MCBOpen Access

Open Access

Article

Application of wearable nano biosensor in sports

Lian He, Shihao Han

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 165 , 2024, DOI: 10.62617/mcb.v21i1.165

Abstract：

Biosensor is a new type of detection and analysis device. Because of its sensitivity, accuracy, ease of use and the ability of online and in vivo monitoring, it can be applied to all walks of life. Biosensors have a broad market in the field of sports science, which can be used for timely monitoring of sports training, and would also become an important method and technology in sports education and sports research. First of all, through consulting a large number of literature and practical research methods, the main body of the article was studied. In the introduction, the first paragraph introduced the background and leaded to the following, then summarized the research direction of scholars on sports and wearable nano biosensors, and finally made a summary; in the second part, the model of sensor related utilization algorithm was established, and various algorithms were proposed as the theoretical basis for the research on the application of wearable nano biosensors in sports; then it described the factors of nano biosensor and application in sports; finally, combined with the method part, the comparative experimental analysis of nano biosensors in the sports prospect was carried out. The results showed that the effectiveness of the algorithm model for the development of sports was improved by 7.83%.

Open Access

Article

Rehabilitation training of hamstring injury in athletes training hamstrings based on BP neural network algorithm

Yukun Chu, Jia Xu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 183 , 2024, DOI: 10.62617/mcb.v21i1.183

Abstract：

Athletes are prone to injury during daily training and competition. In order to achieve better results, they are subjected to heavy training every day, who challenge the limits of their bodies. Excessive exertion, inattention, and irregular movements may all lead to muscle strains in athletes. The hamstrings, consisting of the biceps, semitendinosus, and semimembranosus, are susceptible to injury. Traditional research on hamstring injury rehabilitation training focuses on the prevention of muscle strains and the restoration of muscle elasticity. However, traditional training methods are often unable to make targeted adjustments to each athlete’s specific situation. The actual application effect is not good. In order to improve the effectiveness of rehabilitation training for hamstring injury, this paper has introduced the BP neural network algorithm model. Based on the BP (Back Propagation) algorithm model, this paper has conducted an in-depth analysis of the causes of muscle strain in athletes. The results showed that the average accuracy of the algorithm was 97.83%, which had a high accuracy for the analysis of the cause. Muscle strain rehabilitation training methods were further analyzed. Research showed that the BP neural network algorithm could optimize up to 31%, and the effectiveness was above 96%. In the comparison of these two methods, it can be clearly seen that the algorithm in this paper is more scientific and efficient, which is conducive to better and faster recovery of the injured hamstrings of athletes.

Open Access

Article

Impact of early weaning and nutritional interventions on growth performance, digestive metabolism, and serum biomarkers in lambs: A biomechanical perspective

Shengdong Li, Zanariah Binti Hashim

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 262 , 2024, DOI: 10.62617/mcb.v21i1.262

Abstract：

The aim of this study was to investigate the effects of early weaning and nutritional interventions on growth performance, nutrient digestion and metabolism, and serological indices of hu sheep lambs, with a view to providing a basis for the feasibility of early weaning of hu sheep lambs under conditions of supplemental milk replacer. Ninety neonatal hu sheep lambs were weaned at 21 days ( n = 60) (divided into two groups: early weaned, EW; and resveratrol-fed, RSV) as well as 49 days ( n = 30) (control group, CON) weaning, and the trial period was 90 days. The results showed that: 1) there was no significant difference ( P > 0.05) in the initial and final weights of the lambs, and the average daily gain (ADG) of the EW and RSV groups was significantly lower than that of the CON group ( P < 0.05) from 31 to 45 days of age. Early weaned lambs were more susceptible to weaning stress compared to late weaned lambs and the effect of nutritional intervention (feeding resveratrol) on lamb growth performance was not significant. 2) The apparent digestibility of crude protein (CP) in the experimental group was significantly lower than that of the control group ( P < 0.05), and the nitrogen intake, net protein utilization and protein biological value indexes of lake lambs in the experimental group were significantly different from those of the control group ( P < 0.05). 3) Early feeding of resveratrol was not significant in improving the digestive metabolism of nutrients. 4) Early weaning as well as the addition of resveratrol had a significant effect on the serum GLU and TC indexes of the lambs ( P < 0.05), but did not show a significant effect on any of the other indexes.

Open Access

Article

Using wearable technology to optimize sports performance and prevent injuries

Zehao Yang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 305 , 2024, DOI: 10.62617/mcb.v21i1.305

Abstract：

Purpose: Wearable devices, as emerging computing platforms, have gradually penetrated into people’s daily life, especially in the field of medical health management showing excellent potential. Methods Motion state recognition is performed by deep fusion CNN-LSTM model, CNN is used to obtain the most representative feature information characteristics of the local space of the motion data, while the LSTM layer is used to capture the long-term temporal correlation of these local features, and both of them are combined to obtain the more representative temporal-spatial correlation transportation state feature information implicit in the wearable gait data. An injury prevention method for exercise example parameters is designed, including patient training load characterization, and a Bi-LSTM network structure is used to design lightweight acceleration features to predict abnormalities in exercise physiological indicators. Results: Monitoring parameters such as heart rate rise slope, 1-minute heart rate recovery value, blood oxygen drop area, and 1-minute oxygen saturation recovery value, the false alarm rate of wearable device health data warning were kept at 2.55%. After exercise status and detected abnormalities in physiological parameters, personalized breathing training was performed, and the contribution ratio of abdominal breathing increased by 27% after training, and the patient’s heart rate decreased by 8.5 bpm and oxygen saturation increased by 2.4% compared to the pre-training period. Conclusion: The methodology in this paper can be more comprehensively optimized for sports performance and injury prevention, and is widely applicable in practical applications.

Open Access

Article

Effects of breathing exercises on young swimmers’ respiratory system parameters and performance

Germans Jakubovskis, Anna Zuša, Jelena Solovjova, Behnam Boobani, Tatjana Glaskova-Kuzmina, Juris Grants, Janis Žīdens

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 205 , 2024, DOI: 10.62617/mcb.v21i1.205

Abstract：

Breathing exercises are widely used to enhance respiratory function and athletic performance. This study aimed to assess the efficacy of a modified exercise regimen on respiratory parameters and its effect on the performance of young swimmers in competition. Thirty-one swimmers aged 16–17 from various clubs in Latvia were selected, comprising an experimental group ( n = 15, height: 174.36 ± 7.85 cm, weight: 65.80 ± 9.35 kg, body mass index: 21.60 ± 1.54) and a control group ( n = 16, height: 180.78 ± 7.05 cm, weight: 69.90 ± 6.49 kg, body mass index: 21.40 ± 1.56). With an average of eight years of experience, participants trained for approximately 43–45 weeks annually (pool and gym sessions), with an average training duration of 20 ± 2 hours per week. Measurements were conducted on days one and 30, involving spirometry and swimming performance assessment based on the best results in the freestyle 100-meter distance. The experiment consisted of a modified breathing exercise performed thrice weekly for four weeks. Significant improvements were observed in the experimental group compared to the control group in forced vital capacity ( p = 0.02), peak inspiratory flow ( p = 0.001), and performance ( p = 0.001), with p -values < 0.05. However, no significant changes were noted in peak expiratory flow ( p = 0.46 > 0.05). The findings indicate that modified breathing exercises effectively enhance respiratory parameters and performance in competitive swimmers.

Open Access

Article

A biomechanics research on drug therapy in the rehabilitation of sports training injuries

Lulu Yang, Yu Dong

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 215 , 2024, DOI: 10.62617/mcb.v21i1.215

Abstract：

In the process of sports training and competition, if sports injury occurs, it not only has a serious impact on the physical and psychological of athletes, but also has a negative impact on the overall training and competition. During the competition, if effective treatment is not obtained, it can have an irreversible impact on athletes’ future participation in sports. Nowadays, many athletes lack knowledge on the prevention and treatment of sports injuries during physical exercise. Therefore, it is urgent to conduct research and analysis on the treatment methods for training injury rehabilitation. In this paper, the visual analogue scale (VAS) and cell landscape analysis technology were used to analyze and compare the therapeutic effects of platelet rich plasma (PRP) injection technology, diclofenac, indomethacin, Qili San and placebo tablets. Focusing on how these drugs promote rehabilitation by affecting mechanical parameters and tissue mechanical properties, it is concluded that PRP injection technique, diclofenac and indomethacin improve the treatment effect of pain after rehabilitation of sports injuries. The number of people who completely recovered with Indomethacin was 4 and that with placebo was 2. The results show that these drugs significantly improve the mechanical properties of the tissue and contribute to the rehabilitation process. This study has further explored the drug treatment of sports injury, explored several drug treatment mechanisms and efficacy and has guiding significance for subsequent drug treatment of sports injury.

Open Access

Article

Application of scalable sensor-assisted multi-scale computational methods in the simulation of micro mechanical behavior of composite materials

Pan Wang, Peijin Liu, Wen Ao

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 307 , 2024, DOI: 10.62617/mcb.v21i1.307

Abstract：

Micro mechanics involves the examination of the mechanical behavior of heterogeneous materials, taking into account inhomogeneities such as voids, fractures, and inclusions, and building on mathematical models developed. Composites bring engineering design barriers despite their strengths. Composite manufacturing and processing need specialized equipment, strategies, and labor, ensuring they are challenging and expensive. Mechanical behavior deals with how a composite material performs if faced with mechanical effects and action. Scalable sensor-assisted multi-scale computational Methods (SS-MSCM) are used to investigate topics ranging from the molecular basis of soot production in combustion to how molecule-level flaws influence macroscopic mechanical qualities. Carbon fiber reinforced polymers (CFRP) are generated by mixing graphene fiber with a resin, like vinyl ester and epoxy, to render a composite material with superior performance to the component ingredients. Hence, SS-MSCM-CFRP has Improved mechanical qualities achieved by incorporating nano-reinforcements, including carbon nanofibers and graphene nanoplates, into the CFRP matrix: enhanced flexural and compressive strengths, energy absorption upon impact, toughness to fracture, and interlaminar bonding. Composite materials feature excellent mechanical qualities like high strength and stiffness, fatigue resistance, and durability. It is possible to insert scalable sensors throughout the manufacturing process, which enables real-time monitoring of structural health, strain, and other factors. Scalable sensor-assisted multi-scale computational methods offer enhanced accuracy, real-time monitoring, and cost-effectiveness by integrating sensor data with computational models, improving predictions and failure mechanism insights. However, they face limitations like sensor dependency, computational complexity, data integration challenges, and high implementation costs, leading to potential discrepancies between simulation and experimental results. Important qualities include corrosion resistance, thermal conductivity, and electrical conductivity. As the composite materials develop to satisfy the established mechanical stress and temperature conditions, they offer high durability and strength.

Open Access

Article

Enhancing hotel efficiency and environmental health with biosensors and big data analytics

Boyang Shu, Xianbing Ruan, Pan Li

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 336 , 2024, DOI: 10.62617/mcb.v21i1.336

Abstract：

The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

Open Access

Article

Rotational movement of Chinese dance based on the analysis of kinematic mechanics

Yang Liu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 128 , 2024, DOI: 10.62617/mcb.v21i1.128

Abstract：

Objective: Rotation is a fundamental movement in Chinese dance, and many dancers are difficult to control the smoothness and stability of their movements during rotation, resulting in low completion rates. To better grasp the key points of rotational movements, this article started from a biomechanical perspective and studied the differences in completing rotational movements among dancers of different skill levels. Methods: Twenty dancers were divided into group A (high level) and group B (ordinary level). The dancers’ movements were recorded using two Casio cameras and analyzed using the APAS System. The center of gravity displacements, joint angles, and other kinematic mechanical indicators were compared between the two groups. Results: Group A demonstrated faster completion for a 360° rotation than group B. Group A exhibited larger torso angles during the preparation stage, rotating 180° and rotating 360°, with values of 84.67° ± 1.36°, 85.41° ± 0.65°, and 84.91° ± 0.78°, respectively. Significant differences in hip and knee angles were also observed at each stage in group A ( p < 0.05). Furthermore, when rotating 1080°, group A consumed the longest time in the first lap, followed by the second and third laps, and had more stable center of gravity displacement and velocity. Conclusion: Group A shows superior body control and higher rotation quality when performing rotational movements in Chinese dance.

Open Access

Article

Construction of measurement index system of basketball players’ specific physical fitness training based on AI intelligence and neural network

Xiaoning Yang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 250 , 2024, DOI: 10.62617/mcb.v21i1.250

Abstract：

The development of modern basketball has improved the ability of basketball to compete, and the competition is becoming increasingly intense. Both in attack and defense, they are more active and fiercer. Therefore, higher requirements have been put forward for the physical fitness of basketball players. If good physical fitness cannot be guaranteed, the development of various sports skills will become very difficult. In the actual training of basketball, the specialized physical training of basketball players has received widespread attention and is regarded as the main purpose and way to develop basketball. The status and role of specialized physical training for basketball players in physical education are receiving increasing attention, and specialized physical training has also attracted the attention of coaches. It is necessary to use a sports measurement index system as an objective basis for the testing and evaluation of athletes’ specialized physical training. It is particularly important to improve the training level and establish a scientific and reasonable comprehensive quality evaluation index system for basketball players. Based on neural networks, this article constructs a specialized physical training index system for basketball players and studies the measurement of specialized physical training for basketball players. The experimental data was collected from 100 outstanding basketball players and analyzed using a neural network model. Based on a combination of agility, strength, and endurance tests, the model successfully predicted a 6.68% improvement in performance for special physical training. The method used in this article employs advanced machine learning techniques, and the results demonstrate the potential of neural networks in sports science research.

Open Access

Article

Application of artificial intelligence in the development of personalized sports injury rehabilitation plan

Chao Zhan

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 326 , 2024, DOI: 10.62617/mcb.v21i1.326

Abstract：

Sports injury rehabilitation is a kind of physical treatment used to address musculoskeletal system disorders, injuries, and discomfort in patients of all ages. Sports rehabilitation promotes health and fitness, aids in injury recovery, and lessens pain through movement, exercise, and physical therapy. During a sports injury, rehabilitation has developed into a specialized profession that has gradually brought together sports physicians, sports physiotherapists, and orthopedic surgeons. Finding the best ways to minimize recovery time, avoid injuries, and enhance performance is crucial for sports athletes. The aim of this research is to establish a personalized sports injury rehabilitation evaluation system enabled by artificial intelligence (AI). In this study, a novel advanced penguin search optimized efficient random forest (APSO-ERF) has been proposed for sports injury athletics exercise rehabilitation. This study used exercise movement image data to develop personalized sports injury rehabilitation. The data was preprocessed using a Wiener filter for noise reduction and image restoration. Convolutional neural networks (CNN) are used to extrapolate top-level characteristics from images. The proposed method is used to evaluate physical rehabilitation by assessing patient performance during the completion of prescribed sports injury rehabilitation exercises. The proposed method is compared to other traditional algorithms. With 97.80% accuracy, 96.01% sensitivity, 97.90% specificity, 98.88% precision, 96.11% recall, and 97.50% F1-score, the APSO-ERF approach beats conventional algorithms in tailored sports injury rehabilitation. The result illustrated that the proposed method achieved high performance in the accuracy of sports injury athletics exercise rehabilitation.

Open Access

Article

Longitudinal dynamic study on minority college students’ physique using physical fitness systems

Shijun Xu, Yuanyuan Qiao, Chuanyan Guo, Ling Li, Jie Kan

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 276 , 2024, DOI: 10.62617/mcb.v21i1.276

Abstract：

The physical health of Chinese ethnic minority college students is often concerned, but there is limited research on continuously and effectively monitoring physical health and screening out groups with inadequate physique. This study mainly proves an effective method for screening out normal and unqualified physique groups through longitudinal and dynamic physique investigation. This study tracked Chinese college students’ physique changes during a 4-year physical-fitness system. Using body mass index combined with test results of lung capacity, standing long jump, Sit and Reach, and the 50-m run to evaluate the physical health of students. Calculate the proportion of individuals of different BMI (Body Mass Index) types to the total number of sampled individuals. Using SPSS 22.0 and T-test to examine the differences in physique among 3314 senior students. The quantitative data were expressed as mean ± standard deviation. Physical health is a dynamic process of change. The BMI combinations of 772 minority senior students were divided into four types. Type 1 ( n = 117): normal as freshmen but unqualified as seniors. Type 2 ( n = 149) was unqualified in both freshmen and seniors. The items with decreased physical fitness for Type 1 and Type 2 were: boys’ lung capacity and 50-m run. Type 3 ( n = 72) was unqualified in freshmen and normal in seniors. The items with decreased physical fitness were girl’ 50-m run. Type 4 ( n = 434) was normal in both freshmen and seniors. The items with decreased physical fitness were: boys’ lung capacity and 50-m run. A four-year longitudinal dynamic survey combining BMI with physique can effectively monitor students’ physical health. Minority students’ proportion of normal BMI has been declining, the main contributors are boys. Girls pay attention to weight management, their physique approaches that of the Han, even surpassing Han in Sit and Reach, and 50-m run. However, boys neglected weight management and their BMI significantly increased year by year. Their overall physical fitness is lower than that of the Han, especially with lung capacity far lower than Han, and their physical health is a key focus of physique monitoring.

Open Access

Article

Using biosensors and machine learning algorithms to analyse the influencing factors of study tours on students’ mental health

Kunfeng Li

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 328 , 2024, DOI: 10.62617/mcb.v21i1.328

Abstract：

College students nowadays will inevitably deal with stress. Personal emotional and behavioural responses may be extremely strong when faced with stress. One of the most prevalent sources of stress for college students throughout the globe is mental health issues connected to stress. However, there is a lack of research focusing on the impact of specific activities, such as study tours, on students’ mental health and how these activities can be monitored using advanced technologies. As a result of its ability to analyze, classify, and alert college students’ psychological data with high quality, deep learning and machine learning have recently found widespread application in college students’ mental health education and management. Moreover, the integration of biomechanics and biosensor data offers new insights into understanding the physical and psychological impacts of study tours on mental health. This can potentially promote the development of colleges’ mental health education programs. Hence, this study proposes the Biosensor-based and Deep Neural Network-based College Student Mental Health Prediction Model (BDNN-CSMHPM) for detecting the mental stress of college students during study tours. Using biosensor data, including EEG and biomechanical metrics, this model employs the most effective BDNN to categorize the mental health condition as normal, negative, or positive. Consequently, BDNN is utilized to categorize the gathered emotional and biomechanical information, and based on the classification outcomes, the emotional condition of college students is determined. Considering that different features might stand in for different elements in the original data, it is necessary to extract several biosensors features to represent the information in the original EEG data accurately. Second, fusing various features is essential in the auto-learn model integration method. Third, the BDNN is fed the combined features, resulting in emotion classification. The numerical outcomes demonstrate that the BDNN-CSMHPM model enhances the student’s mental health prediction ratio of 98.9%, accuracy ratio of 96.4%, emotion recognition ratio of 95.3%, Pearson correlation coefficient rate of 97.2% and psychological monitoring ratio of 94.3% compared to other popular methods.

Open Access

Article

Mechanobiological mechanisms in the remediation of soil lead pollution using two-dimensional carbon materials and Morchella: A molecular-level study

Hui Jia, Yali Kong

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 311 , 2024, DOI: 10.62617/mcb.v21i1.311

Abstract：

Graphene-based 2D carbon composites and Morchella mushrooms are used in the paper to study the mechanobiological mechanisms of soil lead remediation. Soil is contaminated with lead, which threatens ecosystems and human health, making it even more important to find remedies. To fully comprehend these processes, present-day materials, and organic compounds are needed to improve soil nutrition and eradicate lead. To deal with the challenge of finding effective means of lead removal; limitations associated with traditional soil pollution remediation methods; and merging biology and material sciences. Multifunctional graphene wettability-patterned nanocoated membranes (MGW-PNM) is a novel technique developed to overcome these challenges. Such processes result in membranes with different wettability patterns that take advantage of the properties of graphene. This facilitates better interaction between the membrane itself and the surrounding soil as well as lead contaminants by modifying its hydrophobicity or hydrophilicity characteristics. For effective removal of lead, extensive simulation studies were done using MGW-PNM. In line with this, it can be inferred that MGW-PNM also remediates highly capable soils at high-efficiency levels. This was established when comparing modern techniques to past ones where considerable improvements were made on how much lead is extracted from them. The study suggests new ways of addressing environmental contamination resulting from microbial activities in soils by combining advanced materials with biological substances such as Morchella spp. For this purpose, it investigates various molecular interactions occurring among carbonaceous species called Morchella microbes and environmental pollutants like those including Pb.

Open Access

Article

Comparative analysis of biomechanical patterns in sprinting: A machine learning approach to optimize running performance in track athletes

Burenbatu Burenbatu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 321 , 2024, DOI: 10.62617/mcb.v21i1.321

Abstract：

Athletes’ success in field and track competitions has been reported to be determined by their sprinting skills. Therefore, it is crucial to understand what biomechanical and physiological factors contribute to the most effective sprinting attributes. The scientific research on sprint evaluation has predominantly dealt with discrete metrics simultaneously, avoiding the interplay between multiple factors as the sprint progresses. Incorporating all the factors that could potentially influence the impact of excellent sprint ability is the primary objective of the present study. This research investigates the biomechanics of sprinting using a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) hybrid approach, focusing mainly on factors like stride length, ground reaction forces, joint angles, and muscle activation patterns. The hybrid Machine Learning (ML) model accurately identifies between the two groups, and the results indicate that sprinters performing at the national level have more extended movements, higher reaction time to ground forces, and improved joint angles. The research project set up a 20-meter track for the race, and 30 participants, divided 50-50 between two distinct groups that included comparable college-level and national-level performers, participated. With a 92.4% accuracy, 90.2% precision, and 90.9% F1 score, the hybrid approach performed better than standard models in predicting optimum sprinting patterns. The higher efficiency is caused by phase-specific changes that the model unattended, such as enhanced knee angles and joint accelerated motion in the swing phase. In comparison, the SVM model, though respectable, lags behind with an accuracy of 85.7% and a lower precision and recall (82.4% and 80.9%, respectively). The RF model performed better than SVM with an accuracy of 88.1% and a balanced F1-score of 86.8% but still fell short of the CNN-LSTM hybrid. The standalone LSTM model performed relatively well, with an accuracy of 89.3% and an F1 score of 88.1%, showing its capability but still not matching the hybrid model’s performance.

Open Access

Article

Predictive analysis of lactate clearance on the prognosis of patients with large burns

Yanli Ma, Pei Wu, Xiaomin Wang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 136 , 2024, DOI: 10.62617/mcb.v21i1.136

Abstract：

Aims: To examine burn shock monitoring and the connection between lactate clearance and shock prognosis in individuals with severe burns. Materials and methods: This retrospective analysis comprised 100 patients with significant burns treated in our hospital between August 2017 and August 2021. The patients were split into two groups based on whether they survived or not: 50 cases each in the survival and death groups. Both groups had their blood lactate levels, partial pressure of oxygen, and other relevant indices tested. Analysis was done on the correlation between lactate level, lactate clearance, and death as well as the association between lactate clearance and prognosis at each time and point. Results: The lactate clearance rates at 6 h, 12 h, 24 h and 24 h in the survival group were greater than those in the death group, and the differences were significant ( p < 0.05). The lactate clearance rates at each time point were compared at a cut-off of 20%, and patients with 6 h and 12 h blood lactate clearance rates ≥20.0% had lower prognostic morbidity and mortality rates than those <20.0%, with a significant difference ( p < 0.05). Conclusion: Lactate clearance is directly correlated with the prognosis of patients with severe burns, and survivors also have a high clearance rate. As a result, in patients with severe burns, lactate clearance monitoring during the shock phase can improve prognostic prediction and enable early treatment plan modification based on outcomes.

Open Access

Article

Sports science: Exploring the mechanics of biomolecules in athletic performance

Jianhui Tang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 203 , 2024, DOI: 10.62617/mcb.v21i1.203

Abstract：

Enhancing athletic common overall performance has taken on the significance of comprehending the complicated physics of biomolecules in the subject of the sports era. Energy metabolism, muscular features, and recovery mechanisms are all laid low with biomolecules like lipids, proteins, and carbs, which affect athletes’ bodily functionality and staying electricity. However, biomolecules’ dynamics and interactions are infamously difficult to recognize. Molecular behaviour below primary rate physiological settings are complicated and multi-faceted, and there are various data belongings to preserve in mind, together with computational models and experimental validations. Dynamic Integrative Biomechanical Optimization Analysis (DIBOA) is a modern technique that those educations indicate have to assist with those problems. DIBOA combines computational simulations, experimental validations, and advanced biomechanical modelling. Its purpose is to offer predictive insights into biomolecular reactions below various exercise intensities and conditions, deciphering the dynamic interactions of biomolecules rather than physical rest. Optimizing education regimens, individualized vitamin techniques, and damage prevention measures best for athlete profiles are all feasible with DIBOA. DIBOA offers an extensive framework for predicting biomolecular responses and optimizing interventions that beautify overall performance via simulation assessment in the sports activity’s era. Researchers can simulate biomolecular dynamics and examine their reactions in practical sports activity conditions with DIBOA’s simulation evaluation ability. This method will assist us in apprehending how biomolecules affect athletic overall performance, which will bring about extra-centered treatments and improvements in sports activities technology.

Open Access

Article

Study on the sports biomechanics prediction, sport biofluids and assessment of college students’ mental health status transport based on artificial neural network and expert system

Haixia Yue, Jun Cui, Xiaoxue Zhao, Yin Liu, Hao Zhang, Mingyi Wang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 256 , 2024, DOI: 10.62617/mcb.v21i1.256

Abstract：

Based on the theories of artificial neural networks and expert systems, this study constructs a model for assessing the sports biomechanics’ mental health of college students using artificial neural networks and expert systems. It assesses the sports biomechanics’ mental health status of college students who had their yoga classes suspended during the COVID-19 pandemic. Meanwhile, A mobile questionnaire was used to collect information on students’ personal circumstances, sport biofluids and yoga exercise routines, as well as data from the Depression Anxiety Stress Scales (DASS-21). This study presents a novel approach to assessing the intersection of sports biomechanics and mental health by employing Artificial Neural Networks (ANNs) and expert systems. Unlike previous research in this domain, this study offers an extensive review of the literature, highlighting both the distinctive contributions of ANNs and expert systems and the existing gaps in current methodologies. Similarly, a univariate analysis method was utilized to quantitatively assess the impact of yoga interventions and other factors on college students’ sports biomechanics and mental health. Building on this analysis, an artificial neural network (ANN) model was developed to predict mental health outcomes and sport biofluid conditions. The model focused on evaluating the significance of various variables, with particular attention to the contribution of yoga exercise routines. In short, this approach aims to enhance the understanding and support for utilizing yoga interventions to improve college students’ mental health within the context of sports biomechanics, especially in the post-pandemic era. The findings should make an important contribution to the field of integrating ANNs with expert systems and sports biomechanics improves mental health prediction accuracy.

Open Access

Article

A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

Caijuan Jiao

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 357 , 2024, DOI: 10.62617/mcb.v21i1.357

Abstract：

Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.

Open Access

Article

Modeling and simulation of nonlinear dynamical systems for biosensor sensitivity based on carbon nanocomposites

Meng Wang, Na Jin

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 260 , 2024, DOI: 10.62617/mcb.v21i1.260

Abstract：

Nanomaterials have a wide range of applications in various fields of scientific research due to their unique physical and chemical properties. With the rapid development of science and technology, nanocomposites synthesized from various nanoparticles have obtained many excellent properties due to their synergistic effects. In this paper, a series of carbon-based nanomaterials are proposed for in-depth research, and corresponding biosensors are constructed. In this study, ECL biosensors based on a variety of nanocomposite materials will be used to detect and analyze cells, and also detect other cells that are similar to cells. The experimental data show that the relative standard deviation of the detection results of the two methods is within 8%, and the sensor has high sensitivity, excellent stability and fast response speed. The sensor showed excellent performance in the repeatability test, and the relative standard deviation of repeated detection was less than 2%. This result shows that the sensor has highly consistent detection capabilities, providing important support for its reliability in practical applications. By adding the description of repeatability data, the summary more comprehensively reflects the performance advantages of the sensor.

Open Access

Article

Comparison of quadriceps and hamstring muscle strength ratios between dominant and non-dominant legs in Saudi under-17 and under-19 premier league football players: A cross-sectional study

Ayman Alhammad, Hussain Ghulam, Ibrahim Al Zaqrati, Abdulaziz Alsahli, Abdulaziz Alhazmi, Husam Almalki, Omar Althomali

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 342 , 2024, DOI: 10.62617/mcb.v21i1.342

Abstract：

Football’s global popularity is often overshadowed by frequent lower limb injuries, particularly hamstring strains, which are linked to imbalances in the strength ratio of hamstring-to-quadriceps (H/Q). Research on these factors among Saudi Premier League players, specifically in Madinah City, is limited. Our study is a cross-sectional study that assessed 42 male professional football players from Ohoud Football Club, divided into Underage 17 (UD- 17) and Under age 19 (UD-19) of age groups. The Strength of muscle for hamstrings and quadriceps, as well as the H/Q ratio, was measured using handheld dynamometers (HDD), and demographic data were analyzed using SPSSv26. Results showed that UD-19 players had significantly greater quadriceps strength on both dominants (dominant and non-dominant) sides compared to UD-17 players, with no significant differences in strength of hamstring. The ratio of hamstring to quadriceps was significantly higher in UD-17 players on the dominant side compared to UD-19 players, but statistically significant differences were not found on the non-dominant side. These results suggest that quadriceps strength develops with age and training, potentially reducing injury risk, while the strength of hamstring stabilizes earlier. Tailored training programs focusing on quadriceps strength and balanced hamstring development are recommended for improving injury prevention and performance. Future research involving larger and more diverse samples could further validate these findings and provide a deeper understanding of muscle dynamics in young football players.

Open Access

Article

Analysis of the mechanical characteristics of progressive one-handed underhand shooting in basketball play through kinematics

Huajian Zhu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 125 , 2024, DOI: 10.62617/mcb.v21i1.125

Abstract：

Background: Progressive one-handed underhand shooting is not only a key movement in basketball but also a primary method of scoring. Objective: This paper aims to compare the mechanical characteristics of players at different levels while performing the progressive one-handed underhand shooting movement to guide training. Methods: Ten athletes were divided into an excellent group and an ordinary group. The shooting was conducted using two SONY cameras, and the kinematics data were acquired through re-analysis in the Simi 3D Motion system for comparative analysis. Results: The average number of successful shots per person in the excellent group was 8.12 ± 0.81, which was significantly different from the ordinary group. During ball holding, the first step length of the excellent group was 1.91 ± 0.03 m, showing a significant difference compared to the ordinary group. At the beginning of ball holding, the right elbow joint angle for the excellent group was 121.26° ± 0.58° and the right hip joint angle was 135.64° ± 0.78°, both significantly different from those in the ordinary group. At the end of holding the ball, the excellent group had a right shoulder joint angle of 51.26° ± 2.36° and a right elbow joint angle of 70.34° ± 1.68°, which was significantly different from the ordinary group. At the end of jump, the excellent group had a right shoulder joint angle of 80.16° ± 2.21° and a right elbow joint angle of 87.45° ± 1.68°, which was significantly different from the ordinary group. During the shooting phase, the excellent group had a shooting angle of 60.12° ± 2.36°, a shooting height of 2.92 ± 0.03 m, and a shooting speed of 4.12 ± 0.46 m/s, all showing significant differences compared to the ordinary group. Conclusion: The excellent group with more sufficient stride, push, and extension and better shooting parameters performed better in performing the movement of progressive one-handed underhand shooting.

Open Access

Article

Changes of microbial active ingredients and antioxidant capacity during fermentation process of dark tea

Zhanjun Liu, Dan Jian, Taotao Li, Zhiyuan Hu, Shiquan Liu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 265 , 2024, DOI: 10.62617/mcb.v21i1.265

Abstract：

Dark tea, a traditional fermented tea in China, is known for its unique flavor and enhanced health potential due to its fermentation process. However, previous studies on the evolution of its active ingredients and antioxidant properties have been limited by inadequate sample collection, single analytical methods, and insufficient data processing. To address these challenges, this study employed a comprehensive strategy to analyze the dynamic changes of active compounds and antioxidant efficacy during dark tea fermentation using refined sampling, diverse assay techniques, and advanced data analysis. A multi-point temporal sampling method was used to capture key stages of fermentation, ensuring comprehensive data. High-Performance Liquid Chromatography (HPLC) and various antioxidant assays (1,1-diphenyl-2-picryl-hydrazyl (DPPH), 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), Ferric ion reducing antioxidant power (FRAP)) enabled precise quantification of tea polyphenols, catechins, theaflavins, and thearubigins. Multivariate statistical analysis revealed that tea polyphenols and catechins decreased, theaflavins increased then slightly declined, and thearubigins steadily rose as fermentation progressed. These changes were linked to fluctuations in antioxidant capacity, peaking at around 30 mg/g of phenolic compounds. The study also explored optimizing fermentation to enhance the retention of beneficial components, maximizing antioxidant properties and improving product quality. This research advances the understanding of dark tea fermentation and supports the sustainable development of the dark tea industry.

Open Access

Article

Empowering college physical education: AI-driven training, teaching, and intelligent information processing

Yu Tian

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 327 , 2024, DOI: 10.62617/mcb.v21i1.327

Abstract：

The current status, methods utilized for Physical Education Training and Teaching System for College Students, and difficulties during information processing are all investigated in this comprehensive study. We compiled 130 empirical research on Artificial Intelligence-based Physical Education (AIPE) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses are analysed. Research shows that AI may improve health tracking, individualized training, and analysis of sporting performance. There is a lot of promise in AIPE for individualized lessons, immediate feedback, varied classroom settings, and evaluation. The problems arise when dealing with technological dependability, privacy concerns, and the need for instructor assistance. These results give light on important questions for future AIPE. This research delves into the process of creating and launching an all-encompassing educational platform that makes use of AI and data processing methods. Our system’s goals include improving the quality of instruction, tailoring feedback to each student, and enhancing the overall learning experience. AI Algorithms powered by artificial intelligence help us shift through student test scores, identify knowledge gaps, and modify lessons appropriately. This makes sure that the curriculum meets each student’s needs. Practical exercises, quizzes, and assignments get immediate feedback through the system. It uses natural language processing (NLP) to analyse student answers, find misunderstandings, and provide help for fixing them. The system personalizes learning routes according to students’ choices, learning styles, and progress. It suggests further reading, interactive games, and group assignments. Through the automation of administrative activities, generation of analytics reports, and suggestion of pedagogical changes, the system aids instructors. It makes it easier for students and instructors to talk to one another. Data protection, overcoming AI biases, and getting teachers on board with tech-enhanced lessons are all obstacles. Future studies should aim to improve the system, confirm its efficacy, and encourage its implementation across educational institutions. Finally, there is great potential for improving higher education with an AI-based training and teaching system with strong data processing skills. Students and teachers may reap the advantages of a technologically enhanced, ever-changing learning environment.

Open Access

Article

Evaluation of the influence of athlete neural activity patterns on the dynamic index of leaping ability using data mining techniques

Lai Liu, Yan Dong

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 150 , 2024, DOI: 10.62617/mcb.v21i1.150

Abstract：

There are many dynamic indicators of jumping ability, and the athlete’s neural activity pattern is an important factor in regulating limb activities. This article uses data mining technology to collect, preprocess, data modeling and analysis, and data visualization of dynamic index data of athletes’ neural activity patterns of jumping ability. The results show that the jumping ability of athletes with higher neural activity intensity increased significantly after training to around 40 cm–50 cm, while athletes with lower neural activity intensity did not change significantly and remained around 30 cm–35 cm. The overall learning ability of athletes with higher levels of neural activity improved by about 10 cm, and the base of neural activity also increased significantly. It shows that there is a significant correlation between the intensity of neural activity and dynamic indicators of jumping ability, which is the main driving force for athletes’ jumping explosive power. The research results can help formulate reasonable and scientific training methods to improve athletes’ jumping ability and overall sports level.

Open Access

Article

Advanced resistance exercise combined with aerobic rehabilitation training on cardiopulmonary function

Qiannan Liu, Yiqiao Zhang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 186 , 2024, DOI: 10.62617/mcb.v21i1.186

Abstract：

Chronic diseases are the number one killer in the world today, and the most common cause of chronic diseases is lack of exercise. Research on the application of artificial intelligence technology in the medical field can shorten the distance between clinical best practice and practical application, and promote the standardized management of clinicians according to specifications. In this paper, three different exercise methods, continuous aerobic, anaerobic, and control, were used to conduct a 12-week study. This paper discussed the effects of three different forms of exercise on the body shape, body composition, and metabolism of patients with chronic diseases, and discussed the intervention effects of the same exercise mode in different periods (0–6 weeks, 6–12 weeks), to provide a theoretical basis for more effective and targeted choice of exercise intervention programs. Through training load, training frequency, training sequence, training interval, and other factors, a training plan can be designed. To avoid mistakes, experts often try to do some items that are prone to mistakes, especially lung function measurement. The scores of self-care, extraction, standing, occupation/housework, social activities, and total scores were significantly lower than those before intervention ( P < 0.01). Advanced resistance kinetic energy can significantly improve the exercise and cardiopulmonary function of patients with various types of chronic diseases. Advanced resistance kinetic energy can significantly promote the strength and explosive force of the shoulder, waist, and back muscles in chronic patients, and can significantly promote the muscle adaptability of the shoulder, waist, and back muscles in chronic patients.

Open Access

Article

Monitoring system for physical activity and fitness based on service robots and biomechanics

Qingtian Zeng, Yingman Ye

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 210 , 2024, DOI: 10.62617/mcb.v21i1.210

Abstract：

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

Open Access

Article

Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production

Jiarong Liu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 277 , 2024, DOI: 10.62617/mcb.v21i1.277

Abstract：

In response to the problems of low reliability and long time consumption in traditional research on sound production, this article used electromyography (EMG) signals as input signals to build a high-precision sound production mechanics model. A Proportional-Integral-Derivative (PID) controller was used to dynamically adjust the model and develop a real-time feedback system. This article established a detailed three-dimensional (3D) finite element model including the vocal cords and throat, defined the nonlinear elastic properties and linear elastic properties of different tissues, and used tetrahedral grid partitioning technology to improve the computational accuracy of the model. Through a EMG sensor, an individual EMG was collected and filtered to remove noise. The processed EMGs were used as input parameters for the finite element model to drive the muscle units in the model. By using a PID controller to receive real-time EMG input, the error was calculated and the model was adjusted, enabling accurate simulation of the mechanical properties of vocal cord vibration under different vocal states and achieving real-time feedback. Considering the complexity of vocal cord vibration driven by biological signals, this article simulated and analyzed the modal characteristics of vocal cord vibration, and analyzed the differences in vocal cord vibration characteristics under different vocal states. The sound pressure distribution and resonance frequency were simulated and analyzed to understand the propagation characteristics of sound. Finally, by comparing and analyzing the simulated data with the actual collected data, it was found that the maximum relative error rate of the model under different sound states was 6.14%, and the overall error rate of the model was relatively low, which verified the reliability of the model. The findings demonstrated that the feedback delays of the model in different sound states were all within 100 milliseconds, indicating that the system had high real-time performance and accuracy, which was promising in practical applications.

Open Access

Article

2D animation comic character action generation technology based on biomechanics simulation and artificial intelligence

Zhijun Yang

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 338 , 2024, DOI: 10.62617/mcb.v21i1.338

Abstract：

AI can greatly improve the process of creating 2D character animations, especially for platformer games. The objective is to enhance visual effects quality and streamline production by incorporating biomechanics and AI-driven technology into the animation process. The current state of study shows that conventional 2D animation production has several problems, such as lengthy procedures, complicated technical requirements, and the requirement for human involvement. Both efficiency and innovation are limited by these limitations. To solve this problem, we suggest generating 2D character animations using AI-driven generative methods and biomechanics simulation. Consistently high-quality outcomes can be achieved by automating aspects of animation design. Improving animation industry innovation, streamlining production workflows, and decreasing manual labor are crucial. The viability of 2D animations generated by AI has been demonstrated in preliminary studies. Artificial intelligence’s effects on animation’s expressiveness, realism, and aesthetics are covered in detail. Furthermore, it has presented challenges with training data, choosing the right model, and fine-tuning. Beyond the field of gaming, our research has wider implications. Several industries may benefit from the benefits of 2D animations enhanced with AI and biomechanics technology. These industries include advertising, entertainment, and education.

Open Access

Article

Research on music genre recognition method based on deep learning

Yuchen Guo

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 373 , 2024, DOI: 10.62617/mcb.v21i1.373

Abstract：

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

Open Access

Article

Digital signal detection and recognition in the communication field combining DAEN and CNN

Yufan Deng, Lele Niu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 482 , 2024, DOI: 10.62617/mcb.v21i1.482

Abstract：

In communication, the detection and recognition of digital signals have always faced problems such as low adaptability and high misclassification rate. To address these issues, this study innovatively fused the deep auto-encoder network with convolutional neural networks to construct a novel digital signal detection and recognition model. Firstly, this study utilized the powerful feature extraction capabilities of the deep auto-encoder network to extract key feature information from massive amounts of data. Then these features were combined with convolutional neural networks to construct a detection and recognition model. These results confirmed that the constructed digital signal detection and recognition model had values of 93.75% and 94.18% in data detection recognition rate and average classification accuracy, respectively. Meanwhile, this model also performed well in terms of data processing accuracy and recall. In the comparison of data processing, the accuracy and recall rates were 93.59% and 94.67%, respectively, and the performance of data detection and recognition was better than that of the comparison methods. This indicates that the constructed digital signal detection and recognition model can significantly improve the reliability and robustness of signal detection and recognition. This paper brings new breakthroughs to the development of digital signal detection and recognition technology in communication.

Open Access

Article

Prevention and rehabilitation measures for knee injuries during sports training

Haolun Xu

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 149 , 2024, DOI: 10.62617/mcb.v21i1.149

Abstract：

In sports training, athletes often cause knee injury due to excessive exercise or inappropriate exercise habits, which increases inconvenience to the training life of athletes. In order to avoid the occurrence of knee injury, this article designed a knee joint injury warning system based on the neural network algorithm, which was used in the process of preventing athletes’ movement damage and concluded. In the survey of pain scores, the measures for preventing athletes’ knee damage in this article really played a role in alleviating the athlete’s knee pain. In unstable scoring, the anxiety score of athletes has greatly improved after taking measures. In the survey of the lock -up score, the athletes’ atresia feel was stable within 21 or above after taking measures. The athletes’ swelling scores increased after taking measures. Athletes 6, 9, 13, 14, and 15 have reached full scores.

Open Access

Article

Monitoring of biochemical indicators before and after quantitative load exercise for athletes based on computational intelligence

Qingxiu Meng, Shengxian Chen

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 227 , 2024, DOI: 10.62617/mcb.v21i1.227

Abstract：

Biochemical monitoring of sports training is a major part of training monitoring. It uses biochemical methods and techniques to measure some biochemical indicators in athletes during training, so that athletes can maximize their sports ability. But for a long time, people’s awareness of the importance of biochemical indicators monitoring of athletes is not enough, which is one of the main factors that restrict and affect the development of athletes themselves. In this paper, computational intelligence technology is used to research and analyze various indicators and strengths of athletes’ load intensity. It takes the changes of creatine kinase, heart rate and hemoglobin before and after quantitative load exercise as the entry point. And through this intelligent technology, the changes in the index data can be objectively and accurately reflected, so that it is hoped that the subsequent practical training can be guided more scientifically and the blindness of training can be reduced. The results showed that the activity of serum CK (creatine kinase) enzyme increased, and the heart rate also reached a peak immediately after training, which indicates that after the optimization of the training model, these biochemical indicators can make more scientific analysis of training volume, training intensity and recovery status. Among them, the maximum difference between the values of CK and BU (blood urea) is 10. At the same time, it can also provide some reference and reference for formulating more reasonable training methods and plans in the later stage and adjusting exercise load in time.

Open Access

Article

Inverse dynamics analysis of 4-DOF stacking robot with closed chain structure

Liang Yu, Ronaldo Juanatas, Mingliang Zheng

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 449 , 2024, DOI: 10.62617/mcb.v21i1.449

Abstract：

A four-degree-of-freedom PR1300 stacking robot with two parallel quadrilateral structures connected in series at the shoulder was designed. The hybrid robot with local closed chains has the characteristics of compact structure, high stiffness, and high modularity. To solve the dynamic problem of the hybrid robot. Firstly, by analyzing the induced relationship between joints, the stacking robot with a local closed chain structure is transformed into two branch chain mechanisms, and the kinematic model of the robot is established using the D-H parameter method. Secondly, the Kane method is applied to model the dynamics of the two branch chains separately, and the rigid body dynamics Kane equation of the entire 4-DOF stacking robot with a closed chain structure is obtained through the augmented matrix. Finally, the SolidWorks Motion mechanical simulation platform and MATLAB software were used to program dynamic simulations and numerical calculations, and the results were compared to verify the correctness of the dynamic theory derivation. This theory provides a necessary theoretical basis for the subsequent synthesis of robot dynamic scales.

Open Access

Article

Physical properties of Korean normal aortic valves based on fluid–structure interactions

Jeongrim Choi, Jieun Park, Junghun Kim, Jongmin Lee

Molecular & Cellular Biomechanics, Vol.21, No.1, 21(1), 123 , 2024, DOI: 10.62617/mcb.v21i1.123

Abstract：

Recently, numerical methods such as computational fluid dynamics (CFD), have been widely used in heart valve research. The CFD approach has fewer restrictions compared to clinical and experimental methods as it involves interpretations through computer calculations, and it can be used to predict and analyze fluids. For valve numerical analysis using CFD, the mechanical properties of the valve must be defined based on the physical properties. However, most of the existing heart valve numerical analysis studies have been conducted for westerners, and only a few studies on the same topic have focused on Asians. Thus, in this paper, we aim to determine the physical property parameters suitable for defining the mechanical properties of the normal aortic valves of Koreans over time. In this study, we used a fluid−structure interaction technique for the valve simulation and applied three representative valve characteristics presented in previous aortic valve simulation studies. Herein, the valve patency rates in case of simulation and multidetector computed tomography images were compared and analyzed through statistical techniques. Our results revealed that the physical properties, such as density (1050 kg/m 3 ), Young’s modulus (2 MPa), and Poisson’s ratio (0.3), are like those of the Korean aortic valve over time. If hemodynamic evaluation of the Korean aortic valve is performed through simulation using these conditions, it can be effective in identifying the factors of heart valve disease.