Zixiang Gao

57207686220

Publications - 8

Comparison of Interlimb Coordination During Soccer Instep Kicking Between Elite and Amateur Players

Yaodong Gu Gusztáv Fekete Zixiang Gao Yucheng Wang Zhanyi Zhou Fengping Li Dongxu Wang

Publication Name: European Journal of Sport Science

Publication Date: 2025-09-01

Volume: 25

Issue: 9

Page Range: Unknown

Description:

This study investigates how interlimb joint coordination influences foot speed during soccer instep kicking, using continuous relative phase (CRP) as a quantitative method. The sample includes 15 elite and 15 amateur players to examine potential differences in coordination patterns and their impact on performance. Specifically, we focused on the coordination between hip, knee, and ankle joints in the forefoot-back kicking motion. Results indicated that elite players exhibited significantly higher hip-knee CRP in the coronal plane during 62%–81% of movement duration (p = 0.015) and higher knee-ankle CRP in the vertical plane during 78%–100% (p = 0.013). Moreover, elite players had significantly greater hip-knee mean absolute relative phase (MARP) and deviation phase (DP) in the coronal plane (p < 0.001), as well as increased knee-ankle DP (p = 0.04). In the horizontal plane, hip-knee MARP was also greater in the elite players compared to amateurs (p < 0.001). Further analysis revealed a significant negative correlation between hip-knee CRP and foot velocity in the sagittal plane (R = −0.66, p < 0.001), whereas a significant positive correlation was observed between knee-ankle CRP and foot velocity in the horizontal plane (R = 0.56, p = 0.002). These findings suggest that elite players have superior joint coordination, which contributes to a faster foot velocity at the moment of ball impact. Understanding these coordination patterns provides valuable insights into optimizing kicking techniques. The findings of this study suggest that joint coordination may play an important role in enhancing kicking foot speed, which could inform future training approaches aimed at improving soccer performance.

Open Access: Yes

DOI: 10.1002/ejsc.70041

Data-driven deep learning for predicting ligament fatigue failure risk mechanisms

Yaodong Gu Julien S. Baker Gusztáv Fekete Justin Fernandez Meizi Wang Wenjing Quan Liangliang Xiang Zixiang Gao Datao Xu Huiyu Zhou Tianle Jie Zhifeng Zhou Fekete Gusztav Yi Yuan Monèm Jemni

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

DOI: 10.1016/j.ijmecsci.2025.110519

Bilateral Asymmetries of Plantar Pressure and Foot Balance During Walking, Running, and Turning Gait in Typically Developing Children

Yaodong Gu Gusztáv Fekete Qichang Mei Zixiang Gao Endre Jánosi Wei Liu Hanhui Jiang Liu Xu Yile Wang Haidan Wu

Publication Name: Bioengineering

Publication Date: 2025-02-01

Volume: 12

Issue: 2

Page Range: Unknown

Description:

Biomechanical asymmetries between children’s left and right feet can affect stability and coordination, especially during dynamic movements. This study aimed to examine plantar pressure distribution, foot balance, and center of pressure (COP) trajectories in children during walking, running, and turning activities to understand how different movements influence these asymmetries. Fifteen children participated in the study, using a FootScan plantar pressure plate to capture detailed pressure and balance data. The parameters, including time-varying forces, COP, and Foot Balance Index (FBI), were analyzed through a one-dimensional Statistical Parametric Mapping (SPM1d) package. Results showed that asymmetries in COP and FBI became more pronounced, particularly during the tasks of running and directional turns. Regional plantar pressure analysis also revealed a more significant load on specific foot areas during these dynamic movements, indicating an increased reliance on one foot for stability and control. These findings suggest that early identification of asymmetrical loading patterns may be vital in promoting a balanced gait and preventing potential foot health issues in children. This study contributes to understanding pediatric foot biomechanics and provides insights for developing targeted interventions to support healthy physical development in children.

Open Access: Yes

DOI: 10.3390/bioengineering12020151

Integrating footwear features into fatigue prediction models for marathon runners: A hybrid CNN-LSTM approach

Yaodong Gu Dong Sun Yang Song Zixiang Gao Xuanzhen Cen Zhenghui Lu Yufan Xu Chengyuan Zhu Chen Hu

Publication Name: Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Footwear design, especially the curvature of carbon plates, may influence fatigue perception, but few studies have integrated footwear features into fatigue prediction models. This study aimed to develop a hybrid CNN-LSTM model to predict runners’ fatigue states and evaluate the impact of footwear characteristics on fatigue perception. Twelve male marathon runners (age = 21.8 ± 1.3 years; body mass = 59.1 ± 4.1 kg; height = 168.9 ± 2.2 cm; and weekly mileage = 68.8 ± 5.5 km) participated. They wore two types of carbon-plated shoes (flat plate, FP, and curved plate (CP)) and ran at a steady pace (Borg score 13) until a Borg score of 16 or 85% of maximum heart rate was reached for 2 min. EMG signals and physiological data were collected during treadmill running. A hybrid CNN-LSTM model was trained with and without footwear features to predict fatigue states. The model with footwear features achieved 85% accuracy, compared to 69% without. Curved carbon plate (CP) shoes delayed semi-fatigue onset, indicating better initial support, but the time to full fatigue was similar for both shoe types. The CNN-LSTM model effectively predicted fatigue states, with significant improvement when footwear features were included. Footwear design, particularly carbon plate curvature, influenced fatigue perception.

Open Access: Yes

DOI: 10.1177/17543371251356133

The Impact of Shoe Heel-Toe Drop on Plantar Pressure During the Third Trimester of Pregnancy

Yaodong Gu Gusztáv Fekete András Kovács Siqin Shen Zixiang Gao Yuqi He Zhenghui Lu Xin Li

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 509-514

Description:

Pregnancy induces various physiological adaptations to accommodate the growing fetus. Pregnant women commonly experience changes in gait, balance, and center of gravity, which may increase the risk of falls. This study investigates the effects of negative heel shoes on plantar pressure distribution during walking in third-trimester pregnant women. Twelve healthy primigravidas participated, wearing both flat shoes and negative heel shoes while walking. Plantar pressure data were collected using the Pedar-X® insole system. Results revealed that negative heel shoes significantly reduced maximum force in the medial forefoot regions compared to flat shoes, and the force-time integral only significantly decreased in the medial forefoot region. Wearing negative-heeled shoes resulted in an increase in peak force in the hallux region. The study suggests that modifying heel-toe drop in shoes can effectively mitigate plantar pressure during third-trimester pregnancy, reducing the risk of forefoot discomfort and potential injuries. Negative heel shoes could be beneficial for pregnant women, offering a solution to alleviate forefoot pressure and promote foot blood circulation during walking. However, further optimization is needed in the hallux region for negative heel shoes.

Open Access: Yes

DOI: 10.3233/ATDE240587

Biomechanical Analysis of Gymnastics Movements Using Wearable Motion Capture Systems and Linear Sensors: A Case Study of the Kipping Bar Muscle-Up

Yaodong Gu Gusztáv Fekete Qichang Mei Zixiang Gao Yining Xu Zhenghui Lu Wei Liu Xin Li

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 523-529

Description:

Gymnastics moves are complex and varied, needing precise technique and body coordination, which traditional biomechanics methods struggle to capture in detail. This study aims to look at and judge how well new motion capture and analysis technology works in gymnastics biomechanics. This study picks the kipping bar muscle up move and uses the IMU-based Xsens system and the GymAware RS unit power test system to finely look at how athletes do the move in terms of body position, power, work done by the body, and main upper limb joint movements. The study tested 8 male elite collegiate gymnasts, collecting movement data with Xsens and power data with GymAware RS unit. Results show the kipping bar muscle up takes 1.42 seconds, with a 1.13-meter shift of the body's center and a peak speed of 3.40m/s. In terms of power, the peak output was 2772.96J/s, showing the need for explosive power and fast strength. Also, the total work done was 889.70J, showing the move's efficiency and energy level. This study shows that new motion capture and analysis tech is effective in capturing complex gymnastics moves. The use of these techs not only expands the ways biomechanics can be studied but also helps in making training better and improving how efficiently moves are done.

Open Access: Yes

DOI: 10.3233/ATDE240589

Customized 3D-Printed Insoles for Diabetic Foot Care: Finite Element analysis and Machine Learning Approach

Yaodong Gu Gusztáv Fekete Zixiang Gao Yining Xu Zhenghui Lu Xin Li Qiaolin Zhang Hairong Chen

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 515-522

Description:

Diabetic foot is a common complication in patients with diabetes, which can lead to plantar ulcers and even necessitate amputation. This study aims to utilize finite element analysis to simulate the offloading effects of 3D-printed insoles with various structures on plantar pressure and to explore the use of machine learning in providing optimal plantar pressure offloading solutions for patients with diabetic foot. The results demonstrated that negative Poisson's ratio structured insoles were more effective in reducing plantar pressure (reducing pressure by an average of 39.2%) than barefoot and conventional structures. This was achieved through a unique lateral contraction deformation, which increased the contact area with the foot. The pressure-reducing effect of insoles may be weight-related, suggesting that heavier patients may require stiffer insoles. However, the machine learning algorithm demonstrated a poor fit (only 60.75%) in the task of recommending suitable insoles. In conclusion, this study demonstrated the significant effect of negative Poisson's ratio structured insoles in reducing plantar pressure in diabetic patients, providing new ideas for diabetic foot protection. With the development of data analysis technology in the future, the feasibility and application of personalised insole design will be more promising.

Open Access: Yes

DOI: 10.3233/ATDE240588

Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors

Vickie Shim Yaodong Gu Gusztáv Fekete Justin Fernandez Liangliang Xiang Zixiang Gao Alan Wang

Publication Name: Frontiers in Bioengineering and Biotechnology

Publication Date: 2024-01-01

Volume: 12

Issue: Unknown

Page Range: Unknown

Description:

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors’ potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

Open Access: Yes

DOI: 10.3389/fbioe.2024.1377383