Sportif Faaliyetlerde Kullanılmak Üzere Bir Uzman Sistem Tasarımı

Öz

Vücut geliştirme gibi izotonik hareketlerin yapıldığı sporlarda ve rehabilitasyon süreçlerinde, antrenmanların eşzamanlı izlenmesi, yanlış yapılan hareketlerin anında düzeltilebilmesi antrenman yeterliliği ve veriminin belirlenebilmesi bireylerin yaralanma risklerinden uzak ve sağlıklı antrenman yapabilmeleri açısından hayati önem taşımaktadır. Çalışmamızda, bu amaca yönelik hareket modeline dayalı kural tabanlı bir Uzman Sistem (ES) tasarlanmıştır. Tasarlanan sistem, antrenör üzerinden elde edilen LR (Lateral Raise) antrenmanı verileri aracılığıyla, DTW metodu ile karşılaştırmalı olarak test edilmiştir. Test sonuçlarında, tasarladığımız ES'in aldığı kararların DTW metodundan elde edilen kararlar ile karşılaştırmasında %64 oranında doğrulukla, daha yüksek oranda doğruluğa sahip olduğu görülmüştür.

Anahtar Kelimeler

• Antrenman Doğruluğu
• DTW
• Hareket Modeli
• İnsan-Makine Etkileşimi
• Uzman Sistem

An Expert System Design for Use in Sports Activities

Abstract

In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital to monitor the training simultaneously, correct the wrong movements, and determine the training adequacy and efficiency, so that individuals can do healthy training without risk of injury. For this purpose, in our study, a rule-based Expert System (ES) based on the motion model was designed. The designed system was tested comparatively with the DTW method via the LR (Lateral Raise) training data obtained from the coach. In the test results, it was seen that the decisions we made by the ES had a 64% higher accuracy in comparison with the decisions obtained from the DTW method.

Keywords

• DTW
• Expert System
• Human-Machine Interaction
• Motion Model
• Training Accuracy

Kaynakça

1. Adelsberger, R., & Tröster, G. (2013, 6-9 May 2013). Experts lift differently: Classification of weight-lifting athletes. Paper presented at the 2013 IEEE International Conference on Body Sensor Networks.
2. Andersen, L. L., Vinstrup, J., Jakobsen, M. D., & Sundstrup, E. (2017). Validity and reliability of elastic resistance bands for measuring shoulder muscle strength. Scandinavian Journal of Medicine & Science in Sports, 27(8), 887-894. doi:10.1111/sms.12695
3. Appelbaum, L. G., & Erickson, G. (2018). Sports vision training: A review of the state-of-the-art in digital training techniques. International Review of Sport and Exercise Psychology, 11(1), 160-189. doi:10.1080/1750984X.2016.1266376
4. Arandjelović, O. (2013). Does cheating pay: the role of externally supplied momentum on muscular force in resistance exercise. European Journal of Applied Physiology, 113(1), 135-145. doi:10.1007/s00421-012-2420-y
5. Bailey, R. (2017). Sport, physical activity and educational achievement – towards an explanatory model. Sport in Society, 20(7), 768-788. doi:10.1080/17430437.2016.1207756
6. Başçiftçi, F., & Avuçlu, E. (2018). An expert system design to diagnose cancer by using a new method reduced rule base. Computer Methods and Programs in Biomedicine, 157, 113-120. doi:https://doi.org/10.1016/j.cmpb.2018.01.020
7. Brill, M., Fluschnik, T., Froese, V., Jain, B., Niedermeier, R., & Schultz, D. (2019). Exact mean computation in dynamic time warping spaces. Data Mining and Knowledge Discovery, 33(1), 252-291. doi:10.1007/s10618-018-0604-8
8. Clark, R. A., Mentiplay, B. F., Hough, E., & Pua, Y. H. (2019). Three-dimensional cameras and skeleton pose tracking for physical function assessment: A review of uses, validity, current developments and Kinect alternatives. Gait & Posture, 68, 193-200. doi:https://doi.org/10.1016/j.gaitpost.2018.11.029

9. Çetinkaya, E., Tanır, H., Atay, E., Bulut, Ç., & Engin, H. (2017). Investigation of musculoskeletal system injuries in athletes doing bodybuilding and fitness sports<p>Vücut geliştirme ve fitness sporu yapanlarda, kas, iskelet sistemi sakatlıklarının belirlenmesi. Journal of Human Sciences, 14(4), 4023-4031.
10. Ericsson, K. A. (2017). Expertise and individual differences: the search for the structure and acquisition of experts’ superior performance. Wiley Interdisciplinary Reviews: Cognitive Science, 8(1-2), e1382. doi:10.1002/wcs.1382
11. Harris, D. J., Wilson, M. R., & Vine, S. J. (2018). A Systematic Review of Commercial Cognitive Training Devices: Implications for Use in Sport. Front. Psychol., 9(709). doi:10.3389/fpsyg.2018.00709
12. Jeong, H., Yamada, K., Kido, M., Okada, S., Nomura, T., & Ohno, Y. (2016). Analysis of Difference in Center-of-Pressure Positions Between Experts and Novices During Asymmetric Lifting. IEEE Journal of Translational Engineering in Health and Medicine, 4, 1-11. doi:10.1109/JTEHM.2016.2599185
13. Keogh, J. W. L., Aickin, S. E., & Oldham, A. R. H. (2010). Can Common Measures of Core Stability Distinguish Performance in a Shoulder Pressing Task Under Stable and Unstable Conditions? The Journal of Strength & Conditioning Research, 24(2), 422-429. doi:10.1519/JSC.0b013e3181c7c6b9
14. Konstantinidis, E. I., Bamparopoulos, G., & Bamidis, P. D. (2017). Moving Real Exergaming Engines on the Web: The webFitForAll Case Study in an Active and Healthy Ageing Living Lab Environment. IEEE Journal of Biomedical and Health Informatics, 21(3), 859-866. doi:10.1109/JBHI.2016.2559787
15. Lavallee, M. E., & Balam, T. (2010). An Overview of Strength Training Injuries: Acute and Chronic. Current Sports Medicine Reports, 9(5), 307-313. doi:10.1249/JSR.0b013e3181f3ed6d
16. Mateo, F., Soria-Olivas, E., Carrasco, J. J., Bonanad, S., Querol, F., & Pérez-Alenda, S. (2018). HemoKinect: A Microsoft Kinect V2 Based Exergaming Software to Supervise Physical Exercise of Patients with Hemophilia. Sensors, 18(8), 2439.
17. Morel, M., Achard, C., Kulpa, R., & Dubuisson, S. (2018). Time-series averaging using constrained dynamic time warping with tolerance. Pattern Recognition, 74, 77-89. doi:https://doi.org/10.1016/j.patcog.2017.08.015
18. Naeemabadi, M., Dinesen, B., Andersen, O. K., & Hansen, J. (2019). Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm. IEEE Sensors Journal, 19(1), 171-179. doi:10.1109/JSEN.2018.2876624
19. Ojeda-Castelo, J. J., Piedra-Fernandez, J. A., Iribarne, L., & Bernal-Bravo, C. (2018). KiNEEt: application for learning and rehabilitation in special educational needs. Multimedia Tools and Applications, 77(18), 24013-24039. doi:10.1007/s11042-018-5678-1
20. Reeves, R. K., Laskowski, E. R., & Smith, J. (1998). Weight Training Injuries. The Physician and Sportsmedicine, 26(3), 54-73. doi:10.1080/00913847.1998.11440348
21. Rybarczyk, Y., Kleine Deters, J., Cointe, C., & Esparza, D. (2018). Smart Web-Based Platform to Support Physical Rehabilitation. Sensors, 18(5), 1344.
22. Selek, M., Başçiftçi, F., & Örücü, S. (2017). Designing medical expert system based on logical reduced rule for basic malaria diagnosis from malaria signs and symptoms. World Journal of Engineering, 14(3), 227-230. doi:10.1108/WJE-10-2016-0112
23. Su, C.-H. (2016). Developing and evaluating effectiveness of 3D game-based rehabilitation system for Total Knee Replacement Rehabilitation patients. Multimedia Tools and Applications, 75(16), 10037-10057. doi:10.1007/s11042-015-2820-1
24. Taniguchi, Y. (1997). Lateral specificity in resistance training: the effect of bilateral and unilateral training. European Journal of Applied Physiology and Occupational Physiology, 75(2), 144-150. doi:10.1007/s004210050139
25. Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2018). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing, 21(1), 681-690. doi:10.1007/s10586-017-0977-2
26. von Rottkay, E., Nöth, U., Zinner, J., & Reichert, J. C. (2018). Schulterverletzungen im CrossFit und verwandten Sportarten. Sports Orthopaedics and Traumatology, 34(2), 145-150. doi:https://doi.org/10.1016/j.orthtr.2017.12.007
27. Wagner, W. P. (2017). Trends in expert system development: A longitudinal content analysis of over thirty years of expert system case studies. Expert Systems with Applications, 76, 85-96. doi:https://doi.org/10.1016/j.eswa.2017.01.028
28. Yu, X., & Xiong, S. (2019). A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People. Sensors, 19(13), 2882.