Pub Date : 2023-09-26DOI: 10.1177/17543371231199810
Jader Sant’ Ana, Rafael Lima Kons, Daniele Detanico, Fernando Diefenthaeler
New technologies have amplified the possibilities for processing and incorporating data and scientific methods in algorithms through the integration of the use of mobile technology and a wide range of wearables that allow acquisition metrics in real-time. These technologies arise as a possible alternative to supply market demand and to present practical solutions to problems that coaches and athletes face in their daily routines. Concerning biomechanical assessment in combat sports (i.e. reaction time, velocity, and force), the literature is scarce regarding studies that carried out surveys of new assessments and monitoring technologies, with solutions for coaches and athletes. Therefore, the current study aimed to investigate, through a literature review, mobile technologies available on the market for biomechanical analyses in combat sports modalities. Significant growth has been observed in the number of studies involving mobile technologies with practical tools for biomechanical assessment in combat sports athletes. However, only seven technological proposals presented scientific reliability studies, and six assessed validity, showing the necessity of more original articles to investigate scientific validation. As a suggestion, a flowchart is presented with operational guidelines for the research and development of new technologies for biomechanical assessment and monitoring in combat sports in real-time.
{"title":"The use of mobile solutions for biomechanical assessment in combat sports: A narrative review","authors":"Jader Sant’ Ana, Rafael Lima Kons, Daniele Detanico, Fernando Diefenthaeler","doi":"10.1177/17543371231199810","DOIUrl":"https://doi.org/10.1177/17543371231199810","url":null,"abstract":"New technologies have amplified the possibilities for processing and incorporating data and scientific methods in algorithms through the integration of the use of mobile technology and a wide range of wearables that allow acquisition metrics in real-time. These technologies arise as a possible alternative to supply market demand and to present practical solutions to problems that coaches and athletes face in their daily routines. Concerning biomechanical assessment in combat sports (i.e. reaction time, velocity, and force), the literature is scarce regarding studies that carried out surveys of new assessments and monitoring technologies, with solutions for coaches and athletes. Therefore, the current study aimed to investigate, through a literature review, mobile technologies available on the market for biomechanical analyses in combat sports modalities. Significant growth has been observed in the number of studies involving mobile technologies with practical tools for biomechanical assessment in combat sports athletes. However, only seven technological proposals presented scientific reliability studies, and six assessed validity, showing the necessity of more original articles to investigate scientific validation. As a suggestion, a flowchart is presented with operational guidelines for the research and development of new technologies for biomechanical assessment and monitoring in combat sports in real-time.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-26DOI: 10.1177/17543371231197323
Ana María Magaz-González, Marta García-Tascón, César Sahelices-Pinto, Ana María Gallardo, Juan Carlos Guevara Pérez
The COVID-19 pandemic in 2020, has accelerated technological immersion into society, economy and public administrations. The sports organisations are no strangers to this digitisation and must carry out their own digital transformation. However, investment in digital technology must be preceded by a diagnosis of the technological needs of each sports entity. Said mapping will help organisations know the most appropriate technological tools for their core businesses so they can properly design their digital transformation strategies. The objectives of this study were to design and create a tool to understand the digital structure of Spanish sports. The result has been the configuration of an instrument that includes the description of different technologies and different digital competencies specific to the sports industry, and which allows individuals to know the use, importance, perceived difficulty of use and economic accessibility of available technologies, as well as the degree of developed competency in the different sports organisations. It is concluded that the creation and application of a consultation instrument on digitalisation is the first and necessary step to carry out relevant, in-depth, valid and replicable research, which allows information to be gathered on the digitalisation needs of sports organisations to design their digital transformation roadmap and that the aids for digital transformation is distributed efficiently.
{"title":"Technology and digital transformation for the structural reform of the sports industry: Building the roadmap","authors":"Ana María Magaz-González, Marta García-Tascón, César Sahelices-Pinto, Ana María Gallardo, Juan Carlos Guevara Pérez","doi":"10.1177/17543371231197323","DOIUrl":"https://doi.org/10.1177/17543371231197323","url":null,"abstract":"The COVID-19 pandemic in 2020, has accelerated technological immersion into society, economy and public administrations. The sports organisations are no strangers to this digitisation and must carry out their own digital transformation. However, investment in digital technology must be preceded by a diagnosis of the technological needs of each sports entity. Said mapping will help organisations know the most appropriate technological tools for their core businesses so they can properly design their digital transformation strategies. The objectives of this study were to design and create a tool to understand the digital structure of Spanish sports. The result has been the configuration of an instrument that includes the description of different technologies and different digital competencies specific to the sports industry, and which allows individuals to know the use, importance, perceived difficulty of use and economic accessibility of available technologies, as well as the degree of developed competency in the different sports organisations. It is concluded that the creation and application of a consultation instrument on digitalisation is the first and necessary step to carry out relevant, in-depth, valid and replicable research, which allows information to be gathered on the digitalisation needs of sports organisations to design their digital transformation roadmap and that the aids for digital transformation is distributed efficiently.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1177/17543371231200056
Diego Marqués-Jiménez, Javier Raya-González, Silvia Sánchez-Díaz, Daniel Castillo
This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches ( p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.
{"title":"A Random Forest clustering to explore the influence of physical fitness level of youth basketball players on match-related physical performance","authors":"Diego Marqués-Jiménez, Javier Raya-González, Silvia Sánchez-Díaz, Daniel Castillo","doi":"10.1177/17543371231200056","DOIUrl":"https://doi.org/10.1177/17543371231200056","url":null,"abstract":"This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches ( p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1177/17543371231199812
Angel Carnero-Diaz, Javier Pecci, Gonzalo Reverte-Pagola, Marzo Edir Da Silva-Grigoletto
Training process in elite padel players is influenced by travels and competitions density. The irregularity in the workloads, as well as demands of the season, could affect the musculoskeletal structures. Strength training has a protective role against the injury incidence, but the competitive context does not always allow adequate periodization of training and thus achieve adaptations. The aim of this study is to analyze, using technological tools, if improvements in player’s fitness are accompanied by improvements in sport performance through a case study. An elite padel player was analyzed during the 2021 season. Physical fitness was evaluated using different technological tools. Athlete monitoring was carried out using self-reported forms and sport performance was assessed through the results obtained in the World Padel Tour ranking at the end of the season. During the training process, multidimensional training was carried out in order to achieve the maximum availability of specific loads through coadjuvant training. Results of the assessment show slight improvements in all fitness tests. Assessment of sport performance reports an increased number of victories and a better position in the professional ranking. Musculoskeletal improvements helped the athlete’s workload tolerance, allowing overall improvement in padel performance. The training approach from this study has shown to be effective in maintaining or even improving force-producing capacity in lower and upper limbs, force-velocity relationship, agility and sport performance, despite the high competitive density. This work provides coaches with a practical approach to assess, monitor and design a competitive season for an elite padel player.
{"title":"The use of technology in the assessment and monitorization of a season in a professional padel player: A case study","authors":"Angel Carnero-Diaz, Javier Pecci, Gonzalo Reverte-Pagola, Marzo Edir Da Silva-Grigoletto","doi":"10.1177/17543371231199812","DOIUrl":"https://doi.org/10.1177/17543371231199812","url":null,"abstract":"Training process in elite padel players is influenced by travels and competitions density. The irregularity in the workloads, as well as demands of the season, could affect the musculoskeletal structures. Strength training has a protective role against the injury incidence, but the competitive context does not always allow adequate periodization of training and thus achieve adaptations. The aim of this study is to analyze, using technological tools, if improvements in player’s fitness are accompanied by improvements in sport performance through a case study. An elite padel player was analyzed during the 2021 season. Physical fitness was evaluated using different technological tools. Athlete monitoring was carried out using self-reported forms and sport performance was assessed through the results obtained in the World Padel Tour ranking at the end of the season. During the training process, multidimensional training was carried out in order to achieve the maximum availability of specific loads through coadjuvant training. Results of the assessment show slight improvements in all fitness tests. Assessment of sport performance reports an increased number of victories and a better position in the professional ranking. Musculoskeletal improvements helped the athlete’s workload tolerance, allowing overall improvement in padel performance. The training approach from this study has shown to be effective in maintaining or even improving force-producing capacity in lower and upper limbs, force-velocity relationship, agility and sport performance, despite the high competitive density. This work provides coaches with a practical approach to assess, monitor and design a competitive season for an elite padel player.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1177/17543371231196814
Manju Rana, Vikas Mittal
Equestrian sports require horses to possess physical and mental attributes such as agility, strength, balance, and gymnastic skills. Performance analysis is critical in evaluating a horse’s performance, which involves assessing athleticism, gait quality, jumping ability, and general health. Assessing the kinematics of horses is crucial for selecting, training, and managing sports horses. Understanding a horse’s gait pattern and detecting Ground Reaction Forces (GRF) help diagnose lameness in the horse. Traditional gait analysis methods are performed visually, which can be biased due to subjectivity and human error. Optical motion capture (OMC) technology for equine gait analysis is expensive and ideal for indoor use. Wearable inertial measurement units (IMUs) offer a cost-effective alternative for analyzing kinematic parameters. This study has devised novel wearable sensor devices for horses and riders to measure forces acting on the legs and body of the horse and the orientation of their legs during field performance. Ground Reaction Forces (GRF) were measured using 100g accelerometer data from each leg to assist owners and riders in analyzing the magnitude of forces and detecting any anomalies. Machine-learning models were developed to classify horse movements, such as jumps, stands, gallops, and trots, using features extracted from the data collected by wearable sensor devices. These models were compared to identify the most effective model for accurately classifying horse movements. This approach provides a valuable tool for recognizing patterns and trends in the data, enabling owners and riders to make informed decisions about training and management strategies.
{"title":"Horse gait analysis using wearable inertial sensors and machine learning","authors":"Manju Rana, Vikas Mittal","doi":"10.1177/17543371231196814","DOIUrl":"https://doi.org/10.1177/17543371231196814","url":null,"abstract":"Equestrian sports require horses to possess physical and mental attributes such as agility, strength, balance, and gymnastic skills. Performance analysis is critical in evaluating a horse’s performance, which involves assessing athleticism, gait quality, jumping ability, and general health. Assessing the kinematics of horses is crucial for selecting, training, and managing sports horses. Understanding a horse’s gait pattern and detecting Ground Reaction Forces (GRF) help diagnose lameness in the horse. Traditional gait analysis methods are performed visually, which can be biased due to subjectivity and human error. Optical motion capture (OMC) technology for equine gait analysis is expensive and ideal for indoor use. Wearable inertial measurement units (IMUs) offer a cost-effective alternative for analyzing kinematic parameters. This study has devised novel wearable sensor devices for horses and riders to measure forces acting on the legs and body of the horse and the orientation of their legs during field performance. Ground Reaction Forces (GRF) were measured using 100g accelerometer data from each leg to assist owners and riders in analyzing the magnitude of forces and detecting any anomalies. Machine-learning models were developed to classify horse movements, such as jumps, stands, gallops, and trots, using features extracted from the data collected by wearable sensor devices. These models were compared to identify the most effective model for accurately classifying horse movements. This approach provides a valuable tool for recognizing patterns and trends in the data, enabling owners and riders to make informed decisions about training and management strategies.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135397300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1177/17543371231199814
Fatma Hilal Yagin, Uday CH Hasan, Filipe Manuel Clemente, Ozgur Eken, Georgian Badicu, Mehmet Gulu
This study aimed to predict professional soccer players’ positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.
{"title":"Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables","authors":"Fatma Hilal Yagin, Uday CH Hasan, Filipe Manuel Clemente, Ozgur Eken, Georgian Badicu, Mehmet Gulu","doi":"10.1177/17543371231199814","DOIUrl":"https://doi.org/10.1177/17543371231199814","url":null,"abstract":"This study aimed to predict professional soccer players’ positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135397460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1177/17543371231194283
Markel Rico-González, Francisco Tomás González Férnandez, Rafael Oliveira, F. Clemente
Acute: chronic workload ratio (ACWR) and training monotony have been criticized as injury risk predictors. Therefore, the use of intensity measures should be oriented to understand the variations of intensity across the season. The aim of this systematic review is to summarize the main evidence about the ACWR and training monotony variations over the season in professional soccer players. The search was made in PubMed, SPORTDiscus, and FECYT according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From the 225 studies initially identified, 27 were fully reviewed, and their outcome measures were extracted and analyzed. Existing literature revealed a variety of designs, ACWR and training monotony ranges, variables assessed and durations of the studies. Overall, the range values for ACWR were 0.4–3.39 AU, while those focused on monotony were 0.49–5.7 AU. Regarding ACWR, the ratios located around 0.85–1.25 could predict lower risk values and ratios around ≥1.50 could predict higher risk values. On the contrary, with respect to training monotony, the ratios are approximately between 0.5 and 2.00 (low values in the preseason and low competition weeks and high values when soccer players are in highly scheduled competition weeks). Nevertheless, ACWR and training monotony methods should be addressed and considered based on their real value before using this indicator to reduce injury risk. In fact, the data did not conclusively define injured and non-injured players. For this reason, utilizing standardized approaches will allow for more precise conclusions about professional soccer players.
{"title":"Acute:chronic workload ratio and training monotony variations over the season in professional soccer: A systematic review","authors":"Markel Rico-González, Francisco Tomás González Férnandez, Rafael Oliveira, F. Clemente","doi":"10.1177/17543371231194283","DOIUrl":"https://doi.org/10.1177/17543371231194283","url":null,"abstract":"Acute: chronic workload ratio (ACWR) and training monotony have been criticized as injury risk predictors. Therefore, the use of intensity measures should be oriented to understand the variations of intensity across the season. The aim of this systematic review is to summarize the main evidence about the ACWR and training monotony variations over the season in professional soccer players. The search was made in PubMed, SPORTDiscus, and FECYT according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From the 225 studies initially identified, 27 were fully reviewed, and their outcome measures were extracted and analyzed. Existing literature revealed a variety of designs, ACWR and training monotony ranges, variables assessed and durations of the studies. Overall, the range values for ACWR were 0.4–3.39 AU, while those focused on monotony were 0.49–5.7 AU. Regarding ACWR, the ratios located around 0.85–1.25 could predict lower risk values and ratios around ≥1.50 could predict higher risk values. On the contrary, with respect to training monotony, the ratios are approximately between 0.5 and 2.00 (low values in the preseason and low competition weeks and high values when soccer players are in highly scheduled competition weeks). Nevertheless, ACWR and training monotony methods should be addressed and considered based on their real value before using this indicator to reduce injury risk. In fact, the data did not conclusively define injured and non-injured players. For this reason, utilizing standardized approaches will allow for more precise conclusions about professional soccer players.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43295732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1177/17543371231196340
Sara Santos, J. Parraca, Joana Alegrete, Carolina Alexandra Cabo, Filipe Melo, Orlando Fernandes
This study examined the effects of Ashtanga Vinyasa Yoga Supta on Center of Pressure (CoP) displacement in healthy student pilots, using the Biosignals Plux force platform, under the premise that yoga would lead to improvements in postural control responses. CoP response was analyzed by the Plux (Portugal) one-dimensional force platform. A total of 18 military pilots participated in this study. The pilots were in their Portuguese Air Force Academy course “Masters in Military Aeronautics: Aviator Pilot Specialist,” also called Tirocinium. Participants were randomly assigned to yoga classes (intervention group) or a waiting list (control group) and completed a flight emergency protocol in a flight simulator. CoP displacement was collected before and after all these maneuvers had been completed and both measures occurred before (baseline values) and after a 12-week yoga program. Although the differences observed between groups are not significant, after calculating the effect size, we can theorize that the intervention group maintains CoP displacement before and after flight and the control group has a higher CoP displacement after flight simulation. CoP information collected through noninvasive portable devices such as the Biosignals Plux force platform can relay important information quickly and easily. Knowing under what circumstances pilots are affected can then lead to development or enhancement of training strategies to improve those psychophysiological responses. In this study the effects, while not significant, are present, so it may be necessary to add more weeks of training to make the yoga program effective.
本研究采用Biosignals plus力量平台，在瑜伽可以改善姿势控制反应的前提下，研究了阿斯汤伽串联瑜伽Supta对健康飞行员学生压力中心位移的影响。采用Plux(葡萄牙)一维力平台对CoP响应进行分析。共有18名军事飞行员参与了这项研究。这些飞行员在葡萄牙空军学院学习“军事航空硕士:飞行员专家”课程，也被称为Tirocinium。参与者被随机分配到瑜伽班(干预组)或等候名单(对照组)，并在飞行模拟器中完成飞行应急协议。在所有这些动作完成之前和之后收集CoP位移，这两个测量都发生在12周瑜伽计划之前(基线值)和之后。虽然组间差异不显著，但经过效应量的计算，我们可以推测干预组在飞行前后保持CoP位移，而对照组在飞行模拟后CoP位移更高。通过无创便携式设备(如Biosignals plus force平台)收集的CoP信息可以快速轻松地传递重要信息。了解飞行员在什么情况下会受到影响，然后可以开发或加强训练策略，以改善这些心理生理反应。在这项研究中，效果虽然不显著，但确实存在，因此可能有必要增加更多的训练周，以使瑜伽项目有效。
{"title":"The effects of a 12-week yoga program on the CoP of military pilots before and after a flight emergency simulation using Biosignals Plux force platform","authors":"Sara Santos, J. Parraca, Joana Alegrete, Carolina Alexandra Cabo, Filipe Melo, Orlando Fernandes","doi":"10.1177/17543371231196340","DOIUrl":"https://doi.org/10.1177/17543371231196340","url":null,"abstract":"This study examined the effects of Ashtanga Vinyasa Yoga Supta on Center of Pressure (CoP) displacement in healthy student pilots, using the Biosignals Plux force platform, under the premise that yoga would lead to improvements in postural control responses. CoP response was analyzed by the Plux (Portugal) one-dimensional force platform. A total of 18 military pilots participated in this study. The pilots were in their Portuguese Air Force Academy course “Masters in Military Aeronautics: Aviator Pilot Specialist,” also called Tirocinium. Participants were randomly assigned to yoga classes (intervention group) or a waiting list (control group) and completed a flight emergency protocol in a flight simulator. CoP displacement was collected before and after all these maneuvers had been completed and both measures occurred before (baseline values) and after a 12-week yoga program. Although the differences observed between groups are not significant, after calculating the effect size, we can theorize that the intervention group maintains CoP displacement before and after flight and the control group has a higher CoP displacement after flight simulation. CoP information collected through noninvasive portable devices such as the Biosignals Plux force platform can relay important information quickly and easily. Knowing under what circumstances pilots are affected can then lead to development or enhancement of training strategies to improve those psychophysiological responses. In this study the effects, while not significant, are present, so it may be necessary to add more weeks of training to make the yoga program effective.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47005160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1177/17543371231194291
G. Praça, Leandro Henrique Albuquerque Brandão, Cristóvão de Oliveira Abreu, Pedro Henrique de Almeida Oliveira, A. G. D. de Andrade
This study examined positional and event-based tactical variables during the Men’s 2022 FIFA World Cup to detect differences between winning and losing teams and to verify which variables would better predict the goal difference between the teams. All 64 matches played during the competition were initially considered. Due to the purpose of the current article, draw matches were excluded from the sample. The data were compared between the match statuses using a MANOVA. A stepwise multiple linear regression was used to detect which variables predict the outcome of the matches. Results indicated differences between winning and losing in Line Breaks per Pass ( p = 0.011), Defensive Line Break per Pass ( p = 0.004), Final Third Phase Height ( p = 0.023), and Width ( p < 0.001). The best predictors of the goal difference were the Final Third Phase Width (standardized beta = −0.389), the Completed Line Breaks (standardized beta = 0.716), and the movements to receive in the progression phase in between (standardized beta = −0.491). It is concluded that positioning the team closer to the central corridor in the last third of the pitch and successfully breaking the defensive lines increases winning probability during the competition.
{"title":"Novel tactical insights from Men’s 2022 FIFA World Cup: Which performance indicators explain the teams’ goal difference?","authors":"G. Praça, Leandro Henrique Albuquerque Brandão, Cristóvão de Oliveira Abreu, Pedro Henrique de Almeida Oliveira, A. G. D. de Andrade","doi":"10.1177/17543371231194291","DOIUrl":"https://doi.org/10.1177/17543371231194291","url":null,"abstract":"This study examined positional and event-based tactical variables during the Men’s 2022 FIFA World Cup to detect differences between winning and losing teams and to verify which variables would better predict the goal difference between the teams. All 64 matches played during the competition were initially considered. Due to the purpose of the current article, draw matches were excluded from the sample. The data were compared between the match statuses using a MANOVA. A stepwise multiple linear regression was used to detect which variables predict the outcome of the matches. Results indicated differences between winning and losing in Line Breaks per Pass ( p = 0.011), Defensive Line Break per Pass ( p = 0.004), Final Third Phase Height ( p = 0.023), and Width ( p < 0.001). The best predictors of the goal difference were the Final Third Phase Width (standardized beta = −0.389), the Completed Line Breaks (standardized beta = 0.716), and the movements to receive in the progression phase in between (standardized beta = −0.491). It is concluded that positioning the team closer to the central corridor in the last third of the pitch and successfully breaking the defensive lines increases winning probability during the competition.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45373414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-02DOI: 10.1177/17543371231194011
F. Abut, MF Akay, S. Daneshvar, A. Özcan, D. Heil
This study proposes new non-exercise models for estimating the racing time of cross-country skiers. Machine learning methods employed to build the prediction models include General Regression Neural Network (GRNN), Support Vector Machine (SVM), Multilayer Feed-Forward Artificial Neural Network (MFANN), and Radial Basis Function Neural Network (RBFNN), whereas the Relief-F algorithm combined with a ranker search has been utilized as the feature selector. The self-created data set contains samples collected from 370 cross-country skiers with inhomogeneous capabilities. Each sample in the data set contains physiological variables such as sex, age, height, weight, and body mass index (BMI) combined with an immersive set of survey data. The outcomes suggest that generally, the GRNN-based models exhibit the best prediction performance and can be used as a feasible tool for the prediction of the racing time of cross-country skiers with tolerable root mean square errors (RMSEs). It is seen that inclusion of age and assigned starting wave of cross-country skiers in models leads to much lower RMSEs, suggesting that the racing time of cross-country skiers is highly correlated to these two predictor variables. When compared with the exercise-based models, the proposed non-exercise-based models produce consistently comparable prediction performance for all evaluated machine learning methods. The non-exercise-based models have the relevant benefit of practical feasibility, as the models do not require the skiers to complete physical exercises and are also applicable to a wide range of cross-country skiers.
{"title":"Non-exercise-based racing time prediction of cross-country skiers using machine learning methods combined with Relief-F feature selection","authors":"F. Abut, MF Akay, S. Daneshvar, A. Özcan, D. Heil","doi":"10.1177/17543371231194011","DOIUrl":"https://doi.org/10.1177/17543371231194011","url":null,"abstract":"This study proposes new non-exercise models for estimating the racing time of cross-country skiers. Machine learning methods employed to build the prediction models include General Regression Neural Network (GRNN), Support Vector Machine (SVM), Multilayer Feed-Forward Artificial Neural Network (MFANN), and Radial Basis Function Neural Network (RBFNN), whereas the Relief-F algorithm combined with a ranker search has been utilized as the feature selector. The self-created data set contains samples collected from 370 cross-country skiers with inhomogeneous capabilities. Each sample in the data set contains physiological variables such as sex, age, height, weight, and body mass index (BMI) combined with an immersive set of survey data. The outcomes suggest that generally, the GRNN-based models exhibit the best prediction performance and can be used as a feasible tool for the prediction of the racing time of cross-country skiers with tolerable root mean square errors (RMSEs). It is seen that inclusion of age and assigned starting wave of cross-country skiers in models leads to much lower RMSEs, suggesting that the racing time of cross-country skiers is highly correlated to these two predictor variables. When compared with the exercise-based models, the proposed non-exercise-based models produce consistently comparable prediction performance for all evaluated machine learning methods. The non-exercise-based models have the relevant benefit of practical feasibility, as the models do not require the skiers to complete physical exercises and are also applicable to a wide range of cross-country skiers.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48699440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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