Planning Fitness Training Sessions Using the Bat Algorithm

Over fairly recent years the concept of an ar- tificial sport trainer has been proposed in literature. This concept is based on computational intelligence algorithms. In this paper, we try to extend the artificial sports trainer by planning fitness training sessions that are suitable for athletes, especially during idle seasons when no competi- tion takes place (e.g., winter). The bat algorithm was used for planning fitness training sessions and results showed promise for the proposed solution. Future directions for development are also outlined in the paper.

[1]  Iztok Fister,et al.  A hybrid bat algorithm , 2013, ArXiv.

[2]  Jeffrey B. Kreher,et al.  Overtraining Syndrome , 2012, Sports health.

[3]  Qidi Wu,et al.  Adaptive bat algorithm for coverage of wireless sensor network , 2015, Int. J. Wirel. Mob. Comput..

[4]  Ian Jones,et al.  The Great Suburban Everest: An ‘Insiders’ Perspective on Experiences at the 2007 Flora London Marathon , 2008 .

[5]  Dongsheng Zhao,et al.  Chaotic binary bat algorithm for analog test point selection , 2015, Analog Integrated Circuits and Signal Processing.

[6]  T. Raastad,et al.  Strength training improves 5‐min all‐out performance following 185 min of cycling , 2011, Scandinavian journal of medicine & science in sports.

[7]  Broderick Crawford,et al.  Online Control of Enumeration Strategies via Bat-Inspired Optimization , 2015, IWINAC.

[8]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.

[9]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[10]  R. Budgett,et al.  Fatigue and underperformance in athletes: the overtraining syndrome. , 1998, British journal of sports medicine.

[11]  J Keul,et al.  Overtraining in endurance athletes: a brief review. , 1993, Medicine and science in sports and exercise.

[12]  Li Cheng,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010 .

[13]  B. V. Manikandan,et al.  Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System , 2015, Appl. Soft Comput..

[14]  C Hausswirth,et al.  Variability in Energy Cost of Running at the End of a Triathlon and a Marathon , 1996, International journal of sports medicine.

[15]  Iztok Fister,et al.  Planning the sports training sessions with the bat algorithm , 2015, Neurocomputing.

[16]  Matjaz Perc,et al.  Computational intelligence in sports: Challenges and opportunities within a new research domain , 2015, Appl. Math. Comput..

[17]  Samo Rauter,et al.  DIFFERENCES IN TRAVEL BEHAVIORS OF SMALL AND LARGE CYCLING EVENTS PARTICIPANTS , 2013 .

[18]  Niklas Lehto,et al.  Effects of age on marathon finishing time among male amateur runners in Stockholm Marathon 1979–2014 , 2015, Journal of sport and health science.

[19]  Thomas Rosemann,et al.  Personal Best Time, Percent Body Fat, and Training Are Differently Associated With Race Time for Male and Female Ironman Triathletes , 2010, Research quarterly for exercise and sport.

[20]  R. McCarville,et al.  From a Fall in the Mall to a Run in the Sun: One Journey to Ironman Triathlon , 2007 .

[21]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[22]  Iztok Fister,et al.  A COMPREHENSIVE REVIEW OF BAT ALGORITHMS AND THEIR HYBRIDIZATION , 2013 .

[23]  Aboul Ella Hassanien,et al.  A Discrete Bat Algorithm for the Community Detection Problem , 2015, HAIS.

[24]  E. Simonsen,et al.  Increased rate of force development and neural drive of human skeletal muscle following resistance training. , 2002, Journal of applied physiology.

[25]  Simon Fong,et al.  Bat algorithm: Recent advances , 2014, 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI).

[26]  Mikael Flockhart,et al.  Adding strength to endurance training does not enhance aerobic capacity in cyclists , 2015, Scandinavian journal of medicine & science in sports.