Molecular & Cellular Biomechanics

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

Hongxia Han — 2024-11-08

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.

Virtual reality technology in rural sports sustainable development reform research

Wang Luo — 2024-11-08

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.

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

Pengfei Zhao — 2024-11-08

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.

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

Yingshun Li — 2024-11-11

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.

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

Yan Bai — 2024-11-11

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.

Sports biomechanical analysis of knee joint injuries in table tennis players

Wenyan Li — 2024-11-12

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.

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

Hao Wang — 2024-11-13

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.

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

Xiaojing Ding — 2024-11-14

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.

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

Huimin Peng — 2024-11-14

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.

Optimization design of biomechanical parameters based on advanced mathematical modelling

Yuan Wen — 2024-11-14

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.

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

Benlai Cui — 2024-11-14

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.

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

Jiaxin Zhu — 2024-11-14

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.

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 — 2024-11-14

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.

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

Kang Liu — 2024-11-14

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.

Algorithm for simulating calligrapher’s stroke features using neural networks

Xiaojun Zhu — 2024-11-14

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.

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

Man Jiang — 2024-11-14

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.

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

Ni Yin — 2024-11-14

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.

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

Xueqin Gong — 2024-11-14

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.

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

Hongmin Yu — 2024-11-14

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.

Integrating gesture and posture analysis in enhancing English language teaching effectiveness

Zhenqiu Yang — 2024-11-15

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.

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

Congjiang Wang — 2024-11-15

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.

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

Yan Bai — 2024-11-15

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.

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

Rui Han — 2024-11-15

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.

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

Xiaochun Nie — 2024-11-15

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.

Research on the biomechanical characteristics of basketball player injuries and their application in sports rehabilitation

Xinke Li — 2024-11-18

Basketball is a dynamic sport, characterized by fast moves, powerful hops, and constant changes in direction, which contributes to its great intensity and excitement. However, these same features elevate athletes to a risk of various types, which include the most common ones such as ligament tears, knee problems, and ankle sprains. The knowledge of the risk factors associated with particular movement patterns and environmental conditions is made possible by knowledge of the biomechanical qualities of these injuries is crucial if one has to develop effective preventive and rehabilitation strategies. The purpose of the study is to establish the biomechanical characteristics of basketball player injuries and their application in sports rehabilitation. The study proposed a novel Tunicate Swarm Optimized Flexible Extreme Boosting (TSO-FXGBoost) to predict the injuries of basketball players and their sports rehabilitation. Player’s motion data capture sessions utilize cameras and sensors to record their biomechanics during basketball activities. A Gaussian filter was employed to process the data to eliminate the noise present in the biomechanical data. Principal component analysis (PCA) served as a dimensionality reduction approach to extract relevant features from the pre-processed data. The results demonstrate that certain biomechanical features have a strong correlation with the occurrence of injuries, which indicates great potential in the strategies of prevention of injuries. In a comparative analysis, the suggested approach performs various assessment metrics such as accuracy (98%), recall (96.2%), precision (98.49%), and F1 score (97.8%). The suggested approach and rehabilitation strategies can be customized to each player’s unique biomechanical profile, improving rehabilitation times and lowering the risk of re-injury.

Sports training injury risk assessment combined with dynamic analysis algorithm

Zhihong Hou — 2024-11-18

To explore the application of dynamic analysis algorithm in sports training injury risk assessment, this paper takes the Spatio-Temporal Graph Convolutional Network (ST-GCN) as the main algorithm, and introduces the Adaptive Graph Convolution Module (AGCM) and Residual Channel Attention Module (RCAM). ST-GCN is improved to form AGCM + RCAM-ST-GCN (ARST-GCN) motion posture recognition algorithm. Meanwhile, combined with the extreme gradient boosting (XG Boost), the final physical training injury risk assessment model is formed. The performance of the improved ARST-GCN and the proposed damage risk assessment model is verified by experiments. The results show that ARST-GCN, which combines AGCM and RCAM modules, performs best in all indicators. Compared with ST-GCN, the accuracy rate is increased by 1.94% and the F1 value is increased by 4.3%. In addition, in the performance comparison of different sports injury risk models, the recall rate and F2 value of XGBoost are 0.937 and 0.893, respectively, and the overall performance is the best, indicating that XGBoost has significant advantages in dealing with sports injury risk assessment (SIRA) tasks. The research results provide theoretical basis and practical reference for injury prevention in sports training, and help to improve the accuracy and reliability of SIRA.

The effect of sterilization treatment on the synthesis of key biomolecules and microbial communities in fruit wine fermentation

Meng Li — 2024-11-19

Sterilization treatments impact microbial communities and key biomolecule synthesis, influencing the aroma and quality of fruit wine fermentation. However, the roles of microorganisms in various fermentation techniques and the impact of sterilization treatments on aroma formation and key biomolecule synthesis are not well understood. This study aims to elucidate the effects of sterilization treatments on the synthesis of key biomolecules and microbial community dynamics in fruit wine fermentation, focusing on their relation to aroma compounds. Using purple fruit as the primary subject, we analyzed the starting and final microbe populations in controlled test fermentation (CTF) and natural test fermentation (NTF) under different sterilizing treatments employing advanced sequencing strategies. We utilized multivariate analysis and regression analyses, via the SPSS tool to examine relationships between microbial fungal and bacterial genus-level communities, microbiological diversity, permanent substances aromatic substances, and physiological indexes. In NTF, we identified a total of 150 fungal genera, and 140 bacterial genera, with dominant genera including Candida, Burkholderia, Streptococcus, and Oenococcus. In CTF, 400 fungal genera, and 120 bacterial genera were identified, with the dominant genera being Geotrichum, Pichia, Aspergilus, and Saccharomyces, alongside Streptococcus, Paucibacter, Pantoea, Akkermansia, Lactobacillus, and Bifidobacterium. Positive correlations were observed between specific microbial genera and flavor compounds in both fermentation methods. This study provides insights into how sterilization treatments affect microbial dynamics and key biomolecule synthesis, offering valuable resources for enhancing the aromatic profile of fruit wine.

Design and research of intelligent watt hour meter fault early warning system based on data mining technology

Taorong Wang — 2024-11-19

With the rapid development of information technology and the continuous improvement of communication technology, electric energy meters are innovating and developing towards networking, informatization and intelligence. This research constructs a model design of electric energy failure early warning system based on big data mining technology under the background of big data. It analyzes and studies the intelligent electric energy meter failure early warning system. Through the analysis and comparison of the factors affected, comprehensive performance and application prospects of the intelligent electric energy meter failure early warning system under different data mining technologies, the research results show that the application of big data mining technology makes the heavy work of data collection and data analysis integrate big data mining technology, it can be popularized more widely and applied to more scenarios, thus reducing the manual workload, it makes the work efficiency more significantly improved, and can promote the reform of China’s smart grid more quickly.

Investigating the impact of different loading modalities on bone quality among athletes in various sports

Ding Peng — 2024-11-19

The impact of different loading modalities on bone quality is a crucial area of study for understanding athletic performance and injury prevention. This research investigates how high-impact, low-impact, and resistance training activities influence Bone Mineral Density (BMD), cortical thickness, trabecular number, and stiffness index among athletes from various sports disciplines. A total of 152 athletes from different regions in China were assessed using advanced diagnostic techniques, including Dual-energy X-ray Absorptiometry (DXA), Quantitative Ultrasound (QUS), and Peripheral Quantitative Computed Tomography (pQCT). The study also examines the interaction between demographic factors, such as age and gender, and their effects on bone adaptation. Statistical analyses, including Analysis of Variance (ANOVA) and effect size calculations, were employed to quantify the impact of each loading modality. Results reveal that high-impact sports significantly enhance BMD and bone microarchitecture, showing the highest effect sizes among all groups. Resistance training also demonstrates positive, though less pronounced, outcomes, while low-impact activities contribute minimally to bone development. The findings emphasize the importance of loading intensity and modality for optimizing bone health, providing evidence-based recommendations for athletes, coaches, and healthcare professionals to design effective training programs that enhance skeletal strength and prevent injury.

Developing an optimization model for minimizing musculoskeletal stress in repetitive motion tasks

Ruohan Wang — 2024-11-19

Repetitive motion tasks are widely prevalent in various industries, including manufacturing and office environments, often leading to significant musculoskeletal stress and associated injuries. The continuous nature of these tasks, coupled with improper posture, excessive force exertion, and inadequate rest periods, exacerbates the risk of long-term damage to muscles, joints, and tendons. This paper presents a novel approach to minimizing musculoskeletal stress by developing a Reinforcement Learning (RL)—based optimization model. The model dynamically adjusts real-time task parameters, such as posture, speed, and force exertion, to reduce joint load, muscle activation, and cumulative fatigue while maintaining task performance and productivity. Data was collected from 45 participants performing repetitive tasks in a controlled laboratory environment. Key biomechanical factors, including joint load, muscle activation, and cumulative fatigue, were measured using motion capture, electromyography (EMG), and force plate systems. The RL was trained and validated using this data, with significant improvements observed across all key metrics. The results demonstrated that the model achieved an average reduction of 25%–28% in joint load, 23%–29% in muscle activation, and 26%–28% in cumulative fatigue. In addition, task completion times and accuracy were maintained or improved, demonstrating the model’s effectiveness in balancing ergonomic benefits with productivity. This study provides an integrated approach to reducing musculoskeletal stress while ensuring task efficiency, offering a dynamic, data-driven solution that can be applied across various industries. The findings suggest that RL optimization can significantly improve worker health and task sustainability without compromising organizational performance.

Blind source separation algorithm for biomedical signal based on lie group manifold

Daguang Cheng — 2024-11-19

Independent Component Analysis (ICA) is a powerful tool for solving blind source separation problem in biomedical engineering. The traditional ICA algorithm ignores the Lie group structure of constrained matrix manifold. In this paper, a gradient descent algorithm on Lie group manifold is proposed based on the geometric framework of optimization algorithm on Riemann manifold. Firstly, the orthogonal constraint separation matrices are regarded as a Lie group manifold, and the gradient of ICA objective function on the Lie group manifold is given by using Riemann metric; Secondly, the geodesic equation of the current iteration point along the gradient descent direction is calculated; Finally, a new iteration point is obtained by moving a certain step along the geodesic line, meanwhile, the step length can be adjusted adaptively. Simulation results show that the gradient algorithm on Lie group manifold is feasible for blind Source Separation, and its performance (convergence speed, stability and error) is better than other algorithms.

Biomechanical analysis of balance control in the elderly By Ba Duan Jin

Ye Cheng — 2024-11-19

This study aims to evaluate the biomechanical effects of Ba Duan Jin on balance control in the elderly, seeking effective fitness methods to enhance their balance capabilities and reduce the risk of falls. Methods: Participants were randomly assigned to an experimental group (EG) and a control group (CG), with 25 individuals in each. The Berg Balance Scale (BBS) and mechanical measurements were utilized to evaluate the participants’ balance abilities and biomechanical performance. Statistical analyses were performed to compare the outcomes between the EG and CG, ensuring a comprehensive assessment of the differences. Results: The experimental group demonstrated a significant improvement in the Berg Balance Scale (BBS) scores (p < 0.01), with a notable increase in the eyes-closed standing task (BBS6), which reached significance (p < 0.05), indicating a consistent advantage for the experimental group in this area. Biomechanical measurements revealed that the experimental group exhibited significantly higher parameters compared to the control group, including stability index (EG: 0.7 ± 0.1/2.2 ± 0.4, CG: 0.6 ± 0.1/1.9 ± 0.3), power (EG: 3.2 ± 0.5/3.8 ± 0.6, CG: 2.9 ± 0.4/3.5 ± 0.5), energy expenditure (EG: 20.1 ± 3.8/25.0 ± 4.3, CG: 16.5 ± 3.2/20.3 ± 3.8), and step frequency (EG: 95.0 ± 5.5/105.0 ± 6.0, CG: 85.0 ± 5.0/100.0 ± 5.5). Additionally, peak force (EG: 345.2 ± 30.1/412.6 ± 35.8, CG: 310.4 ± 28.5/310.4 ± 28.5), impact force (EG: 68.3 ± 7.2/85.7 ± 8.9, CG: 55.8 ± 6.7/72.4 ± 8.1), average force (EG: 280.5 ± 25.6/320.7 ± 30.2, CG: 245.3 ± 22.8/280.1 ± 26.4), and direction of force (EG: 10.0 ± 2.0/15.0 ± 2.5, CG: 8.5 ± 1.5/12.0 ± 2.0) also exhibited significant differences (p < 0.05). Notably, the static single-leg stance (EG: 3.38 ± 0.39, CG: 2.38 ± 0.50), dynamic sit-to-stand (EG: 3.77 ± 0.34, CG: 2.85 ± 0.37), turning movements (EG: 3.88 ± 0.27, CG: 2.58 ± 0.56), and double-leg step-ups (EG: 4.00 ± 0.02, CG: 2.69 ± 0.49) displayed extremely significant differences (p < 0.01). These results indicate that Ba Duan Jin training effectively enhances balance control and reduces the risk of falls among the elderly. Conclusion: As a traditional form of physical exercise, Ba Duan Jin effectively enhances balance control and reduces the risk of falls among older adults, providing valuable practical evidence for health management in this population. Future research should focus on conducting more long-term studies with larger sample sizes to verify the applicability and long-term effects of Ba Duan Jin across various age groups and health conditions in older individuals.

Investigation research on the mechanism of knee joint injury in table tennis players landing before and after fatigue during stroke play

Penghui Zhang — 2024-11-19

This study investigated the relationship between lower extremity biomechanics and anterior cruciate ligament (ACL) injury in table tennis players before and after fatigue. We compared the biomechanical changes in the lower limbs of table tennis players during landing after completing a chasse-step while stroking, both before and after fatigue. A further aim was to examine ACL injury and provide a reference for training table tennis players. Ten national Level I table tennis players underwent lower extremity neuromuscular fatigue by running at a constant speed. Biomechanical data of the athletes were collected before and after fatigue. The effects of movement and characteristic time before and after fatigue on biomechanics were determined using a paired sample t-test. After fatigue, the angle of the ankle joint and the range of motion of the knee joint were significantly reduced (p < 0.001), while the angle of motion of the hip joint did not change considerably (p = 0.747). The angular velocity of the ankle and knee joints increased significantly after fatigue (p < 0.001), but the angular velocity of the hip joint decreased significantly (p = 0.013). Additionally, the ankle plantar flexion moment (p = 0.003), knee flexion moment (p < 0.001), and hip flexion moment (p < 0.001) increased significantly after fatigue. The ankle power (p = 0.023), knee power (p = 0.009), and hip power (p < 0.001) were significantly reduced throughout the landing cycle after fatigue. Fatigue in table tennis athletes reduces the sagittal plane buckling angle of the knee and ankle joints during landing. This change increases ground reaction and knee joint forces, significantly elevating the risk of knee injuries, including ACL tears. The reduced flexion angle exposes the knee to greater torque and diminishes its shock absorption capacity, heightening the risk of lower limb injuries. These findings underscore the need to address the impact of fatigue on landing mechanics in sports training and rehabilitation, emphasizing preventive measures.

Research on the application of biomechanics analysis in optimizing physical education movement techniques

Ming Li — 2024-11-19

College and university students’ general health, fitness, and well-being are greatly enhanced by physical education. As various educational institutions work to improve the efficacy of their physical education programs, more evidence-based techniques are required. Biomechanics, to the movement or structure of student activities, provides insights into the efficiency and effectiveness of physical movements. This study aims to explore how physical activity movement skills can be systematically improved by the use of biomechanics analysis, leading to improved physical results and increased student participation in sports and fitness activities. In this study, a novel synergistic fibroblast-optimized malleable convolutional neural network (SFO-MCNN) is proposed to enhance teaching practices using a biomechanical framework that integrates movement analysis. The data collected from cameras that record students’ movements, capturing joint angles and body positions, as well as data from sensors are gathered from the Kaggle. The data was preprocessed using data cleaning and normalization for the obtained data. A system for assessing instruction quality was created using the suggested model and improved SFO. The findings show that the proposed algorithm has the greatest evaluations for average skill performance, physical fitness, student happiness, and physical education teaching efficiency. By comparing the outcomes with those of conventional approaches, the effectiveness of the proposed framework in improving physical education teaching techniques has been established.

Exploration of the biological basis and training strategies for building athletes’ psychological resilience

Guangmin Li — 2024-11-19

Background: Psychological resilience (PR) is crucial for athletes to perform at their best and recover from failures. However, its biological basis and effective training strategies remain under-explored. Many athletes face psychological challenges that affect their overall well-being and performance. Furthermore, resilience training can benefit from a greater awareness of the biomechanical components of athletes’ performance. To help athletes achieve their best, trainers and sports psychologists must understand resilience’s molecular principles and develop effective training strategies that integrate psychological and biomechanical components. Objective: The study aims (1) to explore the resilience profiles of athletes categorized as high resilience group and moderate resilience group, (2) to assess the biomechanical characteristics associated with these resilience profiles through sensor data, and (3) to determine the relationships between resilience, health-related behaviors, well-being, and perceived social support. Methodology: A cross-sectional online survey with 120 participants was conducted to assess health-related behaviors and well-being pre- and post-intervention. Additionally, biomechanical sensors were used to capture athletes’ movement and performance during training sessions. Data was collected using the social support scale and resilience scale. Descriptive statistics, ANOVA, and correlation analyses were used to analyze the data. Qualitative interviews with athletes provided additional insights into the relationship between resilience profiles and associated characteristics. Result: The findings revealed that athletes in the high resilience group exhibited significantly healthier lifestyle choices, greater overall well-being, and higher perceived social support compared to the moderate resilience group. Correlational analyses showed stronger relationships between resilience scores and associated characteristics in the high resilience group, suggesting that biomechanical efficiency contributes to their resilience. Conclusion: The characteristics and correlates of resilience profiles in athletes can advise tailored interventions to influence PR and biomechanical performance, benefiting their performance and mental health.

Optimization research on biomechanical characteristics and motion detection technology of lower limbs in basketball sports

Weidong Cheng — 2024-11-19

In basketball, the biomechanics of the lower limbs play a significant role in executing specific movements like sprints, jumps, and directional changes. Optimizing the performance of these movements is necessary for enhancing overall athletic performance and reducing injury risks. The objective of the research is to generate and execute a motion detection algorithm focusing on lower limbs in basketball utilizing a deep learning (DL) based approach. The study proposes the Refined Harries Hawks optimized Intelligent Long-Short Term Memory (RHH-ILSTM) method to improve the accuracy of detecting and analyzing biomechanical characteristics of lower limb movements. Data collection involved basketball players equipped with wearable sensors on their lower limbs to gather on-time data throughout dynamic movements to train the method. The data is pre-processed to remove noise, normalize values, and segment movements into discrete time intervals. Principal Component Analysis (PCA) is utilized to extract characteristics by reducing the dimensionality of the data while maintaining significant biomechanical aspects. The RHH-ILSTM system combines the exploration capabilities of the RHH optimization algorithm with ILSTM’s capacity to handle time-series data, leading to improved detection accuracy of lower limb biomechanics. The model efficiently captures crucial lower limb biomechanics, achieving a higher accuracy (94.58%) and recall (95.62%) in detecting movement phases and joint stresses. The proposed RHH-ILSTM method provides a robust solution for monitoring and analyzing lower limb movements in basketball.

Biomechanical analysis and rehabilitation strategies of common lower limb injuries in sprinters

Zhenzhu Hao — 2024-11-19

Lower limb injuries are established among sprinters, often ensuing from repetitive pressure and biomechanical disorganisations. Considering these injuries’ mechanisms and emerging actual rehabilitation approaches are crucial for enhancing performance and preventing repetition. This research aims to evaluate the biomechanical influences associated with common lower limb injuries in sprinters and measure the efficacy of targeted reintegration strategies. A randomized sample of 56 sprinters with a history of lower limb injuries participated in this research. An inclusive biomechanical analysis was employed using 3D motion capture technology to assess running mechanics. Variables such as joint angles, ground reaction forces, and patellar chasing were restrained. Biomechanical analysis convoluted evaluating running form and recognizing specific movement outlines linked to injuries. Based on these assessments, modified rehabilitation strategies were designed, including strength training, flexibility exercises, and real-time response to running technique. The logistic regression analysis revealed important associations between specific biomechanical deviations and injury occurrence. Post-rehabilitation assessments indicated developments in joint function, reduced pain levels, and enhanced running performance among participants. Implementing a biomechanical analysis mutual with personalized rehabilitation strategies effectively reduces lower limb injuries in sprinters, promoting better performance outcomes.

Analysis of a difficulty movement of aerobic gymnasts based on biomechanics

Zhihua Yu — 2024-11-19

Background: With the progress of technology, an increasing number of studies on biomechanics in sports are being conducted. Objective: Based on biomechanical methods, the characteristics of aerobic gymnasts at different skill levels performing the difficulty movement C715 were compared to provide a reference for teaching and training. Methods: Sixteen aerobic gymnasts were divided into two groups, A and B, according to their skill levels. Kinematic data and surface electromyography were collected using modern equipment, analyzed, and compared. Results: Group A spent 0.64 ± 0.03 s during the upright restoration stage, which was longer than that of group B (p < 0.05). In the pre-swing stage, the angle between the legs of group A at the moment of free leg landing was 46.52 ± 2.14°, which was significantly greater than that of group B. Muscle force was predominantly exerted on the right side, and there were obvious differences between group A and group B in the integral electromyogram (IEMG) of the right rectus abdominis, left and right gastrocnemius, and left rectus femoris (p < 0.05). During the phase when the free leg swings forward, no significant differences in kinematic characteristics were found between the two groups; however, there were significant differences in IEMG of muscle activity for the right rectus abdominis, left and right biceps femoris, right gastrocnemius, and left rectus femoris (p < 0.05). The right gastrocnemius force of group A reached 25.77 ± 3.64 μV·S, which was significantly higher. During the 360° leg-controlled rotation phase, the minimum angle of the right ankle in group A was significantly greater than group B (p < 0.05), and the muscle activity showed significant differences in IEMG of the left biceps femoris, right biceps femoris, left gastrocnemius, left rectus femoris, and right rectus femoris. In the upright stage, the knee and ankle angles of group A were larger, and there was a remarkable difference in gastrocnemius force (p < 0.05). Conclusion: There are biomechanical differences among aerobic gymnasts of varying levels when performing the difficulty movement C715.

Dynamic evaluation of community health services and health quality based on biomechanical time series

Xingken Liu — 2024-11-19

Background: Evaluating community health services and fitness exceptional is important for identifying areas of development and making sure powerful healthcare shipping. Biomechanical assessment, especially reading joint movements, provides valuable insights into people’s fitness popularity and useful capabilities. Although biomechanical time-collecting information has terrific promise, there is not much thorough research that comprises those parameters in population health checks. Aim: This study evaluates the dynamic relationship between biomechanical time series data of joint movements and community health quality metrics. It also finds important factors for good health and gives practical advice for improving community health services. Methods: The study utilizes a biomechanical time series dataset from Kaggle. The collected time series data was preprocessed using Z-score standardization to ensure comparability. Gated Refined Long Short-Term Memory (GRLSTM) networks were employed for tasks due to their ability to capture long-term dependencies and temporal relationships inherent in time series data. Results: Statistical analyses such as regression and ANOVA were conducted to explore relationships between joint movement patterns and health quality predictors. The GRLSTM indicates significant associations between specific joint movement patterns and health quality indicators. Regression analyses confirmed key predictors of health quality, while ANOVA demonstrated significant differences in joint movement patterns among different health quality groups. The GRLSTM model demonstrates exceptional performance, with 94% precision and 95% recall rates, an accuracy of 98% and a robust F1-score of 96%, indicating a strong equilibrium between recall and accuracy. The ANOVA shows joint angles as the strongest predictor (p < 0.001). The regression analysis identifies stride length (β = 2.30, p < 0.001) as the strongest positive predictor. Conclusion: This observation emphasizes the importance of incorporating biomechanical assessments into community health reviews, highlighting the capability of GRLSTM networks and predictive analytics in improving fitness satisfaction and healthcare strategies.

Research on biomechanics integrated Bayesian network mental health diagnosis system

Shenghong Dong — 2024-11-19

With the rapid economic development of various countries around the world and the acceleration of global networking, countries are striving to promote their own urbanization and industrialization progress. The side effect is that social pressure leads to the concentrated outbreak of various social contradictions. The main psychological health testing and evaluation method in society is still conducted through dialogue with psychologists. Doctors obtain information through dialogue and communication with patients, and diagnose their psychological status based on this information. Affected by factors such as communication style and the patient’s own mental state. The information obtained may result in omissions and biases, leading to inaccurate diagnostic results. Bayesian network is a probabilistic graphical model that derives the results through information calculation. It can analyze and calculate the finite and incomplete conditions, carry out corresponding reasoning, and obtain more rigorous results. This article applied the naive Bayesian algorithm to the research of mental health diagnosis systems, and compared it with mental health diagnosis systems that do not use algorithms. According to the mental health index of contemporary people, the algorithm achievement test experiment of mental health diagnosis system was carried out. After research and comparison, it was found that for the collected data, the maximum accuracy of the Naive Bayesian algorithm within a hundred calculations reached 99%, with a mean of 96.5%. The traditional paper-based psychological diagnosis method had a maximum accuracy of 89%, a minimum of 70%, and an average accuracy of 80.5%. Therefore, the application of naive Bayesian network to the development and research of mental health diagnosis system can effectively improve the efficiency, accuracy and diagnostic effect of mental diagnosis.

Research on quantitative measurement algorithm for e-commerce customer loyalty based on deep learning algorithm

Sian Chen — 2024-11-19

Traditional algorithms cannot fully explore the potential patterns behind big data, lack personalized customer analysis, and cannot provide personalized services and suggestions for different types of customers. This article employs the Bi LSTM (Bidirectional Long Short-Term Memory) model to accurately capture the complex features and patterns of customer behavior, thereby improving the measurement accuracy of customer loyalty. Collect data on customer behavior, browsing history, and search behavior, and preprocess the collected data. Organize customer behavior data into a time series dataset in chronological order, and divide it into weekly windows to extract feature information from the data. Construct a bidirectional LSTM model while considering the forward and backward information of the sequence data, in order to more comprehensively capture the contextual relationships in the sequence data and quantify customer loyalty. The experimental results show that the average accuracy of Bi LSTM in predicting average customer loyalty is 97.1%. And it can effectively improve the prediction effect of repeat purchase rate. The application of Bi LSTM can accurately quantify customer loyalty in e-commerce, provide reference for enterprise decision-making, formulate corresponding marketing strategies and customer management plans, and improve customer loyalty and competitive advantages.

Intelligence-assisted college English teaching: The application of artificial intelligence technology in personalized learning path design

Rong Jiang — 2024-11-19

English learning is an integrated learning process in which listening, speaking, reading, writing and other parts are interrelated. Because of this feature of English learning, higher requirements are put forward for the design of English learning assistance systems. On the other hand, the wide application of advanced science and technology such as automation and cloud computing in all walks of life has promoted the development of society into the era of artificial intelligence. Under such a large development background, how to build a multifunctional integrated English learning system to realize the personalized learning of learners with different levels and different needs has become a focus of current English teaching research. With the increasing abundance of English learning materials, it has become very important to help users find suitable English learning resources from the massive materials, which directly affects the cost of learning time and interest of users. Based on this, this paper introduces a multi-similarity ranking model to achieve personalized design, and then uses artificial intelligence technology to design and implement a personalized intelligent assistant system for college English teaching.

The real-time application and effectiveness assessment of an intelligent physical fitness testing system in physical training

Haojiang Jing — 2024-11-19

An intelligent physical fitness testing system leverages advanced technologies to monitor and evaluate individuals’ fitness levels accurately. It integrates real-time data acquisition, and analysis, to support personalized physical training and health management. This study aims to evaluate the practical application and effectiveness of an intelligent system for real-time physical fitness testing in the context of physical training. Our suggested model employs portable sensing devices and we proposed a novel Northern Goshawk optimization-driven Gate Customized Long Short-Term Memory (NG-GC-LSTM) for enhancing accuracy in evaluating the individuals’ physical fitness levels. Data acquisition involves gathering bio-sensing data from 25 individuals during diverse physical training activities. The Min-Max Scaling algorithm is utilized to pre-process the obtained sensor data. We employed a Short-Time Fourier Transform (STFT) for extracting crucial features from the processed data. In our proposed framework, the NG optimization algorithm iteratively fine-tunes the GC-LSTM architecture for the accurate evaluation of an established intelligent physical fitness testing system. The recommended model is executed in Python software. During the result analysis phase, we assess the efficacy of our model’s performance across a variety of parameters. Additionally, we conduct comparative analyses with existing methodologies. The obtained outcomes demonstrate the efficacy and superiority of the suggested framework.