Fencing Training Decision Support System Based on Bayesian Network

Abstract

In order to coordinate with and promote the scientific process of the National Fencing Team, we developed the decision support system for training. In fencing training, we established a two-way reasoning model based on Bayesian Network and found the relationship between training process and physiological indicators. Combined with experienced knowledge and sample data, we did research on knowledge representation, learning methods and reasoning functions of network in this model and ultimately got the formation of a complete set of training process management and analysis system. We took the women's epee team as experimental data and did performance comparison with the BP neural network method. The experiments showed that the model can provide effective decision support for coaches.

A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

“…As such, the sports of aikido, archery, badminton, climbing, counter movement jumping, cricket, fencing, (American/Australian) football, golf, hammer throwing, handball, hockey, karate, kickboxing, ski jumping, skiing, Tai-chi, and yoga, which contained less research regarding their domain (N < 3), are presented here and shown in Table 18. The field research ranged from injury prediction and identification [119,120], to pose recognition and evaluation [121][122][123], virtual coaching and coaching assistants [124][125][126], and VR systems [127,128]. The AI coach system for pose tracking was used by volunteers and a questionnaire reported that they were satisfied with the system.…”

AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance

“…-Контроль -відсутні дослідження щодо фітнесу в тренажерних залах, айкідо, стрільби з лука, метання молота, стрибків на лижах та йоги. Важка атлетика Реальні [113] [114], [115] Катання на лижах Реальні [116] Приватний -Синтетичні [97] Приватний -Реальні [88], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126], [127], [128], [129] Вільні, відкриті наборів даних із домену SST та спортивного «скаутингу» існують, наприклад, [130], [131], [132], [133], [134], [135], [136]).…”

Football Player Decision Support System Baghdad-City as a Case Study

“…Bencheng and Xu [20] applied a DD model which employed serial data, data mining technique, and time of ball passing and possession to provide a feasible road for football players and coach in establishing better system to overcome the other team's strategy by studying football match pattern. Whereas, Yu et al [17] did a study about DSS in providing a given sport team a better performance by recognizing the sports' competition Yu et al [17]. While, Kent and Keith [16] developed a DSS program that can produce several schedule versions for Canadian Football League instead of manual creation method of league schedule.…”

“…Figure 4 shows the main system's interface. The candidate player for the scope of this system should be with the category of age between (12)(13)(14)(15)(16)(17)(18) years ; the youth football category players.…”