Abstract The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p < 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.
摘要:本研究的目的是将惯性传感器集成在锤子内部，以实现实时反馈。在第一步，我们建立了自己的原型来测量径向加速度。第二步是用红外摄像系统进行验证。它是沿着线轴的径向加速度(由传感器测量)与红外摄像系统提供的速度之间的比较。因此，测量速度与加速度之间存在显著相关性(r = 0.99, p < 0.001)。说明该系统可用于链球技术的提高训练。
{"title":"Wireless inertial sensor system for hammer throwing","authors":"Stefan Tiedemann, Gwen Spelly, K. Witte","doi":"10.2478/ijcss-2022-0001","DOIUrl":"https://doi.org/10.2478/ijcss-2022-0001","url":null,"abstract":"Abstract The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p < 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42013714","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}
J. Fahey-Gilmour, J. Heasman, B. Rogalski, B. Dawson, P. Peeling
Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.
{"title":"Can Elite Australian Football Player’s Game Performance Be Predicted?","authors":"J. Fahey-Gilmour, J. Heasman, B. Rogalski, B. Dawson, P. Peeling","doi":"10.2478/ijcss-2021-0004","DOIUrl":"https://doi.org/10.2478/ijcss-2021-0004","url":null,"abstract":"Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41624156","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}
Abstract Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.
{"title":"Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weights","authors":"J. Zháněl, P. Holecek, A. Zderčík","doi":"10.2478/ijcss-2021-0005","DOIUrl":"https://doi.org/10.2478/ijcss-2021-0005","url":null,"abstract":"Abstract Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48612124","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}
M. Connor, David Fagan, B. Watters, F. McCaffery, Michael O'Neill
Abstract This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.
{"title":"Optimizing Team Sport Training With Multi-Objective Evolutionary Computation","authors":"M. Connor, David Fagan, B. Watters, F. McCaffery, Michael O'Neill","doi":"10.2478/ijcss-2021-0006","DOIUrl":"https://doi.org/10.2478/ijcss-2021-0006","url":null,"abstract":"Abstract This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45200369","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}
Abstract To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams. In the linear regression analyses the smallest coefficient of determination (R2.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.
摘要为了控制和监测杠铃力量训练，消费市场上有各种系统。它们为用户提供关于杠铃的速度、加速度和轨迹的信息。一些系统还计算了训练的最大1次重复（1RM），并用它来建议未来训练的个人强度。测试了三个系统：GymAware、PUSH Band 2.0和Vmaxpro。GymAware系统基于线性位置传感器，PUSH Band 2.0和Vmaxpro基于惯性测量单元。本文的目的是确定三个系统在确定三种杠铃力量练习（深蹲、杠铃划船、提举）每次重复的平均速度方面的准确性。使用线性回归分析和Bland-Altman图将三个系统的速度数据与Vicon系统进行比较。在线性回归分析中，可以观察到PUSH Band 2.0在每次锻炼中的最小决定系数（R2）。在Bland-Altman图中，所有系统和所有练习的平均速度差的平均值都接近零。PUSH Band 2.0在协议限制之间的差异最大。对于GymAware和Vmaxpro，这些差异具有可比性。
{"title":"Validation of Velocity Measuring Devices in Velocity Based Strength Training","authors":"Thorben Menrad, Jürgen Edelmann-Nusser","doi":"10.2478/ijcss-2021-0007","DOIUrl":"https://doi.org/10.2478/ijcss-2021-0007","url":null,"abstract":"Abstract To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams. In the linear regression analyses the smallest coefficient of determination (R2.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49175070","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}
G. Nagy, Z. Komka, G. Szathmáry, Péter Katona, L. Gannoruwa, Gergely Erdös, P. Tarjányi, M. Tóth, M. Krepuska, László Grand
Abstract Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles. Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities. We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities. As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.
{"title":"Multimodal Approach for Kayaking Performance Analysis and Improvement","authors":"G. Nagy, Z. Komka, G. Szathmáry, Péter Katona, L. Gannoruwa, Gergely Erdös, P. Tarjányi, M. Tóth, M. Krepuska, László Grand","doi":"10.2478/ijcss-2020-0010","DOIUrl":"https://doi.org/10.2478/ijcss-2020-0010","url":null,"abstract":"Abstract Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles. Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities. We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities. As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43997059","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}
Abstract This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.
{"title":"Optimising Daily Fantasy Sports Teams with Artificial Intelligence","authors":"Ryan Beal, T. Norman, S. Ramchurn","doi":"10.2478/ijcss-2020-0008","DOIUrl":"https://doi.org/10.2478/ijcss-2020-0008","url":null,"abstract":"Abstract This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44836911","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}
Abstract In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Naïve Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.
{"title":"A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL","authors":"Ryan Beal, T. Norman, S. Ramchurn","doi":"10.2478/ijcss-2020-0009","DOIUrl":"https://doi.org/10.2478/ijcss-2020-0009","url":null,"abstract":"Abstract In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Naïve Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42639385","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}
Abstract In team sport Human Activity Recognition (HAR) using inertial measurement units (IMUs) has been limited to athletes performing a set routine in a controlled environment, or identifying a high intensity event within periods of relatively low work load. The purpose of this study was to automatically classify locomotion in an elite sports match where subjects perform rapid changes in movement type, direction, and intensity. Using netball as a test case, six athletes wore a tri-axial accelerometer and gyroscope. Feature extraction of player acceleration and rotation rates was conducted on the time and frequency domain over a 1s sliding window. Applying several machine learning algorithms Support Vector Machines (SVM) was found to have the highest classification accuracy (92.0%, Cohen’s kappa Ƙ = 0.88). Highest accuracy was achieved using both accelerometer and gyroscope features mapped to the time and frequency domain. Time and frequency domain data sets achieved identical classification accuracy (91%). Model accuracy was greatest when excluding windows with two or more classes, however detecting the athlete transitioning between locomotion classes was successful (69%). The proposed method demonstrated HAR of locomotion is possible in elite sport, and a far more efficient process than traditional video coding methods.
{"title":"Automatic Classification of Locomotion in Sport: A Case Study from Elite Netball.","authors":"P. D. Smith, A. Bedford","doi":"10.2478/ijcss-2020-0007","DOIUrl":"https://doi.org/10.2478/ijcss-2020-0007","url":null,"abstract":"Abstract In team sport Human Activity Recognition (HAR) using inertial measurement units (IMUs) has been limited to athletes performing a set routine in a controlled environment, or identifying a high intensity event within periods of relatively low work load. The purpose of this study was to automatically classify locomotion in an elite sports match where subjects perform rapid changes in movement type, direction, and intensity. Using netball as a test case, six athletes wore a tri-axial accelerometer and gyroscope. Feature extraction of player acceleration and rotation rates was conducted on the time and frequency domain over a 1s sliding window. Applying several machine learning algorithms Support Vector Machines (SVM) was found to have the highest classification accuracy (92.0%, Cohen’s kappa Ƙ = 0.88). Highest accuracy was achieved using both accelerometer and gyroscope features mapped to the time and frequency domain. Time and frequency domain data sets achieved identical classification accuracy (91%). Model accuracy was greatest when excluding windows with two or more classes, however detecting the athlete transitioning between locomotion classes was successful (69%). The proposed method demonstrated HAR of locomotion is possible in elite sport, and a far more efficient process than traditional video coding methods.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43167109","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：