A SOFT COMPUTING–BASED DECISION SUPPORT SYSTEM FOR MULTIDIMENSIONAL ASSESSMENT OF NON-LINEAR COORDINATIVE PERFORMANCE IN COMBAT SPORT ATHLETES USING A FUZZY INFERENCE SYSTEM
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Jun 11, 2026
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Ram Niwas
https://orcid.org/0009-0003-4366-5045
Monika Verma
https://orcid.org/0009-0000-7044-2507
Avnesh Verma
https://orcid.org/0009-0009-2743-0063
https://orcid.org/0009-0003-4366-5045
Monika Verma
https://orcid.org/0009-0000-7044-2507
Avnesh Verma
https://orcid.org/0009-0009-2743-0063
Abstract
This research establishes a Fuzzy Inference System (FIS) aimed at modeling, analyzing, and forecasting the coordinative capabilities of male athletes in judo and wrestling, with an emphasis on non-linear variables such as balance, reaction time, and agility. These coordinative elements are intricate and qualitative, necessitating a fuzzy logic methodology for enhanced accuracy in evaluation, as conventional binary assessments (e.g., good/bad) frequently overlook the subtleties of performance. The FIS classifies athletes’ capabilities into fuzzy categories (Low, Medium, High), facilitating a more detailed examination of their strengths and weaknesses. By incorporating all physical parameters, it is determined that factors such as LBM (Lean Body Mass), PBF% (Percent Body Fat), BMI (Body Mass Index), and BMR (Basal Metabolic Rate) indicate that the athletes are advancing in both physical conditioning and sport-specific skills. The results imply that the implemented training regimen has a beneficial effect on athletic performance, as shown by positive trends in these parameters. Additionally, the research investigates the incorporation of wearable sensors and biomarkers to improve the accuracy of performance assessments and offer more tailored training protocols. This fuzzy logic framework serves as a robust instrument for optimizing player profiling, talent identification, and training strategies in combat sports, aiding coaches in making better-informed decisions and enhancing overall athlete development.
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Keywords
Fuzzy Inference System (FIS), Wrestling, Judo, Combat Sport Athletes, Multidimensional Non-Linear Coordinative Performance
References
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8. Cataldi, D., Bennett, J. P., Wong, M. C., et al. (2024). Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study. Clinical Nutrition, 43(1), 284–294. https://doi.org/10.1016/j.clnu.2023.10.002
9. Durnin, J. V. G. A., & Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness. British Journal of Nutrition, 32(1), 77–97.
10. Franchini, E., Brito, C. J., & Artioli, G. G. (2012). Weight loss in combat sports: Physiological, psychological and performance effects. Journal of the International Society of Sports Nutrition, 9(1), 52.
11. Franchini, E., & Takito, M. Y. (2014). Physiological profiles of judo athletes. Sports Medicine, 44(3), 361–373.
12. Giovanelli, L., Biganzoli, G., Spataro, A., et al. (2024). Body composition assessment in Olympic athletes with different training loads. Acta Diabetologica, 61(3), 361–372.
13. Gobbo, L., et al. (2019). Use of wearable sensors for monitoring judo training loads. Sensors, 19(18), 3987.
14. Helms, E. R., Aragon, A. A., & Fitschen, P. J. (2014). Evidence-based recommendations for natural bodybuilding contest preparation: Nutrition and supplementation. Journal of the International Society of Sports Nutrition, 11, 20.
15. Khaithi, K., & Pungding, L. (2023). A comparative study of speed, agility and strength endurance between judo and wrestling players. Journal of ReAttach Therapy and Developmental Diversities, 6(1), 1987–1991.
16. Lafont, F., & Balmat, J. F. (2002). Optimized fuzzy control of a greenhouse. Fuzzy Sets and Systems, 128(1), 47–59.
17. Loturco, I., et al. (2017). Strength and power profiling of elite combat athletes. Journal of Sports Sciences, 35(12), 1180–1187.
18. Marques, V., Coswig, V., Viana, R., et al. (2019). Physical fitness and anthropometric measures of young Brazilian judo and wrestling athletes. Sports, 7(2), 38.
19. Maughan, R. J., et al. (2007). Nutrition and hydration for combat sport athletes. Journal of Sports Sciences, 25(1), S3–S10.
20. Moura, D. J., Naas, I. A., Queiroz, M. P. G., & Amendola, E. (2004). Estimating thermal comfort and solar orientation in broiler housing using fuzzy logic. Proceedings of the 6th Latin American and Caribbean Congress of Agricultural Engineering.
21. Nelson, K. M., Weinsier, R. L., Long, C. L., & Schutz, Y. (1992). Prediction of resting energy expenditure from fat-free mass and fat mass. American Journal of Clinical Nutrition, 56(5), 848–856.
22. Nevill, M. E., et al. (2014). Muscle mass and performance relationships in combat athletes. European Journal of Applied Physiology, 114(8), 1687–1695.
23. Niwas, R., Verma, M., Verma, A., et al. (2025). Neuromotor adaptations and coordinative ability differences in judo and wrestling athletes from Haryana, India. Journal of International Education and Practice, 8(2).
24. Ostojic, S. M., Mazic, S., & Dikic, N. (2006). Profiling in Olympic judo athletes: Physical and physiological characteristics. Journal of Sports Medicine and Physical Fitness, 46(3), 364–371.
25. Rani, R., Verma, M., & Verma, A. (2025). Application of fuzzy inference system for interpreting physical fitness metrics of trainee athletes. Journal of Electronic Information Systems, 7(2), 13–24.
26. Santos, D. A., et al. (2015). Body composition and physical fitness in combat athletes. European Journal of Sport Science, 15(3), 263–272.
27. Sayers, S. P., et al. (2011). Predicting performance using lean body mass and neuromuscular tests in combat athletes. Journal of Strength and Conditioning Research, 25(5), 1353–1360.
28. Sharma, A., & Verma, A. (2018). An automated soil pH and EC indicator cum controller system for nutrigation adjustment. AIRO International Research Journal, 4(3), 182–189.
29. Singh, N., Devi, A., Kumar, S., et al. (2015). Response surface methodology for standardisation of lignocellulosic biomass saccharification efficiency of NSF-2 fungus isolate. Journal of Environmental Biology, 36(4), 903–908.
30. Spady, D. W., Schiff, D., & Szymanski, W. A. (1987). A description of the changing body composition of the growing premature infant. Journal of Pediatric Gastroenterology and Nutrition, 6(5), 730–738.
31. Tan, B., et al. (2009). Muscle architecture and performance in elite wrestlers. Journal of Sports Sciences, 27(6), 563–573.
32. Verma, A., Dhingra, S., & Soni, M. K. (2009a). VLSI-cell placement technique for architecture of field programmable gate array design. Turkish Journal of Electrical Engineering & Computer Sciences, 17(3), 327–336.
33. Verma, A., Dhingra, S., & Soni, M. K. (2009b). Design and synthesis of FPGA for speed control of induction motor. International Journal of Physical Sciences, 4(11), 645–650.
34. Verma, A., Dhingra, S., & Soni, M. K. (2009c). PID behavior of FPGA fed IGBT based inverter for induction motor in industrial applications. International Journal of Computer Science & Systems Engineering Information Technology, 2(2).
35. Verma, A., & Tiwari, V. A. (2013). Optimum fuzzy-based approach to improve instrument performance affected by environmental conditions. Serbian Journal of Electrical Engineering, 10(2), 309–318.
36. Verma, A., Kaur, G., & Nagpal, M. (2019). A fuzzy-based artificial intelligence approach to detect caries from digital X-ray data extracted through edge detection technique in pixel values. International Journal of Research and Analytical Reviews, 6(2), 253–261.
37. Verma, A., & Tiwari, V. A. (2019). A fuzzy logic controller to minimize the effect of input temperature fluctuations on the deviation of relative humidity to avoid instrument deterioration. International Journal of Research in Advent Technology, 7(6), 73–77.
38. Wang, Z. M., Deurenberg, P., Guo, S. S., et al. (1998). Six-compartment body composition model: Inter-method comparisons of total body fat measurement. International Journal of Obesity, 22(4), 329–337.
39. Wang, Z., Deurenberg, P., Wang, W., et al. (1999). Hydration of fat-free body mass: Review and critique of a classic body-composition constant. American Journal of Clinical Nutrition, 69(5), 833–841.
40. Zupan, M., et al. (2019). The impact of anthropometric factors on judo performance. Journal of Sports Sciences, 37(14), 1653–1661.
2. Bridge, C. A., & Jones, M. A. (2006). The effect of weight loss on judo-related performance. Journal of Sports Science & Medicine, 5(2), 125–131.
3. Brito, C. J., Marins, J. C. B., & Franchini, E. (2012). Physical performance and anthropometry of elite judo athletes. Journal of Strength and Conditioning Research, 26(6), 1664–1671.
4. Burke, L. M., Loucks, A. B., & Broad, N. (2006). Energy and carbohydrate for training and recovery. Journal of Sports Sciences, 24(7), 675–685. https://doi.org/10.1080/02640410500482602
5. Çağlar, E. Ç., Kutlu, M., & Taşkıran, C. (2024). Comparison of some physical characteristics of young judoka and wrestlers. International Journal of Disability, Sports and Health Sciences, 7(3), 615–620.
6. Campa, F., Toselli, S., Mazzilli, M., Gobbo, L. A., & Coratella, G. (2021). Assessment of body composition in athletes: A narrative review of available methods with special reference to quantitative and qualitative bioimpedance analysis. Nutrients, 13(5), 1620. https://doi.org/10.3390/nu13051620
7. Carvalho de Moura, R., de Moura Costa, C., Pinheiro Ferreira, C., et al. (2025). Body composition and physical performance of mountain bike athletes. Scientific Reports, 15, 3329. https://doi.org/10.1038/s41598-025-12345-6
8. Cataldi, D., Bennett, J. P., Wong, M. C., et al. (2024). Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study. Clinical Nutrition, 43(1), 284–294. https://doi.org/10.1016/j.clnu.2023.10.002
9. Durnin, J. V. G. A., & Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness. British Journal of Nutrition, 32(1), 77–97.
10. Franchini, E., Brito, C. J., & Artioli, G. G. (2012). Weight loss in combat sports: Physiological, psychological and performance effects. Journal of the International Society of Sports Nutrition, 9(1), 52.
11. Franchini, E., & Takito, M. Y. (2014). Physiological profiles of judo athletes. Sports Medicine, 44(3), 361–373.
12. Giovanelli, L., Biganzoli, G., Spataro, A., et al. (2024). Body composition assessment in Olympic athletes with different training loads. Acta Diabetologica, 61(3), 361–372.
13. Gobbo, L., et al. (2019). Use of wearable sensors for monitoring judo training loads. Sensors, 19(18), 3987.
14. Helms, E. R., Aragon, A. A., & Fitschen, P. J. (2014). Evidence-based recommendations for natural bodybuilding contest preparation: Nutrition and supplementation. Journal of the International Society of Sports Nutrition, 11, 20.
15. Khaithi, K., & Pungding, L. (2023). A comparative study of speed, agility and strength endurance between judo and wrestling players. Journal of ReAttach Therapy and Developmental Diversities, 6(1), 1987–1991.
16. Lafont, F., & Balmat, J. F. (2002). Optimized fuzzy control of a greenhouse. Fuzzy Sets and Systems, 128(1), 47–59.
17. Loturco, I., et al. (2017). Strength and power profiling of elite combat athletes. Journal of Sports Sciences, 35(12), 1180–1187.
18. Marques, V., Coswig, V., Viana, R., et al. (2019). Physical fitness and anthropometric measures of young Brazilian judo and wrestling athletes. Sports, 7(2), 38.
19. Maughan, R. J., et al. (2007). Nutrition and hydration for combat sport athletes. Journal of Sports Sciences, 25(1), S3–S10.
20. Moura, D. J., Naas, I. A., Queiroz, M. P. G., & Amendola, E. (2004). Estimating thermal comfort and solar orientation in broiler housing using fuzzy logic. Proceedings of the 6th Latin American and Caribbean Congress of Agricultural Engineering.
21. Nelson, K. M., Weinsier, R. L., Long, C. L., & Schutz, Y. (1992). Prediction of resting energy expenditure from fat-free mass and fat mass. American Journal of Clinical Nutrition, 56(5), 848–856.
22. Nevill, M. E., et al. (2014). Muscle mass and performance relationships in combat athletes. European Journal of Applied Physiology, 114(8), 1687–1695.
23. Niwas, R., Verma, M., Verma, A., et al. (2025). Neuromotor adaptations and coordinative ability differences in judo and wrestling athletes from Haryana, India. Journal of International Education and Practice, 8(2).
24. Ostojic, S. M., Mazic, S., & Dikic, N. (2006). Profiling in Olympic judo athletes: Physical and physiological characteristics. Journal of Sports Medicine and Physical Fitness, 46(3), 364–371.
25. Rani, R., Verma, M., & Verma, A. (2025). Application of fuzzy inference system for interpreting physical fitness metrics of trainee athletes. Journal of Electronic Information Systems, 7(2), 13–24.
26. Santos, D. A., et al. (2015). Body composition and physical fitness in combat athletes. European Journal of Sport Science, 15(3), 263–272.
27. Sayers, S. P., et al. (2011). Predicting performance using lean body mass and neuromuscular tests in combat athletes. Journal of Strength and Conditioning Research, 25(5), 1353–1360.
28. Sharma, A., & Verma, A. (2018). An automated soil pH and EC indicator cum controller system for nutrigation adjustment. AIRO International Research Journal, 4(3), 182–189.
29. Singh, N., Devi, A., Kumar, S., et al. (2015). Response surface methodology for standardisation of lignocellulosic biomass saccharification efficiency of NSF-2 fungus isolate. Journal of Environmental Biology, 36(4), 903–908.
30. Spady, D. W., Schiff, D., & Szymanski, W. A. (1987). A description of the changing body composition of the growing premature infant. Journal of Pediatric Gastroenterology and Nutrition, 6(5), 730–738.
31. Tan, B., et al. (2009). Muscle architecture and performance in elite wrestlers. Journal of Sports Sciences, 27(6), 563–573.
32. Verma, A., Dhingra, S., & Soni, M. K. (2009a). VLSI-cell placement technique for architecture of field programmable gate array design. Turkish Journal of Electrical Engineering & Computer Sciences, 17(3), 327–336.
33. Verma, A., Dhingra, S., & Soni, M. K. (2009b). Design and synthesis of FPGA for speed control of induction motor. International Journal of Physical Sciences, 4(11), 645–650.
34. Verma, A., Dhingra, S., & Soni, M. K. (2009c). PID behavior of FPGA fed IGBT based inverter for induction motor in industrial applications. International Journal of Computer Science & Systems Engineering Information Technology, 2(2).
35. Verma, A., & Tiwari, V. A. (2013). Optimum fuzzy-based approach to improve instrument performance affected by environmental conditions. Serbian Journal of Electrical Engineering, 10(2), 309–318.
36. Verma, A., Kaur, G., & Nagpal, M. (2019). A fuzzy-based artificial intelligence approach to detect caries from digital X-ray data extracted through edge detection technique in pixel values. International Journal of Research and Analytical Reviews, 6(2), 253–261.
37. Verma, A., & Tiwari, V. A. (2019). A fuzzy logic controller to minimize the effect of input temperature fluctuations on the deviation of relative humidity to avoid instrument deterioration. International Journal of Research in Advent Technology, 7(6), 73–77.
38. Wang, Z. M., Deurenberg, P., Guo, S. S., et al. (1998). Six-compartment body composition model: Inter-method comparisons of total body fat measurement. International Journal of Obesity, 22(4), 329–337.
39. Wang, Z., Deurenberg, P., Wang, W., et al. (1999). Hydration of fat-free body mass: Review and critique of a classic body-composition constant. American Journal of Clinical Nutrition, 69(5), 833–841.
40. Zupan, M., et al. (2019). The impact of anthropometric factors on judo performance. Journal of Sports Sciences, 37(14), 1653–1661.
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Niwas , R., Verma, M., & Verma, A. (2026). A SOFT COMPUTING–BASED DECISION SUPPORT SYSTEM FOR MULTIDIMENSIONAL ASSESSMENT OF NON-LINEAR COORDINATIVE PERFORMANCE IN COMBAT SPORT ATHLETES USING A FUZZY INFERENCE SYSTEM. Physical Education and Sport Through The Centuries, 13(1), 113–128. Retrieved from https://phedss.fsfv-pr.rs/index.php/phedss/article/view/76
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