###### Abstract  
The simulation of fall plays a critical role in estimating injuries caused by fall. However, implementing human fall mitigation motions on a simulator proves challenging due to the complexity and variability of fall movement. Our simulator estimates fall motion by extrapolating the motion observed in fall experiments. By incorporating actual fall motion data for the upper limbs, we enhanced the realism of the fall simulation. The application of forward dynamics control to the lower limbs allowed for the adjustment of mitigation motions, taking into account individual physical capabilities. In this study, fall simulations were conducted under the constraints of maximum joint torque and maximum torque change rate, emulating the physical capabilities of both the elderly and young adults. Our results successfully demonstrated the mitigation motion facilitated by the stance leg reduced the descent velocity of the center of mass by 0.75 m/s for elderly individuals and by 1.25 m/s for young adults, compared to a zero torque condition. This indicates that our study introduced a novel method for quantifying the impact of the lower limbs' physical capabilities on fall velocity. Such a method represents a significant advancement in understanding how mitigation motions can influence fall injury simulations. fall simulation; mitigation motion; physical capability +
Footnote †: journal: Medical  
reaction force due to variations in hand placement were measured when falling from midway in a falling posture [9; 10]. Furthermore, it was also observed that the speed and force of the mitigation motion that uses arms were affected by aging [11], which suggests that the effectiveness of mitigation motion differs among individuals. Additionally, we conducted a tripping experiment under conditions that restricted the recovery step, making the fall inevitable, to evaluate the relationship between the length of the recovery step and the descent velocity of the center of mass (CoM) [12]. In simulations, humans were modeled as rigid body links connected with rotational joints. Multibody simulations can estimate the impact of a fall by sequentially simulating the progression of fall movements [13]. These simulations can estimate ground reaction force under various fall conditions. However, some simulation conditions, such as locking or zero-torque of joint angles, differ from the actual human fall conditions [6; 14]. A study estimated changes in falling speed and time duration by accounting for differences in walking speed as initial conditions [15]. Nevertheless, some simulators estimate movements exclusively in the sagittal plane, which is more suitable for simulating symmetric motion. Moreover, simulation studies commonly encounter the issue that base fall movements are either artificial or not based on the actual fall motion. Therefore, the validity of simulated fall behaviors is uncertain. Fall avoidance has been also researched in the field of robotics. Especially the control strategy of biped robots is useful for the stability analysis of human gait [16]. However, it is not reasonable to apply the control strategy of the humanoid robot to actual humans because the motion strategy of humans is not identified sufficiently. The reaction strategy of humans was investigated in experiments [3; 17]. Considering the uncertainty and variation of human motion, simple reaction patterns such as arm stretch and leg brace are more applicable for human fall simulation. To accurately assess the impact of varying fall mitigation strategies on resultant injuries, it is essential to model fall movements based on specific mitigation techniques such as arming and bracing. Furthermore, the realism of motion estimation improves as the duration of the motion simulation is reduced due to a decrease in the variability introduced by the uncertainty of the simulation algorithm. Therefore, it is advantageous to focus the extrapolation primarily on the terminal phase of the fall movement.