{"title":"Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class","authors":"Ahmed Abdulghani Taha, Mohammad Abdulghani Taha","volume":92,"journal":"International Journal of Computer and Information Engineering","pagesStart":1560,"pagesEnd":1565,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10003685","abstract":"This study aims at being acquainted with the using the
\r\nbody fat percentage (%BF) with body Mass Index (BMI) as input
\r\nparameters in fuzzy logic decision support system to predict properly
\r\nthe lifted weight for students at weightlifting class lift according to
\r\nhis abilities instead of traditional manner. The sample included 53
\r\nmale students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28
\r\ncm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI)
\r\n23.42 ± 2.06 kg.m-2, fat mass (FM) = 9.96 ± 3.15 kg and fat
\r\npercentage (% BF) = 13.98 ± 3.51 %.) experienced the weightlifting
\r\nclass as a credit and has variance at BW, Hgt and BMI and FM. BMI
\r\nand % BF were taken as input parameters in FUZZY logic whereas
\r\nthe output parameter was the lifted weight (LW). There were
\r\nstatistical differences between LW values before and after using
\r\nfuzzy logic (Diff 3.55± 2.21, P > 0.001). The percentages of the LW
\r\ncategories proposed by fuzzy logic were 3.77% of students to lift 1.0
\r\nfold of their bodies; 50.94% of students to lift 0.95 fold of their
\r\nbodies; 33.96% of students to lift 0.9 fold of their bodies; 3.77% of
\r\nstudents to lift 0.85 fold of their bodies and 7.55% of students to lift
\r\n0.8 fold of their bodies. The study concluded that the characteristic
\r\nchanges in body composition experienced by students when
\r\nundergoing weightlifting could be utilized side by side with the
\r\nFuzzy logic decision support system to determine the proper
\r\nworkloads consistent with the abilities of students.","references":"[1] N. G. Norgan, \"Anthropometry and physical performance\" in\r\nAnthropometry: the individual and the population, S.J. Ulijaszek and\r\nC.G.N. Mascie-Taylor, Eds, Cambridge University Press, London, 1994.\r\n[2] Brown SP, Miller WC, Eason, JM. Exercise Physiology: Basis of\r\nHuman Movement in Health and Disease. Philadelphia, PA: Lippincott,\r\nWilliams and Wilkins; 2006.\r\n[3] ACE Personal Trainers Manual, Bryant CX and Green DJ, Editors.\r\nAmerican council on exercise at website:\r\nhttp:\/\/www.acefitness.org\/acefit\/expert-insight-article\/3\/112\/what-arethe-\r\nguidelines-for-percentage-of\/\r\n[4] WHO, Global database on Body Mass Index, at website: apps.who\/bmi.\r\n[5] Garrow, J. S. & Webster, J. Quetelet's Index (W\/H2) as a measure of\r\nfatness, 1985. International Journal of Obesity 9, 147-53.\r\n[6] Heymsfield, S. B., McManus, C , Smith, J., Stevens, V. & Nixon, D. W.\r\nAnthropometric measurement of muscle mass: revised equations for\r\ncalculating bone-free arm muscle area, 1982. American Journal of\r\nClinical Nutrition 36, 680-90.\r\n[7] (http:\/\/en.wikipedia.org\/wiki\/Olympic_weightlifting)\r\n[8] Nov\u00e1k, Vil\u00e9m; Perfilieva, Irina; Mo\u010dko\u0159, Ji\u0159\u00ed Mathematical principles of\r\nfuzzy logic. The Kluwer International Series in Engineering and\r\nComputer Science, 1999, Kluwer Academic Publishers.\r\n[9] Welon, Z., Jury nee, R. & Sliwa, W. Ci\u00a7zar nalezny meiczyzn. 1988,\r\nMatieraly i Prace Antropologiczne 109, 53-71\r\n[10] Malina, R.M. Anthropometry. In: Physiological Assessment of Human\r\nFitness. P.J. Maud and C. Foster, Eds. Champaign, IL: Human Kinetics,\r\n1995.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 92, 2014"}