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Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue
Datao Xu1, Huiyu Zhou2, Wenjing Quan1
1Faculty of Sports Science, Ningbo University, Ningbo, 315211, China; Faculty of Engineering, University of Pannonia, Veszprém, 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely, 9700, Hungary.
Computer Methods and Programs in Biomedicine
|August 14, 2023
Summary
This study developed a deep learning model to predict anterior cruciate ligament (ACL) forces during single-leg landings. The model accurately assesses ACL injury risk by analyzing ankle motion, aiding sports training and injury prevention.
Area of Science:
- Biomechanics of sports injuries
- Kinetics and kinematics of human movement
- Machine learning applications in sports science
Background:
- Anterior cruciate ligament (ACL) injuries are common during landing, particularly after fatigue.
- Current methods struggle to accurately detect ACL loading, hindering effective injury prevention and monitoring.
- Ankle motion patterns may change after fatigue, potentially increasing ACL injury risk during single-leg landings (SL).
Purpose of the Study:
- To develop a highly accurate and easily implemented ACL force prediction model.
- To investigate the relationship between ankle motion patterns and ACL force during after-fatigue SL.
- To combine deep learning with biomechanical data for improved ACL injury risk assessment.
Main Methods:
- Collected before and after-fatigue SL data from 56 subjects.
- Explored relationships between ankle initial contact angle (AIC), ankle range of motion (AROM), and peak ACL force (PAF).
- Developed a musculoskeletal model to calculate ACL force and constructed a prediction model using sparrow search algorithm (SSA) optimized extreme learning machine (ELM) and long short-term memory (LSTM).
Main Results:
- A strong linear relationship was found between PAF and AIC (R = -0.70) and AROM (R² = -0.61).
- The SSA-ELM model demonstrated excellent prediction performance (R² = 0.9992, MSE = 0.0023, RMSE = 0.0474) using AIC and AROM.
- Combined SSA-ELM and SSA-LSTM models achieved excellent overall waveform ACL force prediction (R² = 0.9947, MSE = 0.0076, RMSE = 0.0873).
Conclusions:
- Increasing AIC and AROM during SL can enhance lower limb energy dissipation and reduce peak ACL force, thereby lowering injury risk.
- The proposed ACL dynamic load force prediction model offers high accuracy, excellent generalization, and requires minimal input variables (sagittal joint angles).
- This model can serve as an accurate ACL injury risk assessment tool for sports training and monitoring.
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