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Open AccessArticle

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

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Ermenek Vocational School, Karamanoğlu Mehmetbey University, 70400 Karaman, Turkey
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Vocational School of Technical Sciences, Konya Technical University, 42003 Konya, Turkey
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 611; https://doi.org/10.3390/app10020611
Received: 30 November 2019 / Revised: 30 December 2019 / Accepted: 9 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Computational Intelligence and Data Mining in Sports)
In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training. View Full-Text
Keywords: expert system; movement modelization; training accuracy; performance enhancement; injury prevention; sport; human–machine interaction expert system; movement modelization; training accuracy; performance enhancement; injury prevention; sport; human–machine interaction
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Örücü, S.; Selek, M. Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support. Appl. Sci. 2020, 10, 611.

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