Eleven physically active male participants were recruited to conduct landing tasks(the sample size was set according to experience). None of the participants had a history of lower limb injuries at least six months before the study. All participants (age: 23.2 ± 2.5 years; height: 1.78 ± 0.34 m; mass: 74.2 ± 6.1 kg) were physically active in line with the international standard of physical activity guideline, which refers physically active lifestyle as exercising for at least 150 minutes with moderate-intensity or 75 minutes with vigorous-intensity every week in the past three months . Informed consent was provided, and the Biological and Medical Ethics Committee of the Dalian University of Technology approved this study.
In this study, we used the elastic knee brace of Ossur (~ 160g net weight), and it was authenticated by European Union certification and International Organization for Standardization certification. Two multifunctional boxes of heights 30cm and 45cm were employed. For motion analysis, an 8-camera motion capture system with a frequency of 100 Hz (Vicon Peak, Oxford Metrics Ltd, UK) was used to record three dimensions (3D) marker trajectories. Thirty-nine retro-reflective markers (25mm diameter) were attached to each person at the corresponding anatomical landmarks based on the Gait2392 model . In addition, a force platform (AMTI, Advanced Mechanical Technology, Inc., Watertown, USA) was utilized synchronously to record ground reaction force (GRF) at a frequency of 1000 Hz. Kinematics and kinetic data were filtered by a zero phase-lag, fourth-order Butterworth filter with a cut-off frequency of 6 Hz.
The Duration of a single-leg landing was defined as the interval starting from initial foot contact to maximum knee flexion . Before the test, participants were told to conduct a 5 min warm-up (jogging) when arriving at the sports science lab. Then, details about the single-leg landing were explained to make them understand the form of the maneuver. Participants performed single-leg landing three times for each test condition using their dominant legs, defined as the limb that people prefer to kick a ball . All participants used their right lower limbs to perform the task in this study. Experiments proceeded in four categories with two factors (height and knee brace). One participant completed one trial, and a 30-second break was given to avoid fatigue. A successful test was accepted if the participant stepped off the box in a stable landing posture without an upward and forward jumping motion . Injuries are more likely to occur at peak GRF and maximum knee flexion during landings[18, 20].
OpenSim 4.0, an open-source musculoskeletal simulation software  was run to obtain kinematics, kinetics, and muscle forces. Single-leg landing simulations were generated in OpenSim (Figure. 1). First, C3D files obtained from Vicon were transferred by MATLAB (The Mathworks Inc., Natick, MA) and were imported into the OpenSim. A musculoskeletal model named Gait2392 was established in the platform, which possesses eight segments(torso, pelvis, femurs, shanks, feet), 23 degrees of freedom and 92 musculoskeletal muscle, mainly in the lower limbs. A generic model of each participant was obtained through a running scale according to anthropological data captured in the lab [22–24]. Specifically, the dimension of each segment was scaled according to corresponding markers obtained from the static experiment captured in the lab, and their RMS error about the position of markers was restricted under 3cm. Then inverse kinematics was run to minimize the differences between experimental and virtual mark position through the least-squares method, and its RMS mark errors were limited to under 3.5cm . Afterward, joint angles were obtained, a primary outcome measure for conducting the following steps. Inverse dynamics was run to obtain net joint torque using results from the last step (i.e., inverse kinematics) and GRF to solve a series of dynamic equations. In addition, a residual reduction algorithm was adopted to reduce residual force (applied to the pelvis) caused by the inconsistency between the force platform and kinematics data in the musculoskeletal model. Finally, static optimization predicted muscle forces (using the RRA results) with the objective function being the minimizes the sum of squared muscle activations .
The gluteus maximus, gluteus medius, gluteus minimus, rectus femoris, medial femoris, medial femoris, lateral gastrocnemius, lateral gastrocnemius, and soleus muscle were examined. The experimental data in the outcomes of this study were expressed as mean ± standard deviation (Means ± S.D). The muscle forces data was divided by each participant's body mass via Excel 2018, and two characteristic moments were selected based on previous literature reports that could both cause ACL injuries: the moment of peak GRF and the moment of maximum knee flexion. The retrieved data were stored and counted using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) software. A two-way repeated measures ANOVA was utilized to assess if knee braces alter muscle force during single-leg landings at two heights through musculoskeletal simulation. With a significance level of 0.05. Final graphics were drawn using Prism 8.0.