Pub Date : 2023-09-29DOI: 10.32604/mcb.2023.044564
Guixue Wang
The analysis of biomechanical characteristics plays an important role in mastering the technical characteristics of athletes, providing guidance for the formulation and prevention of sports injury training plans and providing theoretical support for research on injury prevention and stability control in the sports field. With the importance of data analysis, the application scope of artificial intelligence methods is more extensive. For example, intelligent training systems can be used for athletes’ personalized and professional training, real-time monitoring and feedback of training data, and further reduce the risk of sports injury. However, deep learning methods process a large number of medical images to identify and predict diseases such as cancer.
{"title":"Hot Topics of Molecular and Cellular Biomechanics in 2022","authors":"Guixue Wang","doi":"10.32604/mcb.2023.044564","DOIUrl":"https://doi.org/10.32604/mcb.2023.044564","url":null,"abstract":"The analysis of biomechanical characteristics plays an important role in mastering the technical characteristics of athletes, providing guidance for the formulation and prevention of sports injury training plans and providing theoretical support for research on injury prevention and stability control in the sports field. With the importance of data analysis, the application scope of artificial intelligence methods is more extensive. For example, intelligent training systems can be used for athletes’ personalized and professional training, real-time monitoring and feedback of training data, and further reduce the risk of sports injury. However, deep learning methods process a large number of medical images to identify and predict diseases such as cancer.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pulmonary embolism (PE), caused by deep venous thrombosis (DVT), is a disease with high morbidity and mortality. Implantation of inferior vena cava filters is an important method for the clinical prevention of PE. The hemodynamic characteristics of filters implanted in the inferior vena cava (IVC) have a significant impact on their performance. However, IVC diameters vary among patients. This may have a direct impact on the hemodynamic properties of the filter. At present, there is no research on this kind of problem to be investigated. In this paper, the hemodynamic properties of the VenaTech convertible filter were simulated in three different IVC models of 15, 20 and 25 mm diameters, using computational fluid dynamics (CFD) as a control variable (only the IVC diameter is varied). The results showed that the diameter has a significant impact on the hemodynamic characteristics after filter implantation. The IVC diameter has a great influence on the stagnation zone of the blood flow, the maximum wall shear stress (WSS) on the upstream side along the filter wire, and the flow resistance. The case of 15 mm diameter was the most prone to thrombus formation downstream of the filter head in the IVC, but the larger WSS on the upstream along the filter wire may facilitate thrombus lysis. Therefore, the change in vessel diameter should be considered when performing filter implantation for patients.
{"title":"CFD Study on Hemodynamic Characteristics of Inferior Vena Cava Filter Affected by Blood Vessel Diameter","authors":"Shiyue Zhang, Xue Song, Jingying Wang, Wen Huang, Yue Zhou, Mingrui Li","doi":"10.32604/mcb.2023.044445","DOIUrl":"https://doi.org/10.32604/mcb.2023.044445","url":null,"abstract":"Pulmonary embolism (PE), caused by deep venous thrombosis (DVT), is a disease with high morbidity and mortality. Implantation of inferior vena cava filters is an important method for the clinical prevention of PE. The hemodynamic characteristics of filters implanted in the inferior vena cava (IVC) have a significant impact on their performance. However, IVC diameters vary among patients. This may have a direct impact on the hemodynamic properties of the filter. At present, there is no research on this kind of problem to be investigated. In this paper, the hemodynamic properties of the VenaTech convertible filter were simulated in three different IVC models of 15, 20 and 25 mm diameters, using computational fluid dynamics (CFD) as a control variable (only the IVC diameter is varied). The results showed that the diameter has a significant impact on the hemodynamic characteristics after filter implantation. The IVC diameter has a great influence on the stagnation zone of the blood flow, the maximum wall shear stress (WSS) on the upstream side along the filter wire, and the flow resistance. The case of 15 mm diameter was the most prone to thrombus formation downstream of the filter head in the IVC, but the larger WSS on the upstream along the filter wire may facilitate thrombus lysis. Therefore, the change in vessel diameter should be considered when performing filter implantation for patients.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135213034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/mcb.2023.041173
Xinsen Wei, Ying Guo, Yan Zhao
As one of the most basal avian clades, the Confuciusornithids are ideal in revealing the early evolution of avian flight. Birds’ hindlimbs are functionally diverse and contain a wealth of information about their behavior. The hindlimb of Confuciusornis, however, has only been studied in detail in terms of functional morphology, and quantitative studies that directly assess locomotor ability are relatively lacking. This has led to certain controversies on the behavior of Confuciusornis. This paper reviews the debates over the life habits and take-off ability of Confuciusornis, which are closely related to their hindlimb function. Several methodologies adopted engineering techniques, including the geometrical analysis of long bones, physiological reconstruction of muscles, kinematic and kinetic characteristics estimating, and appendage locomotor mechanism analysis, are recommended for estimating the hindlimb functions of Confuciusornis. Considering that the fossil bones are fragile, irregular in shape, and usually deformed, it is appropriate to apply computer numerical simulation techniques to such studies. A sufficient functional quantitative study will help clarify early bird locomotor behavior, which will provide clues and evidence for further exploration of the origin of bird flight and early bird movement.
{"title":"Reconstruction of the Hindlimb Locomotion of Confuciusornis (Aves) and Its Implication for the Origin of Avian Flight","authors":"Xinsen Wei, Ying Guo, Yan Zhao","doi":"10.32604/mcb.2023.041173","DOIUrl":"https://doi.org/10.32604/mcb.2023.041173","url":null,"abstract":"As one of the most basal avian clades, the Confuciusornithids are ideal in revealing the early evolution of avian flight. Birds’ hindlimbs are functionally diverse and contain a wealth of information about their behavior. The hindlimb of <i>Confuciusornis</i>, however, has only been studied in detail in terms of functional morphology, and quantitative studies that directly assess locomotor ability are relatively lacking. This has led to certain controversies on the behavior of <i>Confuciusornis</i>. This paper reviews the debates over the life habits and take-off ability of <i>Confuciusornis</i>, which are closely related to their hindlimb function. Several methodologies adopted engineering techniques, including the geometrical analysis of long bones, physiological reconstruction of muscles, kinematic and kinetic characteristics estimating, and appendage locomotor mechanism analysis, are recommended for estimating the hindlimb functions of <i>Confuciusornis</i>. Considering that the fossil bones are fragile, irregular in shape, and usually deformed, it is appropriate to apply computer numerical simulation techniques to such studies. A sufficient functional quantitative study will help clarify early bird locomotor behavior, which will provide clues and evidence for further exploration of the origin of bird flight and early bird movement.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this study was to investigate whether kinematic data during a countermovement jump (CMJ) could explain the post-activation performance enhancement (PAPE) effects following acute resistance exercise. Twenty-four male participants with resistance training and jumping experience were recruited and randomly assigned to either the experimental group (PAPE-stimulus) (n = 12) or the control group (n = 12). In the experimental group, participants performed 5 reps of squats at 80% 1RM to induce PAPE, while the control group received no intervention. Both groups performed three CMJ tests before (PRE) and at immediate (POST0), 4 (POST4), 8 (POST8), and 12 (POST12) min after the intervention, with kinematic data recorded during the CMJ. Kinematic parameters analyzed in this study included jump height, hip-knee-ankle flexion angles at the lowest position of the countermovement, eccentric and concentric time durations, and the temporal changes of hip-knee-ankle flexion angles during the entire jumping phase. The presence of PAPE was determined by the change in jump height. The results showed that in the experimental group, jump height significantly increased at POST4 (p < 0.001) and POST8 (p < 0.001) and significantly decreased at POST0 (p = 0.008), with no significant change at POST12. The control group showed no significant changes at any measured time point. Kinematic parameters showed that there was no significant difference in joint flexion angle of the lower body during the CMJ between pre- and post-intervention, regardless of PAPE or fatigue. However, eccentric time significantly decreased at 4 and 8 min (p = 0.013 and p = 0.001, respectively) after the intervention. These findings suggest that PAPE-induced increases in jump height after acute resistance exercise can be attributed to the decrease in eccentric phase duration, but not joint flexion angle. Additionally, the fatigue-induced decrease in jump height cannot be reflected by jumping kinematics. Based on these findings, coaches may use complex training to utilize the PAPE effects to increase jump height while reducing the eccentric time during vertical jumps. This method can enhance an athlete’s eccentric ability to generate force in a short amount of time which is crucial for performance enhancement.
{"title":"Can PAPE-Induced Increases in Jump Height Be Explained by Jumping Kinematics?","authors":"Xiaojie Jiang, Xin Li, Yining Xu, Dong Sun, Julien S. Baker, Yaodong Gu","doi":"10.32604/mcb.2023.042910","DOIUrl":"https://doi.org/10.32604/mcb.2023.042910","url":null,"abstract":"The aim of this study was to investigate whether kinematic data during a countermovement jump (CMJ) could explain the post-activation performance enhancement (PAPE) effects following acute resistance exercise. Twenty-four male participants with resistance training and jumping experience were recruited and randomly assigned to either the experimental group (PAPE-stimulus) (n = 12) or the control group (n = 12). In the experimental group, participants performed 5 reps of squats at 80% 1RM to induce PAPE, while the control group received no intervention. Both groups performed three CMJ tests before (PRE) and at immediate (POST0), 4 (POST4), 8 (POST8), and 12 (POST12) min after the intervention, with kinematic data recorded during the CMJ. Kinematic parameters analyzed in this study included jump height, hip-knee-ankle flexion angles at the lowest position of the countermovement, eccentric and concentric time durations, and the temporal changes of hip-knee-ankle flexion angles during the entire jumping phase. The presence of PAPE was determined by the change in jump height. The results showed that in the experimental group, jump height significantly increased at POST4 (<i>p</i> < 0.001) and POST8 (<i>p</i> < 0.001) and significantly decreased at POST0 (<i>p</i> = 0.008), with no significant change at POST12. The control group showed no significant changes at any measured time point. Kinematic parameters showed that there was no significant difference in joint flexion angle of the lower body during the CMJ between pre- and post-intervention, regardless of PAPE or fatigue. However, eccentric time significantly decreased at 4 and 8 min (<i>p</i> = 0.013 and <i>p</i> = 0.001, respectively) after the intervention. These findings suggest that PAPE-induced increases in jump height after acute resistance exercise can be attributed to the decrease in eccentric phase duration, but not joint flexion angle. Additionally, the fatigue-induced decrease in jump height cannot be reflected by jumping kinematics. Based on these findings, coaches may use complex training to utilize the PAPE effects to increase jump height while reducing the eccentric time during vertical jumps. This method can enhance an athlete’s eccentric ability to generate force in a short amount of time which is crucial for performance enhancement.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135214871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/mcb.2022.018958
R. Allena, D. Aubry
Cells adapt to their environment and stimuli of different origin. During confined migration through sub-cellular and sub-nuclear pores, they can undergo large strains and the nucleus, the most voluminous and the stiffest organelle, plays a critical role. Recently, patterned microfluidic devices have been employed to analyze the cell mechanical behavior and the nucleus self-deformations. In this paper, we present an in silico model to simulate the interactions between the cell and the underneath microstructured substrate under the effect of the sole gravity. The model lays on mechanical features only and it has the potential to assess the contribution of the nuclear mechanics on the cell global behavior. The cell is constituted by the membrane, the cytosol, the lamina, and the nucleoplasm. Each organelle is described through a constitutive law defined by specific mechanical parameters, and it is composed of a fluid and a solid phase leading to a viscoelastic behavior. Our main objective is to evaluate the influence of such mechanical components on the nucleus behavior. We have quantified the stress and strain distributions in the nucleus, which could be responsible of specific phenomena such as the lamina rupture or the expression of stretch-sensitive proteins.
{"title":"Nuclear Stress-Strain State over Micropillars: A Mechanical In silico Study","authors":"R. Allena, D. Aubry","doi":"10.32604/mcb.2022.018958","DOIUrl":"https://doi.org/10.32604/mcb.2022.018958","url":null,"abstract":"Cells adapt to their environment and stimuli of different origin. During confined migration through sub-cellular and sub-nuclear pores, they can undergo large strains and the nucleus, the most voluminous and the stiffest organelle, plays a critical role. Recently, patterned microfluidic devices have been employed to analyze the cell mechanical behavior and the nucleus self-deformations. In this paper, we present an in silico model to simulate the interactions between the cell and the underneath microstructured substrate under the effect of the sole gravity. The model lays on mechanical features only and it has the potential to assess the contribution of the nuclear mechanics on the cell global behavior. The cell is constituted by the membrane, the cytosol, the lamina, and the nucleoplasm. Each organelle is described through a constitutive law defined by specific mechanical parameters, and it is composed of a fluid and a solid phase leading to a viscoelastic behavior. Our main objective is to evaluate the influence of such mechanical components on the nucleus behavior. We have quantified the stress and strain distributions in the nucleus, which could be responsible of specific phenomena such as the lamina rupture or the expression of stretch-sensitive proteins.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82460773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/mcb.2022.016966
G. Sriram, T. R. Ganesh Babu, R. Praveena, J. V. Anand
Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.
{"title":"Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture","authors":"G. Sriram, T. R. Ganesh Babu, R. Praveena, J. V. Anand","doi":"10.32604/mcb.2022.016966","DOIUrl":"https://doi.org/10.32604/mcb.2022.016966","url":null,"abstract":"Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75076045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome the problem of less training data when starting a new study of Computer Aided Diagnosis (CAD), we firstly pick up a small number of diseased regions to simulate a limited datasets training procedure: 5 nodule patterns are selected and deformed into 110 nodules by random geometric transformation before fusing into 10 normal lung CT images using Poisson image editing. According to the experimental results, the Poisson fusion method achieves a detection rate of about 65.24% for testing 100 new images. Secondly, 419 slices from common database RIDER are used to train and test our YOLOv3 network. The time of lung nodule detection by YOLOv3 is shortened by 2–3 times compared with the mainstream algorithm, with the detection accuracy rate of 95.17%. Finally, the configuration of YOLOv3 is optimized by the learning data sets. The results show that YOLOv3 has the advantages of high speed and high accuracy in lung nodule detection, and it can access a large amount of CT image data within a short time to meet the huge demand of clinical practice. In addition, the use of Poisson image editing algorithms to generate data sets can reduce the need for raw training data and improve the training efficiency.
{"title":"Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets","authors":"Zhaohui Bu, Xuejun Zhang, Jianxiang Lu, Huan Lao, Chan Liang, Xianfu Xu, Yini Wei, Hongjie Zeng","doi":"10.32604/mcb.2022.018318","DOIUrl":"https://doi.org/10.32604/mcb.2022.018318","url":null,"abstract":"The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome the problem of less training data when starting a new study of Computer Aided Diagnosis (CAD), we firstly pick up a small number of diseased regions to simulate a limited datasets training procedure: 5 nodule patterns are selected and deformed into 110 nodules by random geometric transformation before fusing into 10 normal lung CT images using Poisson image editing. According to the experimental results, the Poisson fusion method achieves a detection rate of about 65.24% for testing 100 new images. Secondly, 419 slices from common database RIDER are used to train and test our YOLOv3 network. The time of lung nodule detection by YOLOv3 is shortened by 2–3 times compared with the mainstream algorithm, with the detection accuracy rate of 95.17%. Finally, the configuration of YOLOv3 is optimized by the learning data sets. The results show that YOLOv3 has the advantages of high speed and high accuracy in lung nodule detection, and it can access a large amount of CT image data within a short time to meet the huge demand of clinical practice. In addition, the use of Poisson image editing algorithms to generate data sets can reduce the need for raw training data and improve the training efficiency.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82322090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/mcb.2022.018345
A. Zar, F. Ahmadi, F. Karimi, M. Ahmadi, R. Ramsbottom
{"title":"Effect of Resistance Training and Spirulina platensis on Expression of IL-6, Gp130 Cytokines, JAK-STAT Signaling in Male Rats Skeletal Muscle","authors":"A. Zar, F. Ahmadi, F. Karimi, M. Ahmadi, R. Ramsbottom","doi":"10.32604/mcb.2022.018345","DOIUrl":"https://doi.org/10.32604/mcb.2022.018345","url":null,"abstract":"","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85769929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/mcb.2022.017044
Gang He
This study aims to analyze the biomechanical difference between the two legs of male badminton players when they land on one leg, thereby providing some guidance for preventing sports injury. Ten male badminton players were selected as the subjects. They did the single-leg landing movement successfully three times. The kinematic data were obtained by the Vicon infrared high-speed motion capture system. The kinetic data were obtained by the KISTLER three-dimensional forcing measuring platform. The data were processed and analyzed. The center of gravity of the right leg on the X and Y axes were 0.25 ± 0.05 and 0.21 ± 0.04 m, respectively, which were lower than that of the left leg (p < 0.05). At the moment of landing by a single leg, the hip angle of the left and right legs was 164.78 ± 6.12° and 156.29 ± 6.89°, respectively (p < 0.05), the hip joint speed of the left and right legs was 2.21 ± 0.32 and 1.98 ± 0.31 m/s, respectively (p < 0.05), the knee joint speed of the left and right legs was 2.51 ± 0.21 and 2.21 ± 0.21 m/s, respectively (p < 0.05). Although there was no significant difference in the range of joint motion, the motion range of the right leg was larger than that of the left leg, and the buffering time of the knee joint of the right leg was also significantly less than that of the left leg. The comparison of the kinetic data demonstrated that the ground reaction force (GRF), peak vertical ground reaction force (PVGRF), and lower limb stiffness of the right leg were significantly smaller than those of the left leg, and the time to peak force was greater than that of the left leg (p < 0.05). The injury risk of the left leg is greater than that of the right leg when the athlete land on a single leg. In the process of training, the athlete should strengthen the stability training of two legs, especially the left leg, in order to reduce sports injury.
本研究旨在分析男子羽毛球运动员单腿着地时两腿的生物力学差异，从而为预防运动损伤提供一定的指导。选取10名男子羽毛球运动员作为研究对象。他们成功地做了三次单腿着地动作。运动学数据由Vicon红外高速运动捕捉系统获取。动力学数据由KISTLER三维力测量平台获得。对数据进行了处理和分析。右腿X、Y轴重心分别为0.25±0.05、0.21±0.04 m，均低于左腿(p < 0.05)。单腿着地时，左右腿髋关节角度分别为164.78±6.12°和156.29±6.89°(p < 0.05)，左右腿髋关节速度分别为2.21±0.32和1.98±0.31 m/s (p < 0.05)，左右腿膝关节速度分别为2.51±0.21和2.21±0.21 m/s (p < 0.05)。虽然在关节活动范围上没有明显差异，但右腿的活动范围大于左腿，右腿膝关节的缓冲时间也明显小于左腿。动力学数据对比发现，右腿的地面反力(GRF)、垂直地面反力峰值(PVGRF)和下肢刚度均显著小于左腿，且力峰值时间大于左腿(p < 0.05)。当运动员单腿着地时，左腿受伤的风险大于右腿。在训练过程中，运动员应加强两条腿尤其是左腿的稳定性训练，以减少运动损伤。
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Pub Date : 2021-01-01DOI: 10.32604/mcb.2021.016246
Xia Xie
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