Competitive skiers encounter a high risk of sustaining an ACL injury during jump‐landing in downhill ski racing. Facing an injury‐prone landing manoeuvre, there is a lack of knowledge regarding optimum control strategies. So, the purpose of the present study was to investigate possible neuromuscular control patterns to avoid injury during injury‐prone jump‐landing manoeuvres. A computational approach was used to generate a series of 190 injury‐prone jump‐landing manoeuvres based on a 25‐degree‐of‐freedom sagittal plane musculoskeletal skier model. Using a dynamic optimization framework, each injury‐prone landing manoeuvre was resolved to identify muscle activation patterns of the lower limbs and corresponding kinematic changes that reduce peak ACL force. In the 190 injury‐prone jump‐landing simulations, ACL forces peaked during the first 50 ms after ground contact. Optimized muscle activation patterns, that reduced peak ACL forces, showed increased activation of the monoarticular hip flexors, ankle dorsi‐ and plantar flexors as well as hamstrings prior to or during the early impact phase (<50 ms). The corresponding kinematic changes were characterized by increased hip and knee flexion and less backward lean of the skier at initial ground contact and the following impact phase. Injury prevention strategies should focus on increased activation of the monoarticular hip flexors, ankle plantar flexors and rapid and increased activation of the hamstrings in combination with a flexed landing position and decreased backward lean to reduce ACL injury risk during the early impact phase (<50 ms) of jump landing.Highlights First study investigating advantageous control strategies during injury‐prone jump‐landing manoeuvres in downhill skiing using a musculoskeletal simulation model and dynamic optimization framework. The simulation results predicted high injury risk during the first 50 ms after initial ground contact. Optimized neuromuscular control patterns showed adapted activation patterns (timing and amplitude) of muscles crossing the knee as well as the hip and ankle joints prior to and after initial ground contact, respectively. An optimized control strategy during an injury‐prone landing manoeuvre was characterized kinematically by increasing hip and knee flexion and less backward lean of the skier at initial ground contact and the following impact phase.

In alpine skiing, estimation of the joint moments acting onto the skier is essential to quantify the loading of the skier during turning maneuvers. In the present study, a novel forward dynamics optimization framework is presented to estimate the joint moments acting onto the skier incorporating a three dimensional musculoskeletal model (53 kinematic degrees of freedom, 94 muscles). Kinematic data of a professional skier performing a turning maneuver were captured and used as input data to the optimization framework. In the optimization framework, the musculoskeletal model of the skier was applied to track the experimental data of a skier and to estimate the underlying joint moments of the skier at the hip, knee and ankle joints of the outside and inside leg as well as the lumbar joint. During the turning maneuver the speed of the skier was about 14 m/s with a minimum turn radius of about 16 m. The highest joint moments were observed at the lumbar joint with a maximum of 1.88 Nm/kg for lumbar extension. At the outside leg, the highest joint moments corresponded to the hip extension moment with 1.27 Nm/kg, the knee extension moment with 1.02 Nm/kg and the ankle plantarflexion moment with 0.85 Nm/kg. Compared to the classical inverse dynamics analysis, the present framework has four major advantages. First, using a forward dynamic optimization framework the underlying kinematics of the skier as well as the corresponding ground reaction forces are dynamically consistent. Second, the present framework can cope with incomplete data (i.e., without ground reaction force data). Third, the computation of the joint moments is less sensitive to errors in the measurement data. Fourth, the computed joint moments are constrained to stay within the physiological limits defined by the musculoskeletal model.

In alpine skiing, estimation of the muscle forces and joint loads such as the forces in the ACL of the knee are essential to quantify the loading pattern of the skier during turning maneuvers. Since direct measurement of these forces is generally not feasible, non-invasive methods based on musculoskeletal modeling should be considered. In alpine skiing, however, muscle forces and ACL forces have not been analyzed during turning maneuvers due to the lack of three dimensional musculoskeletal models. In the present study, a three dimensional musculoskeletal skier model was successfully applied to track experimental data of a professional skier. During the turning maneuver, the primary activated muscles groups of the outside leg, bearing the highest loads, were the gluteus maximus, vastus lateralis as well as the medial and lateral hamstrings. The main function of these muscles was to generate the required hip extension and knee extension moments. The gluteus maximus was also the main contributor to the hip abduction moment when the hip was highly flexed. Furthermore, the lateral hamstrings and gluteus maximus contributed to the hip external rotation moment in addition to the quadratus femoris. Peak ACL forces reached 211 N on the outside leg with the main contribution in the frontal plane due to an external knee abduction moment. Sagittal plane contributions were low due to consistently high knee flexion (> 60 $$^{\circ }$$∘ ), substantial co-activation of the hamstrings and the ground reaction force pushing the anteriorly inclined tibia backwards with respect to the femur. In conclusion, the present musculoskeletal simulation model provides a detailed insight into the loading of a skier during turning maneuvers that might be used to analyze appropriate training loads or injury risk factors such as the speed or turn radius of the skier, changes of the equipment or neuromuscular control parameters.