Introduction:​ Falls, particularly forward falls on outstretched arms, are a leading cause of injury, especially among older adults. The primary focus of this research was to develop a computational musculoskeletal model of the upper extremity to simulate fall dynamics and evaluate how varying joint stiffness levels influence the impact forces during a fall. The study aimed to better understand how joint stiffness, pre-impact posture, and fall height affect injury outcomes, with the goal of improving fall prevention strategies and rehabilitation techniques.

Methods:​ A forward dynamic simulation approach was utilized, where a subject-specific musculoskeletal model was developed in OpenSim. The model incorporated the shoulder and elbow joints, with stiffness values systematically varied across multiple degrees of freedom (DOFs). Existing experimental data from the Kawalilak study provided the baseline for scaling and validating the model. Simulations were conducted for three different fall heights, and contact parameters were optimized to match experimental peak forces. A factorial analysis explored the effects of joint stiffness and fall height on impact forces.

Results:​ The results indicated that elbow stiffness was the most influential factor in determining peak impact forces during a forward fall, followed by shoulder rotation stiffness. Models with more flexed elbow configurations demonstrated higher impact force variation in ranges compared to those with extended elbows. The regression analysis revealed that joint stiffness and fall height were key predictors of the simulated forces. Additionally, non-responder models, characterized by extended elbows, exhibited minimal sensitivity to changes in stiffness.

Discussion:​ The findings highlight the significant role of joint stiffness, particularly in the elbow, in reducing impact forces during fall arrest. The results suggest that increasing joint stiffness through targeted interventions like strength training, or by utilizing assistive falling devices could enhance fall mitigation, especially for older adults at higher risk of injury. The developed model provides a robust framework for simulating fall dynamics and can inform future fall prevention strategies and rehabilitation programs.