## 1 Introduction  
The functional movement screen (FMS) is a screening tool widely used by sports medicine practitioners to evaluate fundamental movement patterns in competitive athletes at risk of but not currently experiencing signs or symptoms of musculoskeletal injury. The FMS comprises seven tests that require both mobility and stability for successful completion, namely deep squat, hurdle step, in-line lumge, shoulder mobility, active straight leg raise, trunk stability pushup, and rotary stability. Each test is scored on a scale of 0-3 points according to quality of movement, with a maximum total composite score of 21 [1, 2, 3, 4]. When the total score is fewer than 14 points, injury risk increases by a factor of 2.74 (95% confidence interval 1.70-4.43) [2]. Similar to other fitness tests, the FMS must be manually administered and scored by professionally trained individuals [5], challenging its wide implementation in gyms, sports studios, and other settings. In the present study, inertial measurement unit (IMU) sensors were used to record the movement data of participants performing six FMS tests. Artificial intelligence (AI) was used to perform automatic movement screening. With advancements in science and technology, small, compact, portable, low-cost, multifunctional wearable sensors play increasingly important roles in the sports and medical fields. These sensors allow the acquisition of real-time movement data for analysis. IMUs are typically equipped with a three-axis gyroscope and accelerometer. Their applications include observing the movement patterns of athletes or the gait patterns of patientswith a stroke [6; 7]. IMU sensors have been used as movement indicators in studies of one-leg squat movements [8], weight training [9], running fatigue [10], walking status [11], skiing [12], and tennis [13], all of which indicated high correlations between IMU sensor signals and movement performance. However, in a 2014 study by [PERSON] et al., discrepancies were observed between automatic FMS scoring (achieved with 17 IMU full-body sensors) and manual scoring [14]. These differences may be attributable to the use of a self-set kinematics threshold for FMS scoring; whether this threshold is objective for each test remains questionable. Various kinematic parameters have been applied in other studies on FMS assessment, (e.g., [15]), including the excursion angle of the limb relative to any plane (in degrees), whether the limb is aligned or passes through a certain plane, and the displacement or minimum distance between various landmarks (in cm). To simplify system analysis, in the present study, the quality of physical movement was considered based on mobility and stability, the essential elements of the FMS. Mobility, defined as the \"ability of an individual to initiate, control, or sustain active movements of the body to perform motor tasks\", is essential for achieving sufficient range of motion (ROM). Stability, defined as the \"ability to actively control one's body within a limit of range\", refers to the degree of control over a movement [16]. Furthermore, to realize the objective of developing an automatic FMS system, no specific upper or lower ROM thresholds were set for either mobility or stability. The automated score results generated using AI were compared with manual scores for verification of classification accuracy. AI involves various modeling methods, each with its own advantages, disadvantages, and suitable application areas. The size of the training data set and the number and type of parameters all affect the selection of the most suitable model [17]. The establishment of a good classification model is crucial [18]. In machine learning, an ensemble model denotes a single predictive model composed of submodels. Ensemble models often perform better than any single classifier. Numerous ensemble learning methods, including boosting [19], bagging [20], and stacking [21] have been investigated.