Vol. 21 No. 3 (2024)

Open Access

Article

Magnetic resonance imaging diagnosis of ankle joint athletic injury based on machine learning algorithms

Hongxia Han, Yuanwei Li

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 414 , 2024, DOI: 10.62617/mcb414

Abstract：

The diagnosis of ankle joint athletic injuries using traditional magnetic resonance imaging (MRI) relies on the subjective judgment and experience of doctors, and small structural changes in athletic injuries are difficult to accurately detect and diagnose. By using machine learning (ML) algorithms and image processing techniques to obtain objective and consistent diagnostic results, the accuracy of diagnosing ankle joint athletic injuries can be improved. This article collected a large number of MRI images of ankle joint athletic injuries, and preprocessed the collected images to extract morphological and texture features, and perform feature fusion. The Residual Network (ResNet) was improved, and the Leaky linear rectification function (ReLU, Corrected linear unit) activation function was introduced. The transfer learning was utilized to increase the convergence speed of the model, and the global maximum pooling layer and softmax classifier were used to construct the fully connected layer. After sufficient training on the training set, the findings on the test set indicated that the average accuracy of the improved ResNet model for ankle joint injury classification was 98.3%. The use of an improved ResNet model can effectively improve the diagnostic effectiveness of ankle joint athletic injuries, providing a new method for medical diagnosis of MRI.

Open Access

Article

Virtual reality technology in rural sports sustainable development reform research

Wang Luo, Xianglin Luo

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 366 , 2024, DOI: 10.62617/mcb366

Abstract：

Rural sports support unity among communities, encourage physical exercise in natural environments, and preserve regional traditions all of which are beneficial to sustainable development. Villages frequently encourage environmental stewardship and conservation initiatives, highlight cultural history, and promote local economies. By providing realistic practice environments and simulated games, we used virtual reality (VR) technology to improve rural sports and lessen the demand for substantial development infrastructure. A potential disadvantage of adopting VR technology for rural sports is the possibility of insufficient facilities and supplies available in remote areas. In this paper, we propose a novel World Cup Search-driven Quadratic Support Vector Machine (WCS-QSVM) method to enhance the performance of VR in rural sports. We use one of the Chinese traditional games dragon boating. We employ an immersive setup that replicates the kinematic and sensory subtleties of dragon boating through the use of virtual reality. To allow users to participate in real-world paddling activities, such as moving iron rods on a boat with a reasonably replicated resistance to water, we use a controller-based approach. As a result, we evaluate the performance of our proposal. According to the findings, the use of VR can enhance the growth of sustainable development in rural sports.

Open Access

Article

Exploring the influence of body movements on spatial perception in landscape and interior design

Pengfei Zhao

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 434 , 2024, DOI: 10.62617/mcb434

Abstract：

This study investigates the influence of body movements on spatial perception in both landscape and interior design environments, focusing on how physical interactions shape spatial understanding beyond visual perception alone. Grounded in the theory of embodied cognition, the research examines how gait, posture, and movement dynamics affect spatial awareness. The study captures detailed data on movement patterns and visual engagement across different spatial contexts using a combination of real-world observations and Virtual Reality (VR) simulations, motion-tracking systems, wearable sensors, and eye-tracking technology. A total of 157 participants, aged 20 to 65, navigated both outdoor landscapes and indoor environments, with key variables such as surface materials, spatial layout, and lighting conditions manipulated to assess their effects on spatial perception. The study measured gait speed, step frequency, path deviations, time to destination, visual attention, and subjective ratings of perceived openness, ease of movement, and emotional response. Key findings include that surface materials significantly influenced gait speed and step frequency. For example, participants walking on concrete had a significantly faster gait speed (mean difference = 0.5220, p = 0.001) than those walking on gravel. In terms of spatial layout, the two-way Analysis of variance (ANOVA) results showed that winding paths led to more path deviations ( F -statistic = 350.00, p = 3.19 × 10 −8 ) and longer times to destination ( F -statistic = 1744.00, p = 2.39 ´ 10 − 11 ) compared to straight paths. The environment type (landscape vs. interior) also significantly affected navigation, with landscape participants showing a more significant deviation from direct paths ( F -statistic = 19.60, p = 2.37 × 10 −3 ). Visual engagement data, analyzed through a chi-square test, indicated that vertical elements like walls approached significance in attracting visual attention (Chi-square = 2.88, p = 0.0896), while other elements like trees and benches had less impact. The Wilcoxon signed-rank test results showed significant differences between real-world and VR experiences in perceived openness ( W -statistic = 0.0, p = 0.001953), ease of movement ( W -statistic = 0.0, p = 0.001953), and comfort ( W -statistic = 0.0, p = 0.001953), highlighting VR’s limitations in replicating the full embodied experience of physical spaces.

Open Access

Article

Evaluate the effect of exercise core strength training on antioxidant enzyme activity in women from a biomechanical perspective

Yingshun Li, Yingxue Li

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 232 , 2024, DOI: 10.62617/mcb232

Abstract：

At present, the incidence rate of chronic diseases is increasing year by year. A variety of antioxidant enzymes in the human body, such as Superoxide Dismutase (SOD), Nitric Oxide Synthase (NOS), Glutathione Peroxidase (GSH Px), Malonic Dialdehyde (MDA) and Catalase (CAT), help to inhibit the generation of oxygen free radicals and play a certain role in preventing the occurrence of chronic diseases. The research on the activity of antioxidant enzymes and the delivery of antioxidant drugs has gradually become the focus of relevant scholars. The physical quality of women is lower than that of men, so it is of great practical significance to study the antioxidant enzyme activity of women. Therefore, this paper explores the influence of exercise core strength training on women’s antioxidant enzyme activity from a biomechanical perspective and concludes that core strength training can improve female students’ SOD content level by 2.58%, and can improve female students’ NOS content level, GSH-Px content level, and MDA content level. Sports core strength training has a positive impact on women’s antioxidant enzyme activity.

Open Access

Article

Prediction and treatment of joint injuries in basketball training based on improved regression algorithm from the perspective of sports biomechanics

Yan Bai, Xiao Yang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 258 , 2024, DOI: 10.62617/mcb258

Abstract：

With the increasing popularity of basketball, especially in collegiate competitions like the University Basketball Super League, the sport has become a significant part of student life. The intensity of basketball training and competition has risen, necessitating athletes to have enhanced physical capabilities to meet modern demands. This heightened physical confrontation often leads to various injuries, with joint injuries being particularly common and impactful. This study integrates sports biomechanics with machine learning to address the prediction and treatment of joint injuries in basketball training. By employing an improved regression algorithm and leveraging high-performance computing, we have experimentally analyzed the prediction of joint injuries and proposed effective solutions. Our results indicate that the difference between the highest and lowest predicted residual values for the Back Propagation (BP) model was 0.92, and for the Extreme Learning Machine (ELM) regression model was 0.87. Notably, the improved ELM regression model demonstrated a reduced residual difference of 0.43. This improvement suggests that the enhanced ELM regression model offers superior prediction accuracy for joint injuries in basketball training and provides more comprehensive monitoring of athletes’ physical health, thereby supporting the advancement of basketball training programs.

Open Access

Article

Sports biomechanical analysis of knee joint injuries in table tennis players

Wenyan Li, Sikuan Ren

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 538 , 2024, DOI: 10.62617/mcb538

Abstract：

Table tennis is a fast-paced, explosive sport that puts a lot of strain and stress on players’ lower limbs, particularly their knee joints. This work built a thorough mathematical model to investigate the dynamic and kinematic properties of the knee joint under various motion situations, with the goal of better understanding the biomechanical behavior of the knee joint in table tennis. A model of knee joint motion that takes into account the two degrees of freedom—flexion, extension, and rotation—is put forth based on the concepts of human biomechanics and kinematics theory. This model uses the Newton Euler equation to explain the mechanical behavior of the knee joint and integrates internal forces like muscle forces and external factors like ground reaction forces for torque balance analysis. This study offers a thorough examination of the force distribution and knee joint trajectory in table tennis using numerical simulation techniques. The findings show that the knee joint undergoes considerable compression and shear forces during intense activity, and that the joint’s stress properties vary significantly depending on the kind of movement. This finding is a valuable resource for table tennis players’ knee joint injury prevention and rehabilitation.

Open Access

Article

Full-process supported Simulation Platform Framework based on cloud computing and HPC integration

Hao Wang, Jinghua Feng, Lin Wang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 658 , 2024, DOI: 10.62617/mcb658

Abstract：

Simulation technology is widely used in many fields, it usually involves three processes, (i) pre-process, (ii) analysis solver, and (iii) post-process. Simulation calculations require a large amount of computing resources, and users usually need to use cloud and High-Performance Computing (HPC) systems to complete works. Simulation works are increasingly depending on the capacity of HPC or cloud, for cost reasons, people are more willing to use the services than self-built an HPC or Cloud computing cluster. However, that leads to the isolation of calculations and pre- and post-processing work, adding additional time for data transfer. Moreover, simulation engineers also want to use cloud servers in pre-processing and post-processing, since compared with local workstations, cloud servers have significant advantages in saving hardware investment, remote office collaboration, and data integration management. Therefore, we provide a platform framework based on cloud computing and HPC integration that supports the full process of simulation. Then, we implemented it in Tianhe-1A and Tianhe exascale supercomputers and THCloud environments. Through a city area-level explosion simulation experiment, it was verified that the framework can fully support the whole process of simulation, and effectively reduce the time of simulation work, improving the simulation engineer’s work efficiency. The study shows that the platform provides a feasible solution for full-process simulation. Compared with other platforms, it has the characteristics of full-process, high performance and high security.

Open Access

Article

The biomechanics of public speaking: Enhancing political communication and persuasion through posture and gesture analysis

Xiaojing Ding

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 566 , 2024, DOI: 10.62617/mcb566

Abstract：

Effective public speaking is critical in political communication, where non-verbal cues such as posture, gestures, facial expressions, and body movements significantly influence audience perception. Despite the known impact of these biomechanics, research in diverse cultural contexts remains limited. This study addresses this gap by examining the biomechanics of public speaking in the Philippines, focusing on 16 political speakers and 211 audience participants from varied demographic backgrounds. The study uses advanced motion capture technology, high-definition video recordings, and audience perception surveys to investigate how specific non-verbal elements affect audience engagement and perception. Challenges include accurately measuring and isolating the effects of these biomechanical factors amidst varied cultural and linguistic influences. The findings reveal that an upright posture significantly enhances perceived confidence ( r = 0.72, p = 0.003) and trustworthiness ( r = 0.65, p = 0.004), while high-frequency gestures positively correlate with clarity ( β = 0.47, p = 0.008) and persuasiveness ( β = 0.66, p = 0.003). Head movements, such as nodding, significantly increase audience engagement ( F -value = 5.73, p = 0.002), and high-intensity smiling enhances emotional responses ( t -value = 4.86, p = 0.001). These results underscore the importance of biomechanics in political communication, demonstrating that specific gestures and postures are critical in conveying confidence and persuasiveness. The study contributes to the field by offering insights into the strategic use of body language, particularly in multicultural settings, to enhance audience engagement and speaker effectiveness.

Open Access

Article

DeepmiRNATar: A deep learning-based model for miRNA targets prediction

Huimin Peng, Chenyu Li, Ying Lu, Dazhou Li

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 253 , 2024, DOI: 10.62617/mcb253

Abstract：

MicroRNAs (miRNAs) play a crucial role in regulating fundamental biological processes such as the cell cycle, differentiation, and apoptosis by directly interacting with multiple genes (mRNAs). This regulatory mechanism has a profound impact on cellular function and the overall physiological condition of an organism. However, the prediction of miRNA-mRNA interactions encounters computational challenges in the field of biology due to the diverse sequences and complex data patterns. To overcome these obstacles, this research effort introduced DeepmiRNATar, a tool designed to precisely pinpoint miRNA targets, offering essential assistance in the realm of disease management. DeepmiRNATar leverages the Word2vec-based DeepLncLoc approach for encoding miRNA sequence characteristics and utilizes the DNABERT pre-trained model for in-depth semantic comprehension of target sequences. Through the integration of TextCNN, BiLSTM, and SpatialConv Attention mechanisms, the model scrutinizes structural features, temporal relationships, and overall interactions within the sequences. Following a series of experimental assessments, DeepmiRNATar attained an impressive AUC of 99.15% on the evaluation dataset, on par with the current leading prediction methodologies. Notably, the precision-recall curve, sensitivity, and F -measure values reached 99.18%, 97.43%, and 95.47%, respectively. Compared to existing miRNA target prediction models, DeepmiRNATar demonstrates a notable enhancement in overall predictive accuracy. The successful creation and experimental validation of the DeepmiRNATar model signify a significant advancement in miRNA target identification technology.

Open Access

Article

Optimization design of biomechanical parameters based on advanced mathematical modelling

Yuan Wen

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 463 , 2024, DOI: 10.62617/mcb463

Abstract：

In recent years the use of biomechanics in athletic training and performance has received a lot of attention, especially in university sports programs. Biomechanics is the study of the mechanical principles that control how biological things move or are constructed. It is critical for understanding the intricate relationships between physical performance, body mechanics, and injury prevention. The objective of this study is to establish how biomechanical variables can be designed and optimized in universities using mathematical modeling. In this study, a novel Emperor Penguin Search-driven Dynamic Feedforward Neural Network (EPSO-DFNN) is proposed to optimize the biomechanical parameters of athletes. Various biomechanical data are utilized from athletes participating in different sports. Biomechanical parameters include muscle activation patterns, joint angles, forces, and movement. The data was preprocessed using Z-score normalization from the obtained data. The Fast Fourier Transform (FFT) using features is extracted from preprocessed data. The proposed method is to identify the optimal configurations for athlete’s movements tailored to their sports and individual biomechanical profiles. The proposed method is the performance of various evaluation metrics such as F1-score (92.76%), precision (91.42%), accuracy (90.02%), and recall (89.69%). The result demonstrated that the proposed method effectively improved the performance in athletic capabilities compared to other traditional algorithms. This study demonstrates how mathematical modeling may be used to optimize biomechanical characteristics, providing insightful information that can be used to improve athletic performance and encourage safer behaviors in athletic settings.

Open Access

Article

Biomechanical analysis and teaching strategies of complex movements in physical education teaching

Benlai Cui, Hui Wu

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 478 , 2024, DOI: 10.62617/mcb478

Abstract：

This study suggests a new method for evaluating students’ ordinariness of movement in professional physical education by developing an assessment algorithm based on the biomechanical analysis of complex motions. The study aims to provide purposeful and data-driven techniques for assessing and optimizing movement ability in intricate physical tasks by utilizing higher motion capture and deep learning (DL) approaches, especially the Updated African Buffalo Optimization Based Deep Convolutional Neural Network (UABO-DCNN) categorization. The method includes collecting data utilizing high-precision movement capture equipment to research certain multifaceted movements, preprocessing trajectory data to extract kinematic, temporal, and spatial information, and increasing categorization algorithms with UABO-DCNN. The consequences specify that the algorithm can differentiate between normal and abnormal association patterns with outstanding accuracy. The UABO-DCNN model measures physical education teaching complex movements with accuracy (99.43%), precision (98.12%), recall (98.50%), F1-score (98.56%), and specificity (98.40%). Furthermore, the result is reliable, with a broader tendency toward instructive skill and individualized learning, which requires the development of physical education instruction actions by creating a culture of physical literacy and well-being. The implication of this employment includes an enhanced approach to promote optimal association skill increase in students, particularly for confronting complicated biomechanical measures.

Open Access

Article

The effects of muscle factors irisin on lipid metabolism in breast cancer: A possible mechanism anti-tumor mechanism of physical activity

Jiaxin Zhu, Chengxiang Li, Siyu Tian, Meng Ding

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 345 , 2024, DOI: 10.62617/mcb345

Abstract：

Breast cancer is the most prevalent cancer in the female population and is a significant cause of global cancer deaths in this group. Obesity increases a woman’s risk of developing breast cancer and has a negative impact on prognosis. Metabolic alterations are an important part of the process of cancer migration; invasion and proliferation, with lipids being a major metabolic substrate for rapid cancer progression, capable of influencing the metabolic crosstalk between tumor cells and other cells in the tumor microenvironment. Physical activity-induced irisin affects the progression of obesity-associated breast cancer and is a new indicator for breast cancer diagnosis. Existing evidence suggests a potential inhibitory effect of physical activity-induced irisin on the progression of breast cancer. A strong association exists between obesity and breast cancer progression and outcomes. This paper discusses how physical activity-induced irisin may achieve cancer suppression by affecting lipid metabolic processes between breast cancer cells and cancer-associated adipocytes, and elucidates the molecular pathways involved in the effects of irisin on cancer lipid reprogramming, thereby helping to prevent the metastatic progression of breast cancer, and ultimately improving the survival rate of this patient group.

Open Access

Article

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

Xiangguo Yin

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 648 , 2024, DOI: 10.62617/mcb648

Abstract：

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.

Open Access

Article

The impact of ergonomics and biomechanics on optimizing learning environments in higher education management

Kang Liu, Yiwen Zhou

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 396 , 2024, DOI: 10.62617/mcb396

Abstract：

In higher education, the design of learning environments is serious in prompting student well-being, engagement, and academic performance. Traditional classrooms often lack ergonomic consideration, leading to discomfort, increased physical strain, and reduced concentration. As education evolves, there is a growing need to apply ergonomic and biomechanical principles to create spaces that accommodate students’ diverse physical and cognitive needs. Despite the theoretical support for these interventions, there is limited empirical evidence on their practical impact in educational settings. This study addresses this gap by examining the effects of ergonomic and biomechanical adjustments on student outcomes in higher education. Utilizing a mixed-methods approach, the research was conducted across four universities with a diverse sample of 126 students. The interventions included adjusting furniture, optimized spatial layouts, and environmental adjustments to assess their influence on postural alignment, muscle activity, and engagement. Key findings revealed significant improvements: postural alignment showed an increase in spinal angle from 118° to 133° and a reduction in neck angle from 37° to 29°. Muscle activity, particularly in the neck and lower back, decreased by 40% and 44%, respectively. Additionally, self-reported comfort improved from a mean of 2.8 to 4.3, while physical strain decreased from 3.7 to 2.2. Engagement levels also improved, with scores rising from 3.1 to 4.5. These results underscore the importance of ergonomic design in promoting student well-being and fostering a more conducive learning environment, providing evidence-based recommendations for optimizing learning spaces in higher education.

Open Access

Article

Algorithm for simulating calligrapher’s stroke features using neural networks

Xiaojun Zhu

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 556 , 2024, DOI: 10.62617/mcb556

Abstract：

In the field of calligraphy art, simulating the stroke features of calligraphers has always been a challenging task. Traditional methods often rely on manually designed rules or feature extraction algorithms, which are difficult to accurately capture calligraphy details and time-consuming. This article aimed to explore a more effective method to simulate the stroke features of calligraphers using neural network technology. This article mainly explored the simulation algorithm of calligrapher stroke features based on neural networks, including calligrapher stroke feature statistics, calligrapher stroke feature abstraction, and calligrapher stroke feature extraction. By using Convolutional Neural Network (CNN) to learn and analyze calligraphy works, a better performance neural network model was established to achieve automatic recognition and classification of calligrapher stroke features. By continuously optimizing model parameters, the accuracy of calligrapher stroke feature simulation can be improved. The average similarity rates for imitating stroke features of five calligraphers (regular script, cursive script, clerical script, seal script, and running script) using Artificial Neural Network (ANN) were 0.72, 0.62, 0.40, 0.33, and 0.53, respectively. The average similarity rates for imitating stroke features of 5 calligraphers (regular script, cursive script, clerical script, seal script, and running script) using CNN were 0.93, 0.78, 0.87, 0.67, and 0.80, respectively. The research results of this article promoted the inheritance and innovation of calligraphy art, and expanded the expression forms and application fields of calligraphy art.

Open Access

Article

Wave propagation and soliton behavior in biomechanical tissues: A mathematical approach

Man Jiang, Dan Zhang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 424 , 2024, DOI: 10.62617/mcb424

Abstract：

This study presents a mathematical model for understanding wave propagation and soliton behavior in biomechanical tissues, explicitly focusing on the Achilles tendon. Utilizing the Korteweg-de Vries (KdV) equation, the research incorporates the Achilles tendons’ nonlinear elastic and viscoelastic properties to explore how mechanical waves propagate through this complex tissue. The tendon’s nonlinear elasticity leads to wave steepening, while its viscoelasticity introduces dispersive effects that counteract this steepening, resulting in the formation of solitons—stable, localized waves that maintain their shape as they propagate. Key findings from this study reveal that the formation and propagation of solitons are strongly influenced by the tendon’s mechanical properties. Numerical simulations show that stiffer tendons, characterized by a higher elasticity modulus, support faster soliton propagation, with wave speeds ranging from 18.9 m/s in damaged tendons to 28.6 m/s in stiffened tendons. Additionally, soliton amplitude increases with tissue stiffness, with the highest amplitude observed in stiffened tendons (5.1 mm) and the lowest in damaged tendons (3.2 mm). The study also demonstrates that energy dissipation due to the tendon’s viscoelasticity plays a critical role in soliton behavior. Damaged tendons exhibit the highest energy loss (18.6%), leading to shorter soliton propagation distances, while stiffer tendons retain more energy (96.1%) and allow solitons to travel further distances (up to 180 mm). Moreover, the balance between nonlinearity and dispersion is crucial for maintaining soliton stability. Excessive nonlinearity leads to unstable solitons, while higher levels of dispersion contribute to more stable waveforms.

Open Access

Article

Exploring human body dynamics to optimize spatial arrangements in interior and landscape design

Ni Yin, Bin Zhang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 532 , 2024, DOI: 10.62617/mcb532

Abstract：

The interplay between human body dynamics (HBD) and spatial design (SD) is a crucial yet often overlooked factor in creating environments that optimize comfort, efficiency, and usability. This paper explores the application of HBD, including biomechanics, posture, gait, and balance, to the design of both interior and landscape spaces. Through analyzing body mechanics and movement patterns, this research aims to provide a framework for designers to create spaces that support natural human movement while reducing physical strain and enhancing user experience. This paper demonstrates how spatial arrangements can be optimized for various activities, user groups, and environments by utilizing gait analysis, motion capture, and force plate testing. Case studies from ergonomically designed office spaces, public transportation hubs, outdoor parks, and residential facilities illustrate the practical benefits of integrating HBD into SD. The research also identifies the limitations of current design practices, such as cost, complexity, and the lack of comprehensive data on diverse populations. Additionally, the paper explores future research opportunities, particularly the role of advancements in artificial intelligence, biomechanics, and wearable technology in creating dynamic, adaptive spaces that respond to user needs. The findings highlight the importance of SD that are visually appealing and aligned with users’ physical and ergonomic needs, ensuring that both interior and landscape environments promote comfort, accessibility, and overall well-being.

Open Access

Article

Enhancing the effectiveness of English grammar teaching through biomechanical feedback and deep learning algorithms

Xueqin Gong, Dongjie Li

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 570 , 2024, DOI: 10.62617/mcb570

Abstract：

This study investigates the integration of biomechanical feedback—targeting posture, gestures, and articulation mechanics—with a Convolutional Neural Network (CNN) to improve the effectiveness of English grammar instruction. Traditional teaching methods frequently overlook the physical aspects of speech production, which are critical for both written and spoken language proficiency. In this study, 94 participants from China were divided into an Experimental Group (EG) receiving biomechanical feedback and a Control Group (CG) receiving traditional instruction. Key findings show that the EG demonstrated significant improvements in grammar accuracy (16.2%), sentence fluency (12.1%), and error reduction (12.3%) compared to the CG, with statistically significant differences ( p < 0.05). The EG reported high satisfaction with the learning process, with 88.3% providing positive feedback on the overall experience. The CNN was instrumental in analyzing linguistic and biomechanical data, enabling personalized feedback that improved participant’ speech clarity, pronunciation accuracy, and grammar retention. These results highlight the potential of integrating physical movement with AI-driven feedback to enhance grammar learning outcomes, offering a more comprehensive and engaging approach to language instruction.

Open Access

Article

Application of social media data mining in biomechanical and tactical analysis of tennis tournament players

Hongmin Yu, Xiaokang Wei

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 425 , 2024, DOI: 10.62617/mcb425

Abstract：

The rise of social media has provided a rich source of real-time data for analyzing player performance and tactics in professional sports, particularly tennis. This study harnesses social media data mining techniques to analyze tennis-related discussions on Twitter, focusing on identifying biomechanical patterns and tactical strategies during major tournaments. We propose a hybrid model combining Bidirectional Encoder Representations from Transformers (BERT) for generating contextual embeddings and Bidirectional Long Short-Term Memory (Bi-LSTM) for analyzing the sequential nature of tweets. The data collection spans tweets discussing key tournaments, including the Australian Open, French Open, Wimbledon, and US Open. It focuses on specific player movements such as footwork, speed, endurance, and tactical decisions like serve placement, net play, and shot selection. Our methodology includes preprocessing the data, tokenizing the text, and applying sentiment analysis to capture public perception of player performance. The model achieves an accuracy of 88.5% and an F1-score of 87.95%, outperforming comparative models such as BERT with CNN and GloVe with LSTM. The analysis highlights key player-specific tactics, including Rafael Nadal’s baseline dominance and Novak Djokovic’s defensive play, as well as tournament-specific strategies, such as serve-and-volley at Wimbledon and baseline control at the French Open. Furthermore, sentiment analysis reveals positive public perception toward player performance, with key emotions such as excitement and admiration frequently expressed during intense match moments. This study demonstrates the effectiveness of applying advanced NLP techniques to social media data for sports analytics. The insights generated can inform players, coaches, and analysts in enhancing performance strategies and understanding public reactions. Using social media data, our approach provides a scalable framework for analyzing tactical shifts and player performance in other sports contexts.

Open Access

Article

Integrating gesture and posture analysis in enhancing English language teaching effectiveness

Zhenqiu Yang, Hongying Yang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 571 , 2024, DOI: 10.62617/mcb571

Abstract：

Non-verbal communication, particularly gestures and posture, is vital in enhancing student engagement, comprehension, and retention in language learning. This study investigates the impact of integrating deliberate gestures and posture into English language teaching, focusing on student learning outcomes. A controlled experiment was conducted with 58 participants (8 teachers and 50 students) divided into Experimental Groups (EG) and Control Groups (CG). Teachers in the EG received training on the effective use of iconic, deictic, metaphoric, and beat gestures and posture awareness techniques, while the CG followed traditional teaching practices. Data were collected through pre-and post-tests, student surveys, classroom observations, and retention assessments. The results demonstrated significant improvements in the EG compared to the CG across all measures. Comprehension scores in the EG increased by 6.62 points, compared to 2.96 points in the CG ( T -statistic = 3.27, P -value = 0.002). Student engagement levels were also higher in the EG, with more frequent participation, higher motivation, and a more substantial influence of gestures and posture on learning ( F -statistic for engagement = 18.27, P -value = 0.002). Additionally, retention of language concepts two weeks after the intervention was significantly higher in the EG, with an 8.50% improvement over the CG (Cohen’s d = 2.01, large effect size). Regression analysis further confirmed that gesture frequency and type strongly predicted comprehension, engagement, and retention improvements.

Open Access

Article

Lower limb movement analysis of different skipping rope modes based on Opensim: A middle-aged demograrphic study

Congjiang Wang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3) , 2024, DOI: 10.62617/mcb190

Abstract：

Introduction: Skipping rope is a popular exercise with various techniques. Understanding lower limb movement variance is crucial for optimizing performance and preventing injuries. Aim: To comprehensively analyze lower limb movement during different skipping rope modes using OpenSim, investigating biomechanical factors at the knee, ankle, and hip joints. The objective is to forcast the possible injuries and determines perception for optimizing the methods of exercise and analysis procedures. Method: The study analyze and evaluate the motions of lower limb in various rope skipping methods like boxer skip, single leg jumps, double-under and crossover jumps to comprehensively analyze the effects of biomechanical. In this research, we employed 56 participants and utilized the kinetic and kinematic data of motion capture model to obtain the data. Statistical analysis was performed to calculate the gathered data. Results: joint moment, joint flexion angle, muscle forces, and maximum joint flexion were thoroughly analyzed by OpenSim. In this research, important variations were examined in biomechanics in lower limb throughput various rope skipping methods. The double-under jumps determined the maximum hip and ankle forces of muscles comparison with other techniques and single leg jumps provided highest angles of knee bending. Boxer skip demonstrated the different types of joints motion and determining load variance mechanisms. Conclusion: The research emphasizes the significance by considering biomechanics in lower limb while demonstrating diverse rope skipping methods.

Open Access

Article

Factors and clinical characteristics of anterior cruciate ligament injury caused by basketball training injury based on multimedia visual images

Yan Bai, Jian Li, Yong Jiang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 196 , 2024, DOI: 10.62617/mcb196

Abstract：

With the coordinated development of China’s national economy and the people’s awareness of the importance of physical and mental health, people have become active in sports, and the development of sports medical care for athletes is particularly important. In training, athletes are likely to have ligament injuries, which makes athletes face rehabilitation, prevention and other problems. This paper describes the joint structure, injury factors and preventive measures of the anterior cruciate ligament, and compares the detection algorithm based on multimedia visual image with the traditional algorithm, focusing on the contrast between the clarity of the cruciate ligament injury image and the image information obtained. The results showed that the algorithm under machine learning improved 16.7% in the clarity of the athletes’ ligaments compared with the traditional algorithm, and the amount of information obtained increased 0.168, which made it better to understand the situation of the basketball players’ cruciate ligaments, so as to better prevent them, providing the best rehabilitation training for basketball players. This paper has a certain reference significance for basketball players in the research of cruciate ligament injury.

Open Access

Article

Innovative machine learning approach for analysing biomechanical factors in running-related injuries

Rui Han, Feng Qi, Hong Wang, Mingnong Yi

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 530 , 2024, DOI: 10.62617/mcb530

Abstract：

Running-related injuries are a significant concern for recreational and competitive athletes, often resulting from complex biomechanical interactions. Traditional injury assessment methods are limited in their ability to capture dynamic, real-time data, necessitating the need for more advanced predictive tools. This study proposes an innovative machine-learning approach to predict running-related injuries by analyzing biomechanical data collected from 84 active runners. The data included joint angles, ground reaction forces, stride length, muscle activation, and foot pressure, captured through wearable sensors during laboratory-controlled and outdoor running sessions. An ensemble model combining Gradient-Boosted Decision Trees (GBDT), Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVM) was developed to predict injury risk. The results indicate that ground reaction force, foot pressure, and stride length were the most significant predictors of injury. The proposed ensemble model achieved an accuracy of 88.37%, outperforming individual models such as GBDT (83.74%) and LSTM (81.29%). The findings suggest that integrating machine learning techniques with biomechanical analysis can significantly enhance the prediction and prevention of running-related injuries. This research offers valuable insights into developing personalized injury prevention strategies, potentially reducing injury occurrence among athletes.

Open Access

Article

Optimization strategy of computer numerical control machining process parameters in biomanufacturing mold

Xiaochun Nie, Qin Gao, Yunling Zhang

Molecular & Cellular Biomechanics, Vol.21, No.3, 21(3), 724 , 2024, DOI: 10.62617/mcb724

Abstract：

With the rapid development of biomanufacturing technology in the medical and pharmaceutical fields, the demand for high-precision and high-quality molds has surged. When Computer numerical control (CNC) machining biomanufacturing molds, the optimization of process parameters becomes the key to improve efficiency and quality. The purpose of this study is to explore the optimization strategy of CNC machining process parameters to achieve the best surface quality, dimensional accuracy and machining efficiency. Through literature review, the spindle speed, feed speed and cutting depth are selected as the key parameters, and the multi-objective optimization model is constructed by response surface method, which is solved by genetic algorithm. The experiment shows that the process parameters of CNC system in mold manufacturing are cutting speed 100 (m/min), feed rate 0.2 (mm/rev) and cutting depth 0.5 (mm), which will effectively reduce the manufacturing cost, and effectively control the alarm times within 35 times in different processing equipment, greatly reduce the risk. The optimization strategy can significantly improve the surface quality and productivity of the mold and reduce the cost. The comparative analysis verifies the effectiveness of the method, which provides new theoretical and technical support for CNC machining in the field of biomanufacturing.