Landing Phase Research Articles

Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning

The extension of human spaceflight across an ever-expanding domain, in conjunction with intricate mission architectures demands a paradigm shift in autonomous navigation algorithms, especially for the powered descent phase of planetary landing. Deep learning architectures have previously been explored to perform low-dimensional localization with limited success. Due to the expectations regarding novel algorithms in the context of real missions, the proposed approaches must be rigorously evaluated in extraneous scenarios and demonstrate sufficient robustness. In the current work, a novel formulation is proposed to train CNN-based Deep Learning (DL) models in a multi-layer cascading architecture and utilize the resulting classification probabilities as regression weights to estimate the position of the lander spacecraft. The approach leverages image intensity and depth data provided by multiple sensors on board to accurately determine the spacecraft’s location relative to the observed terrain at a specific altitude. Navigation performance is validated through Monte Carlo analysis, demonstrating the efficacy of the proposed DL architecture and the subsequent state-estimation framework across several simulated scenarios. It shows tremendous promise in extending the multi-modal feature learning approach to realistic missions.

ADS-B Communication Security: Assessing the Performance of Format-Preserving Lightweight Block Ciphers

In this work, we document an investigation regarding the vulnerabilities of ADS-B (automatic dependent surveillance-broadcast) communication, considering the risks to operational security. The ADS-B technology is a communication protocol to transmit surveillance information, that is, data related to position, altimetry, and speed, from the aircraft's avionics, an evolution to radars. We present an encryption solution for this communication using format-preserving encryption implemented in a microcontroller-embedded system. We also evaluate the use of lightweight symmetric block ciphers for better computational performance. When used as a pseudorandom function, we observe that such ciphers maintain a high entropy output value with low computational cost --- up to sixteen times faster for similar entropy values. Finally, the computational performance obtained with the proposed solution is analyzed by processing real-time data from aircraft in the landing and takeoff phase at the international airport of Recife/Guararapes - Gilberto Freyre, based in Recife/PE, Brazil.

Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection

Windshear is a microscale meteorological phenomenon that can be dangerous to aircraft during the take-off and landing phases. Accurate windshear detection plays a significant role in air traffic control. In this paper, we aim to investigate a machine learning method for windshear detection based on previously collected wind velocity data and windshear records. Generally, the occurrence of windshear events are reported by pilots. However, due to the discontinuity of flight schedules, there are presumably many unreported windshear events when there is no flight, making it difficult to ensure that all the unreported events are all non-windshear events. Hence, one of the key issues for machine-learning-based windshear detection is determining how to correctly distinguish windshear cases from the unreported events. To address this issue, we propose to use a positive and unlabeled learning method in this paper to identify windshear events from unreported cases based on wind velocity data collected by Doppler light detection and ranging (LiDAR) plan position indicator (PPI) scans. An optimal-transport-based optimization model is proposed to distinguish whether a windshear event appears in a sample constructed by several LiDAR PPI scans. Then, a binary classifier is trained to determine whether a sample represents windshear. Numerical experiments based on the observational wind velocity data collected at the Hong Kong International Airport show that the proposed scheme can properly recognize potential windshear cases (windshear cases without pilot reports) and greatly improve windshear detection and prediction accuracy.

Research on optimization of basketball jumping landing techniques and reduction of ankle joint injury risk based on biomechanics analysis

The ankle joint’s functional state is crucial during dynamic basketball movements, especially jumping and landing. A higher risk of ankle injuries is linked to limited ankle dorsiflexion, which can result in biomechanical alterations affecting landing safely and effectively. To improve basketball jumping landing skills and decrease the risk of ankle joint injuries, the objective of this research is to examine the way difficulties of dorsiflexion in the ankle influence the biomechanics of the lower extremities. Seventy-five participants completed basketball-specific stop-jumping actions on a flat surface (Control), a 10°, 20°, and 30° incline, using data from their dominant legs. The musculoskeletal framework was developed to simulate and analyze biomechanical data related to landing techniques. Post-hoc Tukey testing is utilized to estimate the category variations and it utilizes the statistical parametric mapping (SPM). As the angle of ankle restrictions increased, significant alterations in lower extremity biomechanics were detected during basketball jumping landings. Hip extension angles, knee external rotation angles, angular velocities, and knee extension angular velocities all showed appreciable increases. These findings indicate that increased ankle limitation has a significant impact on the mechanics of lower limb movements during athletic performance. The peripatellar muscles’ co-activation gradually increased, and the landing phase showed a substantial increase in the patellofemoral joint contact force (PTF). The impact of ankle dorsiflexion difficulties in joint strain and kinematics during basketball jumping landings is demonstrated by these findings. An enhanced co-activation of the peripatellar muscles and increased PTF in response to increasing ankle restriction indicate compensatory strategies for maintaining balance during basketball landings. This study emphasizes the significance of improving basketball jumping landing techniques to improve ankle stability and decrease the possibility of ankle joint injuries in players.

Is it safe to use sand surface during rehab when training basic skills in early stages? How sand surface and inertial sensor position affect Drop Jump landing's G-forces and it's application in injury recovery

In sports, the use of sand surface as a tool in the injury recovery process has received very good acceptance and an increased attention in recent years. Unfortunately, the number of studies in this area continues being scarce. Therefore, this study analyzes the existence of possible differences in the magnitude of the impact generated during a 45 cm Drop Jump (DJ), depending on the body area analyzed, the contact surface (sand or grass) and the height (thoracic spine and ankle) at which a jump is placed. To that purpose, 6 participants were analyzed by wearing 3 IMU sensors (WIMU PRO™) to measure G foreces in the landing phase of the DJ. The results suggest the existence of statistically significant differences when comparing surfaces (sand vs. grass), body area (ankle and lumbar) and location of device placement (thoracic area vs. ankle).

Skittering locomotion in cricket frogs: a form of porpoising.

Multiple species of frogs in the Ranidae family have been observed to 'skitter' across the water surface, but little is understood about the biomechanical or physical mechanisms that underlie this behavior. All documented descriptions are anecdotal, asserting simply that the frogs can cross the water surface without sinking. To study this form of interfacial locomotion, we recorded high-speed video of the northern cricket frog Acris crepitans and quantified its kinematics. We also compared its semi-aquatic behavior with the frogs' terrestrial locomotion. Contrary to expectations based on anecdotal knowledge, we found that cricket frogs do not maintain an above-surface position throughout the locomotor cycle. Instead, the frogs are completely submerged during both the launching and landing phase of a jump cycle, similar to porpoising in other animals. It is possible that leg-retraction time constrains these frogs from performing true surface-only locomotion.

A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.

A bio-inspired looming detection for stable landing in unmanned aerial vehicles**This research was funded by the National Natural Science Foundation ofchina, grant number 62206065, and the Bagui Scholar Program of Guangxi.

Flying insects, such as flies and bees, have evolved the capability to rely solely on visual cues for smooth and secure landings on various surfaces. In the process of carrying out tasks, micro unmanned aerial vehicles (UAVs) may encounter various emergencies, and it is necessary to land safely in complex and unpredictable ground environments, especially when altitude information is not accurately obtained, which undoubtedly poses a significant challenge. Our study draws on the remarkable response mechanism of the Lobula Giant Movement Detector to looming scenarios to develop a novel UAV landing strategy. The proposed strategy does not require distance estimation, making it particularly suitable for payload-constrained micro aerial vehicles. Through a series of experiments, this strategy has proven to effectively achieve stable and high-performance landings in unknown and complex environments using only a monocular camera. Furthermore, a novel mechanism to trigger the final landing phase has been introduced, further ensuring the safe and stable touchdown of the drone.

The effect of simulated basketball game load on patellar tendon load during stop-jump movement

Patellar tendinopathy (PT) is frequently observed among basketball players, particularly in sports involving repetitive jumping movements. However, the overall impact of accumulated exercise load on the patellar tendon is still not fully comprehended. Therefore, the goal of this study is to examine how a simulated basketball game affects the biomechanics of stop-jump movements, specifically focusing on the effects on the patellar tendon. The kinematic and kinetic data were collected immediately after the warm-up and each phase of the simulated basketball game (P1, P2, P3, and P4). A musculoskeletal model was built to calculate patellar tendon force (PTF) and the key biomechanical metrics during the horizontal landing and vertical jumping phases were explored separately, followed by correlation analyses. Linear regression analyses were performed on variables strongly correlated with PTF. The accumulation of load led to significant differences (p < 0.05) in the angles, velocities, torques, work contributions, peak patellar tendon force (PTF), and anterior-posterior ground reaction force (APGRF) observed during the landing and vertical jump phases at the hip, knee, and ankle joints. PTF showed strong correlations with knee flexion angle, knee extension angular velocity, ankle plantarflexion angular velocity, and APGRF, with R2 values of 0.50, 0.58, 0.70, and 0.56, respectively. PTF significantly decreased in P3 and P4, possibly due to the subjects’ adaptation and adjustment of their stop-jump posture strategy after load accumulation, including reducing knee and hip flexion angles and decreasing the net knee extension moment.

Coupling of Advanced Guidance and Robust Control for the Descent and Precise Landing of Reusable Launchers

This paper investigates the coupling of successive convex optimization guidance with robust structured H∞ control for the descent and precise landing of Reusable Launch Vehicles (RLVs). More particularly, this Guidance and Control (G&C) system is foreseen to be integrated into a nonlinear six-degree-of-freedom RLV controlled dynamics simulator which covers the aerodynamic and powered descent phase until vertical landing of a first-stage rocket equipped with a thrust vector control system and steerable planar fins. A cost function strategy analysis is performed to find out the most efficient one to be implemented in closed-loop with the robust control system and the vehicle flight mechanics involved. In addition, the controller synthesis via structured H∞ is thoroughly described. The latter are built at different points of the descent trajectory using Proportional-Integral-Derivative (PID)-like structures with feedback on the attitude angles, rates, and lateral body velocities. The architecture is verified through linear analyses as well as nonlinear cases with the aforementioned simulator, and the G&C approach is validated by comparing the performance and robustness with a baseline system in nominal conditions as well as in the presence of perturbations. The overall results show that the proposed G&C system represents a relevant candidate for realistic descent flight and precise landing phase for reusable launchers.

Impact of Quadriceps Muscle Fatigue on Ankle Joint Compensation Strategies During Single-Leg Vertical Jump Landing.

This study investigates the impact of quadriceps fatigue on lower limb biomechanics during the landing phase of a single-leg vertical jump (SLJ) in 25 amateur male basketball players from Ningbo University. Fatigue was induced through single-leg knee flexion and extension exercises until task failure. Kinematic and dynamic data were collected pre-fatigue (PRF) and post-fatigue (POF) using the Vicon motion capture system and the AMTI force platform and analyzed using an OpenSim musculoskeletal model. Paired sample t-tests revealed significant changes in knee and hip biomechanics under different fatigue conditions, with knee joint angle (p < 0.001), velocity (p = 0.006), moment (p = 0.006), and power (p = 0.036) showing significant alterations. Hip joint angle (p = 0.002), moment (p = 0.033), and power (p < 0.001) also exhibited significant changes. Muscle activation and joint power were significantly higher in the POF condition, while joint stiffness was lower. These findings suggest that quadriceps fatigue leads to biomechanical adjustments in the knee and hip joints, which may increase the risk of injury despite aiding in landing stability.

Redundancy Design of FADS System for Complex Leading- Edge Aircraft Based on Neural Network

The flush airdata sensing system (FADS) calculates flight parameters such as angle of attack, sideslip angle, Mach number, incoming flow pressure and static pressure based on the pressure measurement of the aircraft surface. It can effectively solve the problem that the leading edge of the pitot tube protruding from the fuselage cannot adapt to the severe aerodynamic heating faced by hypersonic aircraft during the cruise phase, and at the same time meet the aircraft's demand for stealth performance. At present, there is little analysis and research on the use of neural network methods and FADS systems for complex leading edge aircraft. In view of the subsonic/transonic conditions of hypersonic aircraft returning autonomously during the landing phase, the redundancy design and verification of the head FADS system of complex leading edge aircraft are carried out considering the influence of thin leading edge and inlet components. 15 pressure measuring holes are opened in the head of the complex leading edge aircraft. Through a large number of refined numerical simulations, the pressure database of the aircraft under different incoming flow conditions is established, and the typical working conditions are verified by wind tunnel tests. For the complex leading edge aircraft, 4 sets of neural network algorithms are established based on pressure data and redundant design research is carried out, including 1 set of 9-hole algorithm and 3 sets of redundant algorithms. Among them, the 9-hole algorithm has a higher accuracy, with the solution error of the angle of attack within 0.07∘, the solution error of the sideslip angle within 0.3∘, the solution error of the Mach number within 0.0012, and the relative error of the solution of the incoming flow pressure and static pressure within 1.5%. In addition, a system solution process with a certain fault tolerance was established, which can continue to maintain the effective output of the incoming flow parameters in the event of failure of any single pressure measuring hole.

Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

AbstractThe workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real‐time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA‐TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA‐TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k‐nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing &gt; takeoff &gt; cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

Spatiotemporal dynamics and transformation of the Parana State coastline: A 34-year analysis using RS, GIS, and machine learning

The coastal zone is a critical and vibrant area within coastal cities. Analyzing the changes in the coastal zone of Parana State helps determine the spatiotemporal evolution patterns and driving forces of this region, providing guidance for resource protection and utilization. This study employs remote sensing (RS), Geographic Information System (GIS), and Machine Learning techniques, using Landsat data sets with eight distinct scenes from 1990 to 2024, to extract and analyze the coastline. Specifically, methodologies such as Shoreline Extraction, the Coastline Diversity Index (CDI), and the Coastline Utilization Index (CUI) were applied to assess the coastline's characteristics. The spatiotemporal characteristics of the coastline development in Parana are meticulously examined to ensure accuracy. The findings reveal that: 1) Over the past 34 years, the coastline of Parana State has shown a consistent increasing trend. Natural coastlines have been declining by 45% annually, while artificial coastlines have been increasing by approximately 55% annually. 2) The nature of the shoreline is transitioning from natural to sea-reclamation, engineering-enhanced, and degraded forest. 3) The coastline changes have occurred in three phases: The Built-up Land phase, the Port Petrochemical phase, and the Reducing Coastal Erosion phase. Significant shoreline extension is observed near both biological and ecological coasts. 4) The primary factors influencing the coastline alterations in Parana State include natural forces, human activities, and regional policies. These insights into the spatiotemporal dynamics of the Parana coastline can inform better coastal management and conservation strategies.

Automatic Aircraft Landing System: A Review

The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.

Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Building interfaces between unmanned aircraft systems (UAS), air traffic controllers (ATCo), and the national airspace system (NAS): A software training platform

Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.

Effects of 16weeks of plyometric training on knee biomechanics during the landing phase in athletes.

This study investigated the effects of plyometric training on lower-limb muscle strength and knee biomechanical characteristics during the landing phase. Twenty-four male subjects were recruited for this study with a randomised controlled design. They were randomly divided into a plyometric training group and a traditional training group and underwent training for 16weeks. Each subject was evaluated every 8weeks for knee and hip isokinetic muscle strength as well as knee kinematics and kinetics during landing. The results indicated significant group and time interaction effects for knee extension strength (F=74.942 and p=0.001), hip extension strength (F=99.763 and p=0.000) and hip flexion strength (F=182.922 and p=0.000). For landing kinematics, there were significant group main effects for knee flexion angle range (F=4.429 and p=0.047), significant time main effects for valgus angle (F=6.502 and p=0.011) and significant group and time interaction effects for internal rotation angle range (F=5.475 and p=0.008). The group main effect for maximum knee flexion angle was significant (F=7.534 and p=0.012), and the group and time interaction effect for maximum internal rotation angle was significant (F=15.737 and p=0.001). For landing kinetics, the group main effect of the loading rate was significant (F=4.576 and p=0.044). Significant group and time interaction effects were observed for knee extension moment at the moment of maximum vertical ground reaction force (F=5.095 and p=0.010) and for abduction moment (F=8.250 and p=0.001). These findings suggest that plyometric training leads to greater improvements in hip and knee muscle strength and beneficial changes in knee biomechanics during landing compared to traditional training.

The Comparison of Electromyography (EMG) During Blocking in Elite Sepaktakraw Athletes

Background: Blocking is a major indicator of the sepaktakraw game’s results. The analysis of muscle activity involved in blocking provides coaches or amateur athletes with additional information about the importance of muscle groups that influence effective blocking. Objective: This research aimed to compare the maximum voluntary contraction (MVC)during blocking elite sepaktakraw athletes in different muscles. Methods: This research is a cross-sectional study design with fourteen male sepaktakraw athletes (striker position) in the Sepaktakraw Thailand League, aged 20-40 years. All participants completed an informed consent by the ethical committee of Thailand National Sports University (TNSU-SCI 043-2566), and were placed electrodes on five muscle groups, including the rectus abdominis (RA), left rectus femoris (LRF), left gastrocnemius medialis (LGM), right rectus femoris (RRF) and right gastrocnemius medialis (RGM). The collection of blocking is divided into 3 phases: takeoff, flight and landing. Results: The LGM had the highest and was significantly with RA in takeoff phase and landing phase (P0.05), RGM had the highest in flight phase and significantly different with takeoff (p0.05). Moreover, RA during flight and landing significantly differed from takeoff (p0.05). Conclusion: The information this research will help coaches and amateur athletes understanding the blocking in elite athletes. Additionally, they need a specific muscle training program.

The effects of fatigue on the relationship between ankle angle at initial contact and the knee and hip joints in landing: Assessing the risk of ACL injury

BackgroundAnterior cruciate ligament (ACL) injuries may correlate with lower limb angles and biomechanical factors in both dominant and non-dominant legs at initial contact (IC) post-landing. This study aims to investigate the correlation between ankle angles in three axes at IC and knee and hip joint angles during post-spike landings in professional volleyball players, both pre- and post-fatigue induction. Research questionTo what extent does fatigue influence lower limb joint angles, and what is the relationship between ankle joint angles and hip and knee angles at IC during the landing phase following a volleyball spike? MethodsUnder conditions involving the peripheral fatiguing protocol, the lower limb joint angles at IC following post-spike landings were measured in 28 professional male volleyball players aged between 19 and 28 years, who executed the Bosco fatigue protocol both before and after inducing fatigue. A paired t-test was utilized to compare the joint angles pre- and post-fatigue in both dominant and non-dominant legs. Furthermore, Pearson's correlation test was conducted to explore the relationship between ankle angles at IC and the corresponding knee and hip joint angles. ResultsThe findings of the study revealed that fatigue significantly increased hip external rotation and decreased knee joint flexion and external rotation in both the dominant and non-dominant legs (p < 0.05). Additionally, correlation analysis demonstrated that the ankle joint's positioning in the frontal and horizontal planes was significantly associated with hip flexion and external rotation at the IC, as well as with knee flexion and rotation (0.40 < r < 0.80). ConclusionFatigue increased hip external rotation and ankle internal rotation, weakening the correlation between these joints while strengthening the ankle-knee relationship, indicating a reduced hip control in jumps. This suggests a heightened ACL injury risk in the dominant leg due to the weakened ankle-hip connection, contrasting with the non-dominant leg.