ABSTRACT
The variations in muscular contraction between endurance and power athletes have usually been evaluated from lower limb muscles. The aim of this study is to integrate the application of machine learning in automatically classifying the muscle performance recorded from upper limb muscle. Muscle contraction of bicep brachii was recorded based on the surface electromyography (sEMG) analysis. The evaluation of muscle performance consists of three main processing parts, i.e., pre-processing, feature extraction, and classification. EMG features were extracted from three types of domains: time domain (TD), frequency domain (FD), and time-frequency domain (TFD). For classification purposes, a Support Vector Machine (SVM) classifier was used, and the classification performance was analysed based on the classification accuracy. The best classification performance was observed from the feature set selected from sequential backward selection (SBS). This finding shows that it is possible to differentiate muscle performance from non-specifically trained muscle, which might be further related to the intrinsic properties of different groups of athletes.
- Flück, M., Kramer, M., Fitze, D. P., Kasper, S., Franchi, M. V. and Valdivieso, P. Cellular Aspects of Muscle Specialization Demonstrate Genotype – Phenotype Interaction Effects in Athletes. Frontiers in Physiology, 10, 526 (2019-May-08 2019).Google ScholarCross Ref
- Costill, D., Daniels, J., Evans, W., J. Fink, W., Krahenbuhl, G. and Saltin, B. Skeletal enzymes and fibre composition in male and female track athletes, 1976.Google Scholar
- Weyerstraß, J., Stewart, K., Wesselius, A. and Zeegers, M. Nine genetic polymorphisms associated with power athlete status – A Meta-Analysis. Journal of Science and Medicine in Sport, 21, 2 (2018/02/01/ 2018), 213-220.Google ScholarCross Ref
- Zawadowska, B., Majerczak, J., Semik, D., Karasiński, J., Kolodziejski, L., Kilarski, W., Duda, K. and Zoladz, J. Characteristics of myosin profile in human vastus lateralis muscle in relation to training background. Folia histochemica et cytobiologica / Polish Academy of Sciences, Polish Histochemical and Cytochemical Society, 42 (02/01 2004), 181-190.Google Scholar
- Klaver-Król, E. G., Henriquez, N. R., Oosterloo, S. J., Klaver, P., Kuipers, H. and Zwarts, M. J. Distribution of motor unit potential velocities in the biceps brachii muscle of sprinters and endurance athletes during short static contractions at low force levels. Journal of Electromyography and Kinesiology, 20, 6 (2010/12/01/ 2010), 1107-1114.Google Scholar
- Trninić, V., Trninić, M. and Čavala, M. Influences of Genetic and Environmental Factors on the Development of Personality, Performance and Sports Achievement. Acta kinesiologica, 12, 1 (2018), 55-61.Google Scholar
- Baker, J., Young, B. W. and Mann, D. Advances in athlete development: understanding conditions of and constraints on optimal practice. Current Opinion in Psychology, 16 (2017/08/01/ 2017), 24-27.Google ScholarCross Ref
- Gawda, P., Ginszt, M., Ginszt, A., Pawlak, H. and Majcher, P. Differences in myoelectric manifestations of fatigue during isometric muscle actions. Annals of agricultural and environmental medicine: AAEM, 25, 2 (2018), 296-299.Google Scholar
- Herda, T. J., Siedlik, J. A., Trevino, M. A., Cooper, M. A. and Weir, J. P. Motor unit control strategies of endurance- versus resistance-trained individuals. Muscle & Nerve, 52, 5 (2015), 832-843.Google ScholarCross Ref
- Huber, C., Göpfert, B., Kugler, P. F.-X. and Von Tscharner, V. The effect of sprint and endurance training on electromyogram signal analysis by wavelets. The Journal of Strength & Conditioning Research, 24, 6 (2010), 1527-1536.Google ScholarCross Ref
- Rainoldi, A., Gazzoni, M. and Melchiorri, G. Differences in myoelectric manifestations of fatigue in sprinters and long distance runners. Physiological Measurement, 29, 3 (2008), 331.Google ScholarCross Ref
- Dhindsa, I., Agarwal, R. and Ryait, H. Performance evaluation of various classifiers for predicting knee angle from electromyography signals. Expert Systems, 36 (06/01 2019), e12381.Google Scholar
- Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F. and Laurillau, Y. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications, 40, 12 (2013/09/15/ 2013), 4832-4840.Google ScholarDigital Library
- Waris, A. and Kamavuako, E. N. Effect of threshold values on the combination of EMG time domain features: Surface versus intramuscular EMG. Biomedical Signal Processing and Control, 45 (2018/08/01/ 2018), 267-273.Google ScholarCross Ref
- Yang, C., Xi, X., Chen, S., Miran, S. M., Hua, X. and Luo, Z. SEMG-based multifeatures and predictive model for knee-joint-angle estimation. AIP Advances, 9, 9 (2019), 095042.Google ScholarCross Ref
- Phinyomark, A., R, N. K. and Scheme, E. Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors (Basel), 18, 5 (May 18 2018).Google ScholarCross Ref
- Abbaspour, S., Lindén, M., Gholamhosseini, H., Naber, A. and Ortiz-Catalan, M. Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Medical & Biological Engineering & Computing, 58, 1 (2020/01/01 2020), 83-100.Google ScholarCross Ref
- Bohari, Z. H., Jali, M., Baharom, F., Mohd Nasir, M. N. i., Jaafar, H. I. and Wan Daud, W. M. B. EMG signal statistical features extraction combination performance benchmark using unsupervised neural network for arm rehab device. International Journal of Applied Engineering Research, 9 (01/01 2014), 12393-12402.Google Scholar
- Triwiyanto, Wahyunggoro, O., Nugroho, H. A. and Herianto Effect of window length on performance of the elbow-joint angle prediction based on electromyography. Journal of Physics: Conference Series, 853 (2017/05 2017), 012014.Google ScholarCross Ref
- Ashraf, H., Waris, A., Jamil, M., Gilani, S. O., Niazi, I. K., Kamavuako, E. N. and Gilani, S. H. N. Determination of Optimum Segmentation Schemes for Pattern Recognition-Based Myoelectric Control: A Multi-Dataset Investigation. IEEE Access, 8 (2020), 90862-90877.Google Scholar
- Hassan, H. F., Abou-Loukh, S. J. and Ibraheem, I. K. Teleoperated robotic arm movement using electromyography signal with wearable Myo armband. Journal of King Saud University - Engineering Sciences, 32, 6 (2020/09/01/ 2019), 378-387.Google ScholarCross Ref
- Wahid, M. F., Tafreshi, R., Al-Sowaidi, M. and Langari, R. Subject-independent hand gesture recognition using normalization and machine learning algorithms. Journal of Computational Science, 27 (2018/07/01/ 2018), 69-76.Google ScholarCross Ref
- Islam, A. and Alam, M. Classification of Electromyography Signals Using Support Vector Machine (01/01 2017).Google Scholar
Index Terms
- Machine Learning Classification of Non-Specifically Trained Muscle between Endurance and Power Athletes
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