Datao Xu
57215841612
Publications - 9
Data-driven deep learning for predicting ligament fatigue failure risk mechanisms
Publication Name: International Journal of Mechanical Sciences
Publication Date: 2025-09-01
Volume: 301
Issue: Unknown
Page Range: Unknown
Description:
The pathogenesis of musculoskeletal disorders is closely associated with the cumulative damage and fatigue failure behavior of fibrous connective tissues under long-term repetitive loading. However, significant technological challenges remain in real-time dynamic monitoring of ligament fatigue life, particularly the lack of efficient computational mechanics modeling frameworks and precise assessment tools adaptable to real-world movement scenarios. The multimodal integrated framework for ligament fatigue life assessment was proposed in this study. First, the high-accuracy subject-specific musculoskeletal models were developed based on individualized medical imaging data. A coupled hyperelastic-viscoelastic constitutive model was incorporated to accurately characterize the nonlinear mechanical behavior of ligamentous tissues and their fatigue damage evolution under cyclic loading. Furthermore, by integrating continuum damage mechanics theory, a time-dependent cumulative damage evolution equation was established to systematically quantify the coupling relationship between fatigue failure probability and dynamic mechanical loading. In the data-driven prediction module, an innovative deep-learning model that integrates kinematic-dynamic coupling was developed. By integrating wearable inertial measurement units, the model enables real-time inversion of ligament loading force-fatigue failure states and prediction of fatigue life. This approach effectively overcomes the limitations of traditional mechanical modeling in long-term, multi-scenario dynamic monitoring, achieving high-precision and minimally invasive fatigue life evaluation of ligaments. The proposed computational framework breaks the static-loading constraints of conventional fatigue testing, achieving the dynamic biomechanical analysis and fatigue life prediction under real movement conditions. This work not only provides novel theoretical insights into the mechanisms and modeling of ligament fatigue damage, but also provides a generalizable tool for biomechanical injury prevention, rehabilitation planning, and soft tissue fatigue analysis in the musculoskeletal system.
Open Access: Yes
Contribution of ankle motion pattern during landing to reduce the knee-related injury risk
Publication Name: Computers in Biology and Medicine
Publication Date: 2024-09-01
Volume: 180
Issue: Unknown
Page Range: Unknown
Description:
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.
Open Access: Yes
Adaptive Adjustments in Lower Limb Muscle Coordination during Single-Leg Landing Tasks in Latin Dancers
Publication Name: Biomimetics
Publication Date: 2024-08-01
Volume: 9
Issue: 8
Page Range: Unknown
Description:
Previous research has primarily focused on evaluating the activity of individual muscles in dancers, often neglecting their synergistic interactions. Investigating the differences in lower limb muscle synergy during landing between dancers and healthy controls will contribute to a comprehensive understanding of their neuromuscular control patterns. This study enrolled 22 Latin dancers and 22 healthy participants, who performed a task involving landing from a 30 cm high platform. The data were collected using Vicon systems, force plates, and electromyography (EMG). The processed EMG data were subjected to non-negative matrix factorization (NNMF) for decomposition, followed by classification using K-means clustering algorithm and Pearson correlation coefficients. Three synergies were extracted for both Latin dancers and healthy participants. Synergy 1 showed increased contributions from the tibialis anterior (p < 0.001) and medial gastrocnemius (p = 0.024) in Latin dancers compared to healthy participants. Synergy 3 highlighted significantly greater contributions from the vastus lateralis in healthy participants compared to Latin dancers (p = 0.039). This study demonstrates that Latin dancers exhibit muscle synergies similar to those observed in healthy controls, revealing specific adjustments in the tibialis anterior and medial gastrocnemius muscles among dancers. This research illustrates how dancers optimize control strategies during landing tasks, offering a novel perspective for comprehensively understanding dancers’ neuromuscular control patterns.
Open Access: Yes
A new method applied for explaining the landing patterns: Interpretability analysis of machine learning
Publication Name: Heliyon
Publication Date: 2024-02-29
Volume: 10
Issue: 4
Page Range: Unknown
Description:
As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.
Open Access: Yes
A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis
Publication Name: Gait and Posture
Publication Date: 2024-01-01
Volume: 107
Issue: Unknown
Page Range: 293-305
Description:
Background: Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition. Methods: Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature. Results: The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase. Significance: The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.
Open Access: Yes
New Insights Optimize Landing Strategies to Reduce Lower Limb Injury Risk
Publication Name: Cyborg and Bionic Systems
Publication Date: 2024-01-01
Volume: 5
Issue: Unknown
Page Range: Unknown
Description:
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 ratedependent) 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.
Open Access: Yes
Control Deficits and Compensatory Mechanisms in Individuals with Chronic Ankle Instability During Dual-Task Stair-to-Ground Transition
Publication Name: Bioengineering
Publication Date: 2025-10-01
Volume: 12
Issue: 10
Page Range: Unknown
Description:
(1) Background: Chronic ankle instability (CAI), a common outcome of ankle sprains, involves recurrent sprains, balance deficits, and gait impairments linked to both peripheral and central neuromuscular dysfunction. Dual-task (DT) demands further aggravate postural control, especially during stair descent, a major source of fall-related injuries. Yet the biomechanical mechanisms of stair-to-ground transition in CAI under dual-task conditions remain poorly understood. (2) Methods: Sixty individuals with CAI and age- and sex-matched controls performed stair-to-ground transitions under single- and dual-task conditions. Spatiotemporal gait parameters, center of pressure (COP) metrics, ankle inversion angle, and relative joint work contributions (Ankle%, Knee%, Hip%) were obtained using 3D motion capture, a force plate, and musculoskeletal modeling. Correlation and regression analyses assessed the relationships between ankle contributions, postural stability, and proximal joint compensations. (3) Results: Compared with the controls, the CAI group demonstrated marked control deficits during the single task (ST), characterized by reduced gait speed, increased step width, elevated mediolateral COP root mean square (COP-ml RMS), and abnormal ankle inversion and joint kinematics; these impairments were exacerbated under DT conditions. Individuals with CAI exhibited a significantly reduced ankle plantarflexion moment and energy contribution (Ankle%), accompanied by compensatory increases in knee and hip contributions. Regression analyses indicated that Ankle% significantly predicted COP-ml RMS and gait speed (GS), highlighting the pivotal role of ankle function in maintaining dynamic stability. Furthermore, CAI participants adopted a “posture-first” strategy under DT, with concurrent deterioration in gait and cognitive performance, reflecting strong reliance on attentional resources. (4) Conclusions: CAI involves global control deficits, including distal insufficiency, proximal compensation, and an inefficient energy distribution, which intensify under dual-task conditions. As the ankle is central to lower-limb kinetics, its dysfunction induces widespread instability. Rehabilitation should therefore target coordinated lower-limb training and progressive dual-task integration to improve motor control and dynamic stability.
Open Access: Yes
Impact of wearable resistance training on knee and ankle joint biomechanics: Enhancing change of direction ability in football athletes
Publication Name: Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study aimed to examine the effects of wearable resistance (WR) training on change of direction ability (CODA), muscle activation patterns, and knee joint stress in athletes. Fifteen healthy male football players participated in a pre- and post-training intervention designed to target the quadriceps, hamstrings, and calf muscles to improve neuromuscular control and joint stability. Surface electromyography (EMG) was used to assess muscle activation, and finite element analysis (FEA) was applied to evaluate stress distribution in the knee joint. Following the WR training program, there was a significant reduction in knee abduction angle during the stance phase (p = 0.001), indicating enhanced joint stability. Strength in the calf muscles increased significantly, while muscle activation levels in the quadriceps (p < 0.001) and hamstrings (p = 0.007) were also elevated. Enhanced co-activation between quadriceps and hamstrings was observed, and FEA demonstrated a significant decrease in the maximal von Mises stress in the anterior cruciate ligament (ACL) and meniscus. These findings suggest that WR training improves CODA and lower limb muscle coordination while reducing internal knee joint stress, potentially lowering the risk of ACL injuries and enhancing athletic performance.
Open Access: Yes
AI-powered biomechanical modeling for ACL-reconstructed knees: predicting knee joint contact forces via computer vision and deep learning
Publication Name: Journal of Neuroengineering and Rehabilitation
Publication Date: 2026-12-01
Volume: 23
Issue: 1
Page Range: Unknown
Description:
Background: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at high risk of osteoarthritis or secondary injuries, with abnormal knee contact forces (KCFs) identified as a key factor in joint degeneration. Traditional KCF assessment relies on expensive lab systems while advances in computer vision and AI now enable low-cost alternatives. However, currently available methods oversimplify knee mechanics and neglect compensatory movements, highlighting the urgent need for intelligent, real-time monitoring tools for personalized rehabilitation. Therefore, the aim of this study was to develop and validate an integrated, non-invasive framework for accurate KCFs prediction in ACLR patients during daily activities. We hypothesized that combining enhanced musculoskeletal modeling with a deep learning architecture incorporating spatiotemporal attention would improve the prediction accuracy across multiple movement tasks. Methods: This study simultaneously recorded three daily movements of 29 post-ACLR patients using both Vicon and OpenCap. Motion trajectories captured by Vicon were imported into OpenSim for musculoskeletal modeling and KCFs calculation. Dataset comprising OpenCap-derived kinematics and OpenSim-computed KCFs was used to train 3 learning models for the prediction of KCFs in ACLR patients across different movements. Results: Among three models, CNN-BiGRU-Attention model demonstrated the best predictive performance across all three movement tasks (R2walking = 0.973 ± 0.003, R2running = 0.982 ± 0.004, R2descending stairs = 0.951 ± 0.007). CNN and self-attention mechanism collectively enhanced the model's ability to capture key features in ACLR patients' movement data, thereby improving KCF prediction accuracy. Furthermore, for the three daily activities, all models showed superior KCFs prediction performance in running and stair-descent tasks compared to walking. Conclusion: The developed framework successfully achieved high-precision prediction of KCFs. This technological breakthrough not only provides a real-time quantitative tool for rehabilitation monitoring in patients with ACLR, but also facilitates a paradigm shift from static laboratory analysis to dynamic real-time monitoring, with broad application prospects in sports medicine, rehabilitation engineering.
Open Access: Yes
