Samo Rauter

Planning the sports training sessions with the bat algorithm

Planning proper sports training has always been a very challenging task for coaches. In line with this, they need to have almost two special abilities: firstly, to have a lot of earlier experiences with sports training and secondly, to know the capability of their athletes very well. New ways in planning sports training have emerged with development of pervasive and mobile technologies. Recently, a GPS receiver is one of the most useful parts of each standard sports watch that enables athletes to track the duration of their sports activities and analyze them later on digital computers using GPS viewers. Most sports watches are also capable of measuring an athletes heart rate during activities. Both measures represent reliable data sources that can be used for planning the sports trainings by coaches. In this paper, we introduce a novel intelligent planning method for sports training sessions, where the training plans are generated on digital computers using the bat algorithm according to reliable data obtained from sports watches. Real-world experiments showed promising results that encouraged us to proceed with this research also in the future.

How to Deal with Sports Activity Datasets for Data Mining and Analysis: Some Tips and Future Challenges

The aim of this paper is to briefly present the fundamentals of sports training to computational intelligence community and present a short description on recently released sport activity datasets for data mining and data analysis. Training plan for professional mountainbiker is presented in details. Moreover, tools for parsing sport activities files are discussed. Finally, tips and open challenges are exposed that refer to the data analysis and data mining of the proposed sports activity datasets.

Generating eating plans for athletes using the particle swarm optimization

This paper presents the automatic generation of optimal eating plans for athletes. The automatic generation of the eating plans is introduced as an optimization problem, where particle swarm optimization is taken as the problem solver. Inputs for the proposed particle swarm optimization algorithm are generated training plan and list of the potential meals, while the output of the algorithm represents a list of meals that should be consumed by the athletes. The first practical experiments showed that this solution is very promising.

Deep Analytics Based on Triathlon Athletes’ Blogs and News

Studying the lifestyle of various groups of athletes has been a very interesting research direction of many social sport scientists. Following the behavior of these athletes’ groups might reveal how they work, yet function in the real-world. Triathlon is basically depicted as one of the hardest sports in the world (especially long-distance triathlons). Hence, studying this group of people can have a very positive influence on designing new perspectives and theories about their lifestyle. Additionally, the discovered information also helps in designing modern systems for planning sport training sessions. In this paper, we apply deep analytic methods for discovering knowledge from triathlon athletes’ blogs and news posted on their websites. Practical results reveal that triathlon remains in the forefront of the athletes’ minds through the whole year.

Body Asymmetries in Young Male Road Cyclists

An anthropometric analysis was conducted on 64 competitive young male road cyclists of different age categories (U17; U19; & U23 years of age). The purpose of the study is to find asymmetries between the left and right side of upper and lo wer limbs with the NX-16 (TC) 3D body scanner, which includes measurement of left and right upper arm girth, elbow girth, forearm girth, wrist girth, thigh girth, knee girth, thigh length, calf girth, and shin length. Body composition was measured by the bioelectrical i mpedance machine InBody 720 (Biospace Ltd.). Results of body composition measurements of male road cyclists showed that U17 and U19 yout h road cyclist differed statistically in five (from 11) paired variables, and the U23 age group differed statistically in six (fr om 11) paired variables. All of the age groups differed statistically in elbow, forearm, and calf girth. The main finding of study was that a s the ge of a cyclist increases, there is a tendency to increase asymmetries between the left and right side of several body segments.