## 2 Materials and Methods  
The model of 'Gait10 dof18 musc' [32], which is provided with the OpenSim distribution, was modified and used for the simulation. The marker set data, GRF, and torque data during walking (about 1.2 m/s walking speed), which are also provided with the model, were used as the tracking objective data [33]. The data from one heel strike (HS) to the next HS, when considered at the right leg, were taken as one gait cycle. The original model has 10 degrees of freedom (pelvic translation in x and y directions (2), pelvic tilt (1), flexion/extension in bilateral hip, knee, and ankle (6), and flexion/extension of lumbar (1)) without foot-ground contact model. To simulate resistance training in microgravity, the kinematic degrees of freedom of the model were modified to a total of six degrees of freedom (flexion/extension in bilateral hip, knee, and ankle) with the pelvis fixed to the ground. The upper body located above the pelvis was also removed so that only the lower body remained. The gravitational acceleration was set to zero. For other damping effects on the joints of the human body, we added dampers by referring to the model of the relevant study [13]. Actuators representing the assistive device were added to the model using the \"coordinate actuator\", \"point actuator\" and \"torque actuator\" functions. The functions apply forces or torques ideally to the joints of the human body model. It enables to exclude undesirable effects that occur in actual devices such as the power loss caused by joint axis misalignment between human and device. The purpose of the simulation is to allow the human model to track the state variables (generalized coordinate, generalized speed, muscle activation, and muscle fiber length) measured and predicted during 1 g normal walking with the assistance of an external actuator under microgravity conditions. Therefore, it is necessary to obtain the state variables from the marker set and GRF data in order to generate target tracking data. To do this, we performed inverse kinematics (IK), residual reduction algorithm (RRA), and CMC consecutively [27], and obtained the state variables (Figure 1a). The CMC uses static optimization to determine human muscle force at the given kinematic data. In slow motions such as walking, static optimization yields nearly the same results as using dynamic optimization, and computation is much faster [34]. Therefore, we used CMC to obtain target tracking data efficiently. Initial guess is also required to solve the optimization problem. Because the state and control variables described in (Figure 1a) are datasets obtained from 1 g normal walking data, they do not dynamically match the modified human body model for simulation in the microgravity situation. Therefore, using this data directly as an initial guess can adversely affect the convergence. A method that uses a solution of the system of defect functions, which were set as the constraints of the optimization problem, as an initial guess, can be exploited to improve convergence [13]. For this, we solved the defect functions by using the MATLAB 'fsolve' function that solves a system of non-linear equations and used the obtained solution as an initial guess for the optimization problem. The OpenSim API was used to link the human musculoskeletal model and corresponding governing equations (muscle contraction dynamics, musculoskeletal dynamics, and musculotendon dynamics [35]) provided by OpenSim with MATLAB, which has advantages in optimization and matrix calculation. The optimization design variables, states and controls, in the MATLAB optimization solver, can be made to satisfy the governing equations of the OpenSim model by exploiting the defect constraints (Figure 1b). In the process of solving the optimization problem using the direct collocation method, all the state and control variables are discretized by mesh interval, resulting in a large number of optimization design variables.