Artificial Intelligence in Sports on the Example of Weight Training.
The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training...
| Publicado en: | Journal of Sports Science & Medicine Vol. 12; no. 1; pp. 27 - 38 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Hakan Gur, Journal of Sports Science & Medicine
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=91530270&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 91530270 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13032968 FYN jtl: Journal of Sports Science & Medicine issn: 13032968 maglogo: N pubinfo: dt: 2013 vid: 12 iid: 1 pid: 26030 pub: Hakan Gur, Journal of Sports Science & Medicine artinfo: ui: 91530270 91530270 104148722 91530270 ppf: 27 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence in Sports on the Example of Weight Training. aug: au: Novatchkov, Hristo Baca, Arnold affil: University of Vienna, Centre for Sport Science and University Sports, Auf der Schmelz 6A, 1150 Vienna, Austria sug: subj: Muscle Strengthening Artificial Intelligence Physical Performance Evaluation Austria Biophysical Instruments Feedback Leg Physiology Human Male Female Body Weights and Measures Descriptive Statistics Young Adult Learning Evaluation Exercise Physiology Male Female ab: The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10- 12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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