniques. Their research suggested a predictive model using long short-term memory (LSTM). The predicted moment and joint angle showed a normalized root mean square error (RMSE) of about 10% [23]. Additionally, [PERSON] et al. (2019) have developed an LSTM model that predicts the inclination angle between the center of gravity and the center of pressure when walking using time-series data obtained from a single IMU sensor. Compared to an artificial neural network (ANN), the relative RMSE result of the LSTM model showed 6% reduction, indicating improved accuracy [24].