Estimation of forces on anterior cruciate ligament in dynamic activities

Contribution of ankle motion pattern during landing to reduce the knee-related injury risk

BACKGROUND

Single-leg landing (SL) is an essential technique in sports such as basketball, soccer, and volleyball, which is often associated with a high risk of knee-related injury. The ankle motion pattern plays a crucial role in absorbing the load shocks during SL, but the effect on the knee joint is not yet clear. This work aims to explore the effects of different ankle plantarflexion angles during SL on the risk of knee-related injury.

METHODS

Thirty healthy male subjects were recruited to perform SL biomechanics tests, and one standard subject was selected to develop the finite element model of foot-ankle-knee integration. The joint impact force was used to evaluate the impact loads on the knee at various landing angles. The internal load forces (musculoskeletal modeling) and stress (finite element analysis) around the knee joint were simulated and calculated to evaluate the risk of knee-related injury during SL. To more realistically revert and simulate the anterior cruciate ligament (ACL) injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain.

RESULTS

As the ankle plantarflexion angle increased during landing, both the peak knee vertical impact force (p = 0.001) and ACL force (p = 0.001) decreased significantly. The maximum von Mises stress of ACL, meniscus, and femoral cartilage decreased as the ankle plantarflexion angle increased. The overall range of variation in ACL stress was small and was mainly distributed in the femoral and tibial attachment regions, as well as in the mid-lateral region.

CONCLUSION

The current findings revealed that the use of larger ankle plantarflexion angles during landing may be an effective solution to reduce knee impact load and the risk of rupture of the medial femoral attachment area in the ACL. The findings of this study have the potential to offer novel perspectives in the optimized application of landing strategies, thus giving crucial theoretical backing for decreasing the risk of knee-related injury.

Effect of changes in motor skill induced by educational video program to decrease lower-limb joint load during cutting maneuvers: based on musculoskeletal modeling

BACKGROUND

This study investigated the effects of changes in motor skills from an educational video program on the kinematic and kinetic variables of the lower extremity joints and knee ligament load.

METHODS

Twenty male participants (age: 22.2 ± 2.60 y; height: 1.70 ± 6.2 m; weight: 65.4 ± 7.01 kg; BMI: 23.32 ± 2.49 [Formula: see text]) were instructed to run at 4.5 ± 0.2 m/s from a 5 m distance posterior to the force plate, land their foot on the force plate, and perform the cutting maneuver on the left. The educational video program for cutting maneuvers consisted of preparatory posture, foot landing orientation, gaze and trunk directions, soft landing, and eversion angle. The measured variables were the angle, angular velocity of lower extremity joints, ground reaction force (GRF), moment, and anterior cruciate ligament (ACL) and medial collateral ligament (MCL) forces through musculoskeletal modeling.

RESULTS

After the video feedback, the hip joint angles increased in flexion, abduction, and external rotation (p < 0.05), and the angular velocity increased in extension (p < 0.05). The ankle joint angles increased in dorsiflexion (p < 0.05), and the angular velocity decreased in dorsiflexion (p < 0.05) but increased in abduction (p < 0.05). The GRF increased in the anterior-posterior and medial-lateral directions and decreased vertically (p < 0.05). The hip joint moments decreased in extension and external rotation (p < 0.05) but increased in adduction (p < 0.05). The knee joint moments were decreased in extension, adduction, and external rotation (p < 0.05). The abduction moment of the ankle joint decreased (p < 0.001). There were differences in the support zone corresponding to 64‒87% of the hip frontal moment (p < 0.001) and 32‒100% of the hip horizontal moment (p < 0.001) and differences corresponding to 32‒100% of the knee frontal moment and 21‒100% of the knee horizontal moment (p < 0.001). The GRF varied in the support zone at 44‒95% in the medial-lateral direction and at 17‒43% and 73‒100% in the vertical direction (p < 0.001).

CONCLUSIONS

Injury prevention feedback reduced the load on the lower extremity joints during cutting maneuvers, which reduced the knee ligament load, mainly on the MCL.

New Insights Optimize Landing Strategies to Reduce Lower Limb Injury Risk

Single-leg landing (SL) is often associated with a high injury risk, especially anterior cruciate ligament (ACL) injuries and lateral ankle sprain. This work investigates the relationship between ankle motion patterns (ankle initial contact angle [AICA] and ankle range of motion [AROM]) and the lower limb injury risk during SL, and proposes an optimized landing strategy that can reduce the injury risk. To more realistically revert and simulate the ACL injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain. Sixty healthy male subjects were recruited to collect biomechanics data during SL. The correlation analysis was conducted to explore the relationship between AICA, AROM, and peak vertical ground reaction force (PVGRF), joint total energy dissipation (TED), peak ankle knee hip sagittal moment, peak ankle inversion angle (PAIA), and peak ACL force (PAF). AICA exhibits a negative correlation with PVGRF (r = -0.591) and PAF (r = -0.554), and a positive correlation with TED (r = 0.490) and PAIA (r = 0.502). AROM exhibits a positive correlation with TED (r = 0.687) and PAIA (r = 0.600). The results suggested that the appropriate increases in AICA (30° to 40°) and AROM (50° to 70°) may reduce the lower limb injury risk. This study has the potential to offer novel perspectives on the optimized application of landing strategies, thus giving the crucial theoretical basis for decreasing injury risk.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

BACKGROUND AND OBJECTIVE

For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training.

METHODS

This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern.

RESULTS

Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent.

CONCLUSION

The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue

BACKGROUND AND OBJECTIVE

As a fundamental exercise technique, landing can commonly be associated with anterior cruciate ligament (ACL) injury, especially during after-fatigue single-leg landing (SL). Presently, the inability to accurately detect ACL loading makes it difficult to recognize the risk degree of ACL injury, which reduces the effectiveness of injury prevention and sports monitoring. Increased risk of ACL injury during after-fatigue SL may be related to changes in ankle motion patterns. Therefore, this study aims to develop a highly accurate and easily implemented ACL force prediction model by combining deep learning and the explored relationship between ACL force and ankle motion pattern.

METHODS

First, 56 subjects' during before and after-fatigue SL data were collected to explore the relationship between the ankle initial contact angle (AIC), ankle range of motion (AROM) and peak ACL force (PAF). Then, the musculoskeletal model was developed to simulate and calculate the ACL force. Finally, the ACL force prediction model was constructed by combining the explored relationship and sparrow search algorithm (SSA) to optimize the extreme learning machine (ELM) and long short-term memory (LSTM).

RESULTS

There was almost a stronger linear relationship between the PAF and AIC (R = -0.70), AROM (R2 = -0.61). By substituting AIC and AROM as independent variables in the SSA-ELM prediction model, the model shows excellent prediction performance because of very strong correlation (R2 = 0.9992,  MSE = 0.0023,  RMSE = 0.0474). Based on the equal scaling by combining results of SSA-ELM and SSA-LSTM, the prediction model achieves excellent performance in ACL force prediction of the overall waveform (R2 = 0.9947,  MSE = 0.0076,  RMSE = 0.0873).

CONCLUSION

By increasing the AIC and AROM during SL, the lower limb joint energy dissipation can be increased and the PAF reduced, thus reducing the impact loads on the lower limb joints and reducing ACL injuries. The proposed ACL dynamic load force prediction model has low input variable demands (sagittal joint angles), excellent generalization capabilities and superior performance in terms of high accuracy. In the future, we plan to use it as an accurate ACL injury risk assessment tool to promote and apply it to a wider range of sports training and injury monitoring.

An open-source OpenSim® ankle-foot musculoskeletal model for assessment of strains and forces in dense connective tissues

BACKGROUND

The ankle and foot are among the most critical load-bearing joints in the human anatomy. Anatomically accurate human body models are imperative to understanding the mechanics of injury and musculoskeletal disorders. A typical human ankle-foot anatomy consists of 25 DOFs, 112 dense connective tissues (DCTs) (92 ligaments, one capsule and 19 fasciae), 30 tendons, and 65 muscles. Existing models possess less than half of the DOFs and physiological elements. In this work, we have developed an ankle-foot joint complex musculoskeletal model for the OpenSim® platform by incorporating 24 degrees of freedom (DOF) comprising of 66 DCTs (46 ligaments, one 1 capsule and 19 fasciae), 30 tendons, and 65 muscles.

METHODS

Computed tomography (CT) data of human ankle joint-foot complex was segmented using Mimics ® (Version 17.0, Materialise, Belgium) to obtain models of the cartilages and bones of the ankle joint-foot complex. The position and resting lengths of the DCTs were attained from the MRI data and literature. Five joints, namely, tibiotalar, subtalar, chopart, tarsometatarsal (TMT), and metatarsophalangeal (MTP) joints and their joint axes were formulated to yield 24 DOFs. A forward simulation was carried out at each joint of the ankle-foot complex within their respective range of motions. The strains, instantaneous strain rates, and forces developed in the ligaments during the simulation were studied.

RESULTS

During plantar-dorsiflexion of the tibiotalar joint, the anterior tibio-talar ligament (aTTL) yielded the maximum strain compared to all other ligaments. Anterior tibio-fibular ligament (aTFL) experienced extreme strain during subtalar inversion. Hence, the coupled kinematics of subtalar inversion and plantar flexion are failure-prone activities for aTFL. The chopart, TMT, and MTP joints yielded maximum strains or forces for several bundles at the extremes of the range of motion. This signifies that rotations of these joints to their extreme range of motion are prone to failure for the bundles attached to the joint complex.

CONCLUSION

The results illustrate the potential application of the proposed OpenSim® ankle-foot model in understanding the ligament injury mechanism during sports activity and its prevention. Researchers can use the proposed model or customise it to study complex kinematics, understanding injury mechanisms, testing fixtures, orthosis or prosthesis, and many more in the domain of musculoskeletal research.