APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT

Robert KARPIŃSKI

r.karpinski@pollub.pl
* Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin (Poland)

Anna MACHROWSKA


Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin (Poland)

Marcin MACIEJEWSKI


 Institute of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin (Poland)

Abstract

The paper presents results of preliminary research of analysis of signals recorded for open and closed kinematic chain in one volunteer with chondromalacia in both knees. The preliminary research was conducted in order to establish the accuracy of the proposed method and will be used for formulating further research areas. The aim of the paper is to show how FFT, recurrence plots and recurrence quantification analysis (RQA) can help in bioacoustic signals analysis.


Keywords:

knee joint, kinetic chain, signals, articular cartilage

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

Bączkowicz, D., Kręcisz, K., & Borysiuk, Z. (2019). Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography. BMC Musculoskeletal Disorders, 20(1). https://doi.org/10.1186/s12891-019-2429-z
DOI: https://doi.org/10.1186/s12891-019-2429-z   Google Scholar

Bączkowicz, D., & Majorczyk, E. (2014). Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders. BMC Musculoskeletal Disorders, 15, 426. https://doi.org/10.1186/1471-2474-15-426
DOI: https://doi.org/10.1186/1471-2474-15-426   Google Scholar

Brinckmann, P., Hoefert, H., & Jongen, H. T. (1981). Sex differences in the skeletal geometry of the human pelvis and hip joint. Journal of Biomechanics, 14(6), 427–430. https://doi.org/10.1016/0021-9290(81)90060-9
DOI: https://doi.org/10.1016/0021-9290(81)90060-9   Google Scholar

Chen, Y., & Yang, H. (2012). Multiscale recurrence analysis of long-term nonlinear and nonstationary time series. Chaos, Solitons & Fractals, 45(7), 978–987. https://doi.org/10.1016/j.chaos.2012.03.013
DOI: https://doi.org/10.1016/j.chaos.2012.03.013   Google Scholar

Choi, D., Ahn, S., Ryu, J., Nagao, M., & Kim, Y. (2018). Knee Acoustic Emission Characteristics of the Healthy and the Patients with Osteoarthritis Using Piezoelectric Sensor. Sensors and Materials, 30(8), 1629. https://doi.org/10.18494/SAM.2018.1877
DOI: https://doi.org/10.18494/SAM.2018.1877   Google Scholar

Gilsanz, V., Boechat, M. I., Gilsanz, R., Loro, M. L., Roe, T. F., & Goodman, W. G. (1994). Gender differences in vertebral sizes in adults: biomechanical implications. Radiology, 190(3), 678–682. https://doi.org/10.1148/radiology.190.3.8115610
DOI: https://doi.org/10.1148/radiology.190.3.8115610   Google Scholar

Goodacre, J., Schlueter, D. K., Shark, L.-K., Spain, L., Platt, N., Platt, N., Mercer, J., Waterton, J. C., Bowes, M., Dixon, M., & Huddleston, J. (2018). Identifying Novel Acoustic Emission Biomarkers for Use in Knee Osteoarthritis Clinical Trials. Rheumatology, 57(suppl_3), key075.321. https://doi.org/10.1093/rheumatology/key075.321
DOI: https://doi.org/10.1093/rheumatology/key075.321   Google Scholar

Karpiński, R., Jaworski, Ł., Jonak, J., & Krakowski, P. (2019). Stress distribution in the knee joint in relation to tibiofemoral angle using the finite element method. MATEC Web of Conferences, 252, 07007. doi:10.1051/matecconf/201925207007
DOI: https://doi.org/10.1051/matecconf/201925207007   Google Scholar

Kim, K. S., Seo, J. H., Kang, J. U., & Song, C. G. (2009). An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis. Computer Methods and Programs in Biomedicine, 94(2), 198–206. https://doi.org/10.1016/j.cmpb.2008.12.012
DOI: https://doi.org/10.1016/j.cmpb.2008.12.012   Google Scholar

Krakowski, P., Gerkowicz, A., Pietrzak, A., Krasowska, D., Jurkiewicz, A., Gorzelak, M., & Schwartz, R. A. (2018). Psoriatic arthritis – new perspectives. Archives of Medical Science. https://doi.org/10.5114/aoms.2018.77725
DOI: https://doi.org/10.5114/aoms.2018.77725   Google Scholar

Kręcisz, K., & Bączkowicz, D. (2018). Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals. Computer Methods and Programs in Biomedicine, 154, 37–44. https://doi.org/10.1016/j.cmpb.2017.10.027
DOI: https://doi.org/10.1016/j.cmpb.2017.10.027   Google Scholar

Litak, G., Gajewski, J., Syta, A., & Jonak, J. (2008). Quantitative estimation of the tool wear effects in a ripping head by recurrence plots. Journal of Theoretical and Applied Mechanics, 46(3), 521–530.
  Google Scholar

Litak, G., Syta, A., Gajewski, J., & Jonak, J. (2010). Detecting and identifying non-stationary courses in the ripping head power consumption by recurrence plots. Meccanica, 45(4), 603–608. https://doi.org/10.1007/s11012-009-9265-4
DOI: https://doi.org/10.1007/s11012-009-9265-4   Google Scholar

Litak, G., Syta, A., & Rusinek, R. (2011). Dynamical changes during composite milling: recurrence and multiscale entropy analysis. The International Journal of Advanced Manufacturing Technology, 56(5), 445–453. https://doi.org/10.1007/s00170-011-3195-8
DOI: https://doi.org/10.1007/s00170-011-3195-8   Google Scholar

Maciejewski, M., Dzierżak, R., Surtel, W., & Saran, T. (2016). Human ECG indicators for fast screening and evaluation. In R. S. Romaniuk (Ed.) (1003131). Presented at the Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, Wilga, Poland. https://doi.org/10.1117/12.2249149
DOI: https://doi.org/10.1117/12.2249149   Google Scholar

Maciejewski, M., Surtel, W., & Dzida, G. (2015). Human ECG signal parameters estimation during controlled physical activity. In R. S. Romaniuk (Ed.) (96621P). Presented at the XXXVI Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, Wilga, Poland. https://doi.org/10.1117/12.2205811
DOI: https://doi.org/10.1117/12.2205811   Google Scholar

Maciejewski, M., Surtel, W., Wójcik, W., Masiak, J., Dzida, G., & Horoch, A. (2014). Telemedical systems for home monitoring of patients with chronic conditions in rural environment. Annals of Agricultural and Environmental Medicine, 21(1), 167–173.
  Google Scholar

Marras, W. S., Jorgensen, M. J., Granata, K. P., & Wiand, B. (2001). Female and male trunk geometry: size and prediction of the spine loading trunk muscles derived from MRI.
  Google Scholar

Clinical Biomechanics, 16(1), 38–46. https://doi.org/10.1016/S0268-0033(00)00046-2
DOI: https://doi.org/10.1016/S0268-0033(00)00046-2   Google Scholar

Marwan, N., Carmenromano, M., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329. https://doi.org/10.1016/j.physrep.2006.11.001
DOI: https://doi.org/10.1016/j.physrep.2006.11.001   Google Scholar

Rangayyan, R. M., Oloumi, F., Wu, Y., & Cai, S. (2013). Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomedical Signal Processing and Control, 8(1), 23–29. https://doi.org/10.1016/j.bspc.2012.05.004
DOI: https://doi.org/10.1016/j.bspc.2012.05.004   Google Scholar

Shannon, C. E. (1948). A Mathematical Theory of Communication. Reprinted with corrections from The Bell System Technical Journal, 27, 379–423, 623–656.
DOI: https://doi.org/10.1002/j.1538-7305.1948.tb00917.x   Google Scholar

Shark, L.-K., Chen, H., & Goodacre, J. (2010). Knee Acoustic Emission: A Clue to Joint Ageing and Failure. Rheumatology, 49, I79–I79.
  Google Scholar

Shark, L.-K., Chen, H., & Goodacre, J. (2011). Knee acoustic emission: A potential biomarker for quantitative assessment of joint ageing and degeneration. Medical Engineering & Physics, 33(5), 534–545. https://doi.org/10.1016/j.medengphy.2010.12.009
DOI: https://doi.org/10.1016/j.medengphy.2010.12.009   Google Scholar

Syta, A., Jonak, J., Jedliński, Ł., & Litak, G. (2012). Failure Diagnosis of a Gear Box by Recurrences. Journal of Vibration and Acoustics, 134(4), 041006. https://doi.org/10.1115/1.4005846
DOI: https://doi.org/10.1115/1.4005846   Google Scholar

Takens, F. (1981). Detecting strange attractors in turbulence. In D. Rand & L.-S. Young (Eds.), Dynamical Systems and Turbulence, Warwick 1980 (898, pp. 366–381). Berlin, Heidelberg: Springer. https://doi.org/10.1007/BFb0091924
DOI: https://doi.org/10.1007/BFb0091924   Google Scholar

Tool box of recurrence plot and recurrence quantification analysis – File Exchange – MATLAB Central. (2019, March 13). Retrieved from https://www.mathworks.com/matlabcentral/fileexchange/58246-tool-box-of-recurrence-plot-and-recurrence-quantification-analysis
  Google Scholar

Wiens, A. D., Prahalad, S., & Inan, O. T. (2016). VibroCV: A computer vision-based vibroarthrography platform with possible application to Juvenile idiopathic arthritis. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4431–4434). IEEE. https://doi.org/10.1109/EMBC.2016.7591710
DOI: https://doi.org/10.1109/EMBC.2016.7591710   Google Scholar

Wu, Y., Chen, P., Luo, X., Huang, H., Liao, L., Yao, Y., Wu, M., & Rangayyan, R. M. (2016). Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Computer Methods and Programs in Biomedicine, 130, 1–12. https://doi.org/10.1016/j.cmpb.2016.03.021
DOI: https://doi.org/10.1016/j.cmpb.2016.03.021   Google Scholar

Yang, H. (2011). Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals. IEEE Transactions on Biomedical Engineering, 58(2), 339–347. https://doi.org/10.1109/TBME.2010.2063704
DOI: https://doi.org/10.1109/TBME.2010.2063704   Google Scholar

Zubrzycki, J., Karpiński, R., & Górniak, B. (2016). Computer aided design and structural analysis of the endoprosthesis of the knee joint. Applied Computer Science, 12(2), 84–95.
  Google Scholar

Download


Published
2019-03-30

Cited by

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

Authors

Robert KARPIŃSKI 
r.karpinski@pollub.pl
* Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin Poland

Authors

Anna MACHROWSKA 

Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin Poland

Authors

Marcin MACIEJEWSKI 

 Institute of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin Poland

Statistics

Abstract views: 205
PDF downloads: 37


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.


Most read articles by the same author(s)

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.