DIAGNOSTIC FACTORS FOR OPENED AND CLOSED KINEMATIC CHAIN OF VIBROARTHROGRAPHY SIGNALS
Anna MACHROWSKA
a.machrowska@pollub.plLublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Robert KARPIŃSKI
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Przemysław KRAKOWSKI
Orthopedic Department, Łęczna Hospital, Krasnystawska 52, 21-010 Łęczna (Poland)
Józef JONAK
* Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Abstract
The paper presents results of preliminary research of vibroarthrography signals recorded from one healthy volunteer. The tests were carried out for the open and closed kinematic chain in the range of motion 90° - 0° - 90°. Analysis included initial signal filtration using the EMD algorithm. The aim was to investigate the occurrence of differences in the values of selected energy and statistical parameters for the cases studied.
Keywords:
EMD, EEMD, knee joint, vibration, kinetic chainReferences
Adouni, M., & Shirazi-Adl, A. (2009). Knee joint biomechanics in closed-kinetic-chain exercises. Computer Methods in Biomechanics and Biomedical Engineering, 12(6), 661–670. https://doi.org/10.1080/10255840902828375
DOI: https://doi.org/10.1080/10255840902828375
Google Scholar
Alickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94–102. https://doi.org/10.1016/j.bspc.2017.07.022
DOI: https://doi.org/10.1016/j.bspc.2017.07.022
Google Scholar
Barcellona, M. G., & Morrissey, M. C. (2016). The effect of open kinetic chain knee extensor resistance training at different training loads on anterior knee laxity in the uninjured. Manual Therapy, 22, 1–8. https://doi.org/10.1016/j.math.2015.12.011
DOI: https://doi.org/10.1016/j.math.2015.12.011
Google Scholar
Charnley, J. (1955). Kinesiology of the human body under normal and pathological conditions. The Journal of Bone and Joint Surgery. British Volume, 37–B(4), 736–737. https://doi.org/10.1302/0301-620X.37B4.736
DOI: https://doi.org/10.1302/0301-620X.37B4.736
Google Scholar
Cohen, Z. A., Roglic, H., Grelsamer, R. P., Henry, J. H., Levine, W. N., Van Mow, C., & Ateshian, G. A. (2001). Patellofemoral Stresses during Open and Closed Kinetic Chain Exercises: An Analysis Using Computer Simulation. The American Journal of Sports Medicine, 29(4), 480–487. https://doi.org/10.1177/03635465010290041701
DOI: https://doi.org/10.1177/03635465010290041701
Google Scholar
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-Ch., Tung, Ch. Ch., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
DOI: https://doi.org/10.1098/rspa.1998.0193
Google Scholar
Jonak, J., Machrowska, A., Podgórski, J., & Bęc, J. (2016). Identification of the operating parameters for the mechanical systeIdentification system using EMD algorithm. MATEC Web of Conferences, 83, 05001. https://doi.org/10.1051/matecconf/20168305001
DOI: https://doi.org/10.1051/matecconf/20168305001
Google Scholar
Karpiński, R., Machrowska, A., & Maciejewski, M. (2019). Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint. Applied Computer Science, 15(1), 36–48. https://doi.org/10.23743/acs-2019-03
Google Scholar
Kolotkov, D. Y., Anfinogentov, S. A., & Nakariakov, V. M. (2016). Empirical mode decomposition analysis of random processes in the solar atmosphere. Astronomy & Astrophysics, 592, A153. https://doi.org/10.1051/0004-6361/201628306
DOI: https://doi.org/10.1051/0004-6361/201628306
Google Scholar
Luque-Seron, J. A., & Medina-Porqueres, I. (2016). Anterior Cruciate Ligament Strain In Vivo: A Systematic Review. Sports Health: A Multidisciplinary Approach, 8(5), 451–455. https://doi.org/10.1177/1941738116658006
DOI: https://doi.org/10.1177/1941738116658006
Google Scholar
Machrowska, A., & Jonak, J. (2018). xEMD procedures as a data – Assisted filtering method. AIP Conference Proceedings, 1922, 120007. https://doi.org/10.1063/1.5019122
DOI: https://doi.org/10.1063/1.5019122
Google Scholar
Maciejewski, M. (2015). Zastosowanie i ograniczenia technologii informacyjnych w diagnostyce medycznej. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 1, 66–72.
DOI: https://doi.org/10.5604/20830157.1148052
Google Scholar
Maciejewski, M., Surtel, W., & Dzida, G. (2015). Human ECG signal parameters estimation during controlled physical activity. In R. S. Romaniuk (Ed.), Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (96621P ). https://doi.org/10.1117/12.2205811
DOI: https://doi.org/10.1117/12.2205811
Google Scholar
Powers, C. M., Bolgla, L. A., Callaghan, M. J., Collins, N., & Sheehan, F. T. (2012). Patellofemoral Pain: Proximal, Distal, and Local Factors—2nd International Research Retreat, August 31–September 2, 2011, Ghent, Belgium. Journal of Orthopaedic & Sports Physical Therapy, 42(6), A1–A54. https://doi.org/10.2519/jospt.2012.0301
DOI: https://doi.org/10.2519/jospt.2012.0301
Google Scholar
Sanchis-Alfonso, V. (2014). Holistic approach to understanding anterior knee pain. Clinical implications. Knee Surgery, Sports Traumatology, Arthroscopy, 22(10), 2275–2285. https://doi.org/10.1007/s00167-014-3011-8
DOI: https://doi.org/10.1007/s00167-014-3011-8
Google Scholar
Sanchis-Alfonso, V., & Dye, S. F. (2017). How to Deal With Anterior Knee Pain in the Active Young Patient. Sports Health: A Multidisciplinary Approach, 9(4), 346–351. https://doi.org/10.1177/1941738116681269
DOI: https://doi.org/10.1177/1941738116681269
Google Scholar
Witvrouw, E., Danneels, L., van Tiggelen, D., Willems, T. M., & Cambier, D. (2004). Open versus Closed Kinetic Chain Exercises in Patellofemoral Pain: A 5-Year Prospective Randomized Study. The American Journal of Sports Medicine, 32(5), 1122–1130. https://doi.org/10.1177/0363546503262187
DOI: https://doi.org/10.1177/0363546503262187
Google Scholar
Yaslan, Y., & Bican, B. (2017). Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting. Measurement, 103, 52–61. https://doi.org/10.1016/j.measurement.2017.02.007
DOI: https://doi.org/10.1016/j.measurement.2017.02.007
Google Scholar
Authors
Anna MACHROWSKAa.machrowska@pollub.pl
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Robert KARPIŃSKILublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Przemysław KRAKOWSKIOrthopedic Department, Łęczna Hospital, Krasnystawska 52, 21-010 Łęczna Poland
Authors
Józef JONAK* Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
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