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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
