Machine learning methods for the automatic evaluation of exercises on sensor-equipped weight training machines

Novatchkov, Hristo; Baca, Arnold

doi:10.1016/j.proeng.2012.04.096

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…The sports mining domain has emerged with different application areas, in which ML techniques are applied. These application areas that use ML on sports training can be listed as analyzing the performances in sports [5], rapid feedback systems [6], automatic evaluation of exercises [7], performance evaluation [8], exercise repetition detection [9], intelligent systems for personalized sport training [10] and planning the sports training sessions [11], which are not within the scope of our work. The world of sports now also embraced the information technologies in many phases like live analysis, statistics, or player performance analysis [12].…”

Section: Related Workmentioning

confidence: 99%

“…Lineups given in the table belong to 2019-2020 season for Altınordu U14 team because the U13 team of 2018-2019 season should be evaluated with the next season's performances after they were trained with Hit/it. The lineups data can be accessed from Turkish Football Federation web site for all teams throughout seasons 7 . The lineup data of each match is compared with the lineups generated by all seven ML algorithms.…”

Section: The Evaluation Of Team Formationmentioning

confidence: 99%

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A case study on player selection and team formation in football with machine learning

Abidin¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for the U13 team of Altınordu Football Academy and comparing the results with that of the coach's lineup and lineups of 20 matches played in 2019-2020 season. Second is combining the data obtained from the trainings of the players and coach evaluations of the players and feeding the machine to make more accurate predictions. The data from the trainings is gathered with Hit/it Assistant and the coach evaluations of the players are stated by the golden standard according to eighteen criteria stated in the literature. Synthetically generated data is also used in the final dataset to obtain more accurate classification results. Another remarkable aspect of the study is that no match data is used to form the team to be proposed for the next match, instead real match data is only used for evaluation. The results show that machine learning algorithms can be used for player selection and team formation process because random forest algorithm, which is executed on WEKA environment, can make player selections with 93.93% reliability and the lineup suggestions of these algorithms are 97.16% similar to coach's ideal team and also the best performing algorithm has an average performance of 89.36% for team formation when compared with the match lineups of 2019-2020 football season.

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Section: Related Workmentioning

confidence: 99%

Section: The Evaluation Of Team Formationmentioning

confidence: 99%

A case study on player selection and team formation in football with machine learning

Abidin¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…Analyzing of performances in sports [40] Rapid feedback systems [31] Adaptive systems in sport [29] Modeling of training loads [51] Automatic physical effort plan generation [36] Sport training modelling [42] Recruitment process for sport swimming [50] Complex systems in sport [34] Automatic evaluation of exercises [43] Training optimization [61] Sport training support [35,37] Method and system of delivering an interactive and dynamic multi-sport training program [59] Performance evaluation [46] [41] seconds' interval, named track-points but are too complex to be analyzed by coaches manually. Therefore, first CI algorithms for mining such data have emerged that can plan and predict the number of sports' training sessions, detect the phenomenon of over-training, and even advise a nutrition during endurance competitions.…”

Section: Application Referencementioning

confidence: 99%

Computational intelligence in sports: Challenges and opportunities within a new research domain

Ljubič

Suganthan

Perc

et al. 2015

Applied Mathematics and Computation

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a b s t r a c tComputational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain.

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“…The present idea and work originated from previous studies done in the area of strength training [22,23]. In particular, research focused on the implementation of artificial intelligence (AI) techniques such as machine learning algorithms on the basis of neural networks (NNs) for the automated classification of sensor information gathered from weight training equipment.…”

Section: Proposed Work 31 Backgroundmentioning

confidence: 99%

Fuzzy Logic in Sports: A Review and an Illustrative Case Study in the Field of Strength Training

Novatchkov¹,

Baca²

2013

IJCA

Self Cite

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As a special form of probabilistic reasoning, the fuzzy logic concept allows the effective realization of approximate, vague, uncertain, dynamic, continuous and, at the same time, more realistic conditions, which are closer to the actual physical world and human thinking. This many-valued idea involves the definition of fuzzy sets and rules as well membership functions. These techniques allow the mapping of classes of objects not only -according to the binary logicto false (0) and true (1) but also to intermediate values in between. Based on this theorem, the particular purpose of this paper was to propose a fuzzy logic approach for the evaluation of strength training exercises. The motivation for the present study arose from previous research done in the area of artificial intelligence (AI) in sports, the effective number of multidisciplinary solutions integrating fuzzy logic methodologies and the lack of applications in the fields of sport and especially strength training. The conception takes into account gathered data from sensor-equipped machines as well as recommended suggestions and criteria regarding a proper execution. The final aim is to integrate the designed procedures into a computer-based coaching framework, returning automated feedback on the performed technique.

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Machine learning methods for the automatic evaluation of exercises on sensor-equipped weight training machines

Cited by 14 publications

References 8 publications

A case study on player selection and team formation in football with machine learning

A case study on player selection and team formation in football with machine learning

Computational intelligence in sports: Challenges and opportunities within a new research domain

Fuzzy Logic in Sports: A Review and an Illustrative Case Study in the Field of Strength Training

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