Skip to main content
SpringerLink
Log in

Detecting the presence of anterior cruciate ligament deficiency based on a double pendulum model, intrinsic time-scale decomposition (ITD) and neural networks

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The anterior cruciate ligament (ACL) possesses the function of stabilizing the knee joint through limiting anterior tibial translation and controlling tibial rotation. Patients with unilateral ACL deficiency often demonstrate alterations of knee kinematics, kinetics and gait patterns in the deficient side in comparison to the unaffected contralateral side. This also leads to the early onset of osteoarthritis. In order to detect and monitor the progression of ACL deficiency over time, various classification approaches using spatiotemporal gait variables have been presented. In this study we propose a novel method for classifying gait patterns between ACL-deficient (ACLD) knee and unaffected contralateral ACL-intact (ACLI) knee based upon gait system dynamics, intrinsic time-scale decomposition (ITD) and neural networks. First, human leg is modeled as a double-pendulum to imitate and simplify the human walking. Since the lower extremities act as a kinetic chain during dynamic tasks, control of the hip joint will interact with knee motion. Related gait kinematic parameters including knee and hip joint angle and angular velocity are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first PRCs of knee and hip joint angle and angular velocity are extracted, which contain most of the kinematic signals’ vibration energy and are considered to be the predominant PRCs. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between ACLD and ACLI knees based on the difference of gait system dynamics between the two groups. Finally, experiments are carried out on forty-three patients to assess the effectiveness of the proposed method. By using the leave-one-out cross-validation style under normal and fast walking speed conditions, the correct classification rates are reported to be \(95.12\%\) and \(93.28\%\), respectively. In comparison to other state-of-the-art methods, the results demonstrate superior performance and the proposed method may serve as a potential assistant tool for the automatic detection of ACL deficiency in the clinical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ackermann M, Van den Bogert AJ (2010) Optimality principles for model-based prediction of human gait. J Biomech 43(6):1055–1060

    Google Scholar 

  • Almosnino S, Brandon SC, Day AG, Stevenson JM, Dvir Z, Bardana DD (2014) Principal component modeling of isokinetic moment curves for discriminating between the injured and healthy knees of unilateral ACL deficient patients. J Electromyogr Kinesiol 24(1):134–143

    Google Scholar 

  • An X, Jiang D, Chen J, Liu C (2012) Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing. J Vib Control 18(2):240–245

    Google Scholar 

  • Andersen RE, Arendt-Nielsen L, Madeleine P (2018) Knee joint vibroarthrography of asymptomatic subjects during loaded flexion-extension movements. Med Biol Eng Comput 56(12):2301–2312

    Google Scholar 

  • Atarod M, Frank CB, Shrive NG (2014) Kinematic and kinetic interactions during normal and ACL-deficient gait: a longitudinal in vivo study. Ann Biomed Eng 42(3):566–578

    Google Scholar 

  • Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24:1163–1177

    Google Scholar 

  • Azus A, Teng HL, Tufts L, Wu D, Ma CB, Souza RB, Li X (2018) Biomechanical factors associated with pain and symptoms following anterior cruciate ligament injury and reconstruction. PMR 10(1):56–63

    Google Scholar 

  • Bazargan-Lari Y, Eghtesad M, Khoogar AR, Mohammad-Zadeh A (2015) Adaptive neural network control of a human swing leg as a double-pendulum considering self-impact joint constraint. Trans Can Soc Mech Eng 39(2):201–219

    Google Scholar 

  • Berruto M, Uboldi F, Gala L, Marelli B, Albisetti W (2013) Is triaxial accelerometer reliable in the evaluation and grading of knee pivot shift phenomenon? Knee Surg Sports Traumatol Arthrosc 21(4):981–985

    Google Scholar 

  • Bosga J, Hullegie W, Cingel RV, Meulenbroek R (2019) Solution space: monitoring the dynamics of motor rehabilitation. Physiother Theory Pract 35(6):507–515

    Google Scholar 

  • Brown C, Bowser B, Simpson KJ (2012) Movement variability during single leg jump landings in individuals with and without chronic ankle instability. Clin Biomech 27(1):52–63

    Google Scholar 

  • Chen B, He Z, Chen X, Cao H, Cai G, Zi Y (2011) A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis. Meas Sci Technol 22(5):055704

    Google Scholar 

  • Chen HC, Wu CH, Wang CK, Lin CJ, Sun YN (2014) A Joint-constraint model-based system for reconstructing total knee motion. IEEE Trans Biomed Eng 61(1):171–181

    Google Scholar 

  • Christian J, Kröll J, Strutzenberger G, Alexander N, Ofner M, Schwameder H (2016) Computer aided analysis of gait patterns in patients with acute anterior cruciate ligament injury. Clin Biomech 33:55–60

    Google Scholar 

  • Chu K (1999) An introduction to sensitivity, specificity, predictive values and likelihood ratios. Emerg Med Australas 11(3):175–181

    Google Scholar 

  • Czaplicki A, Kuniszyk-Jozkowiak W, Jaszczuk J, Jarocka M, Walawski J (2017) Using the discrete wavelet transform in assessing the effectiveness of rehabilitation in patients after ACL reconstruction. Acta Bioeng Biomech 19(3):139–146

    Google Scholar 

  • Decker LM, Moraiti C, Stergiou N, Georgoulis AD (2011) New insights into anterior cruciate ligament deficiency and reconstruction through the assessment of knee kinematic variability in terms of nonlinear dynamics. Knee Surg Sports Traumatol Arthrosc 19(10):1620–1633

    Google Scholar 

  • Feng Z, Lin X, Zuo MJ (2016) Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis. Mech Syst Signal Process 72:223–240

    Google Scholar 

  • Frei MG, Osorio I (2007) Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals. Proc R Soc Lond A Math Phys Eng Sci 463(2078):321–342

    MathSciNet  MATH  Google Scholar 

  • Gritli H, Belghith S, Khraief N (2015) OGY-based control of chaos in semi-passive dynamic walking of a torso-driven biped robot. Nonlinear Dyn 79(2):1363–1384

    MATH  Google Scholar 

  • Hart HF, Collins NJ, Ackland DC, Cowan SM, Crossley KM (2015) Gait characteristics of people with lateral knee OA after ACL reconstruction. Med Sci Sports Exerc 47(11):2406–2415

    Google Scholar 

  • Heard BJ, Beveridge JE, Atarod M, O’Brien EJ, Rolian C, Frank CB, Shrive NG (2017) Analysis of change in gait in the ovine stifle: normal, injured, and anterior cruciate ligament reconstructed. BMC Musculoskel Disord 18(1):212

    Google Scholar 

  • Hebert-Losier K, Schelin L, Tengman E, Strong A, Hager CK (2018) Curve analyses reveal altered knee, hip, and trunk kinematics during drop-jumps long after anterior cruciate ligament rupture. Knee 25(2):226–239

    Google Scholar 

  • Heilmeier U, Amano K, Tanaka M, Schwaiger BJ, Huebner JL, Stabler TV, Li X (2017) Synovitis of knee joint fat pads is correlated with inflammatory synovial cytokine profile and may have a potential role in the development of posttraumatic OA following ACL injury. Osteoarthr Cartil 25:S41–S42

    Google Scholar 

  • Herman DC, Jones D, Harrison A, Moser M, Tillman S, Farmer K, Chmielewski TL (2017) Concussion may increase the risk of subsequent lower extremity musculoskeletal injury in collegiate athletes. Sports Med 47(5):1003–1010

    Google Scholar 

  • Huang B, Kunoth A (2013) An optimization based empirical mode decomposition scheme. J Comput Appl Math 240:174–183

    MathSciNet  MATH  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454(1971):903–995

    MathSciNet  MATH  Google Scholar 

  • Huang H, Keijsers N, Horemans H, Guo Q, Yu Y, Stam H, Ao Y (2017) Anterior cruciate ligament rupture is associated with abnormal and asymmetrical lower limb loading during walking. J Sci Med Sport 20(5):432–437

    Google Scholar 

  • Hurwitz DE, Ryals AB, Case JP, Block JA, Andriacchi TP (2002) The knee adduction moment during gait in subjects with knee osteoarthritis is more closely correlated with static alignment than radiographic disease severity, toe out angle and pain. J Orthop Res 20(1):101–107

    Google Scholar 

  • Iliopoulos E, Galanis N, Iosifidis M, Zafeiridis A, Papadopoulos P, Potoupnis M, Kirkos J (2017) Anterior cruciate ligament deficiency reduces walking economy in “copers” and “non-coper”. Knee Surg Sports Traumatol Arthrosc 25(5):1403–1411

    Google Scholar 

  • Jac Fredo AR, Josena TR, Palaniappan R, Mythili A (2017) Classification of normal and knee joint disorder vibroarthrographic signals using multifractals and support vector machine. Biomed Eng Appl Basis Commun 29(03):1750016

    Google Scholar 

  • Kaipust JP, Huisinga JM, Filipi M, Stergiou N (2012) Gait variability measures reveal differences between multiple sclerosis patients and healthy controls. Motor Control 16(2):229–244

    Google Scholar 

  • Kaplan Y (2016) Identifying individuals with an anterior cruciate ligament deficient knee as copers and non-copers: a narrative literature review. J Sci Med Sport 19:e26

    Google Scholar 

  • Kaufman KR, Hughes C, Morrey BF, Morrey M, An KN (2001) Gait characteristics of patients with knee osteoarthritis. J Biomech 34(7):907–915

    Google Scholar 

  • Kessler MA, Behrend H, Henz S, Stutz G, Rukavina A, Kuster MS (2008) Function, osteoarthritis and activity after ACL-rupture: 11 years follow-up results of conservative versus reconstructive treatment. Knee Surg Sports Traumatol Arthrosc 16(5):442–448

    Google Scholar 

  • Koga H, Nakamae A, Shima Y, Bahr R, Krosshaug T (2018) Hip and ankle kinematics in noncontact anterior cruciate ligament injury situations: video analysis using model-based image matching. Am J Sports Med 46(2):333–340

    Google Scholar 

  • Kopf S, Kauert R, Halfpaap J, Jung T, Becker R (2012) A new quantitative method for pivot shift grading. Knee Surg Sports Traumatol Arthrosc 20(4):718–723

    Google Scholar 

  • Li Y, Xu M, Wei Y, Huang W (2015) Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition. Mech Mach Theory 94:9–27

    Google Scholar 

  • Machado M, Flores P, Claro JP, Ambrosio J, Silva M, Completo A, Lankarani HM (2010) Development of a planar multibody model of the human knee joint. Nonlinear Dyn 60(3):459–478

    MATH  Google Scholar 

  • Martin AE, Schmiedeler JP (2014) Predicting human walking gaits with a simple planar model. J Biomech 47(6):1416–1421

    Google Scholar 

  • McCarthy I, Hodgins D, Mor A, Elbaz A, Segal G (2013) Analysis of knee flexion characteristics and how they alter with the onset of knee osteoarthritis: a case control study. BMC Musculoskel Disord 14(1):169

    Google Scholar 

  • Mehdizadeh S (2017) The largest Lyapunov exponent of gait in young and elderly individuals: a systematic review. Gait Posture 60:241–250

    Google Scholar 

  • Moraiti C, Stergiou N, Ristanis S, Georgoulis AD (2007) ACL deficiency affects stride-to-stride variability as measured using nonlinear methodology. Knee Surg Sports Traumatol Arthr 15(12):1406–1413

    Google Scholar 

  • Ntagiopoulos PG, Dejour DH (2017) The use of stress X-rays in the evaluation of ACL deficiency. In: Musahl V, Karlsson J, Kuroda R, Zaffagnini S (eds) Rotatory knee instability. Springer, Berlin

    Google Scholar 

  • Oiestad BE, Engebretsen L, Storheim K, Risberg MA (2009) Knee osteoarthritis after anterior cruciate ligament injury: a systematic review. Am J Sports Med 37(7):1434–1443

    Google Scholar 

  • Park C, Looney D, Van Hulle MM, Mandic DP (2011) The complex local mean decomposition. Neurocomputing 74(6):867–875

    Google Scholar 

  • Prabhu P, Karunakar AK, Anitha H, Pradhan N (2018) Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2018.05.006

    Article  Google Scholar 

  • Roberts D, Andersson G, Friden T (2004) Knee joint proprioception in ACL-deficient knees is related to cartilage injury, laxity and age: a retrospective study of 54 patients. Acta Orthop Scand 75(1):78–83

    Google Scholar 

  • Robinson MA, Donnelly CJ, Tsao J, Vanrenterghem J (2013) Impact of knee modeling approach on indicators and classification of ACL injury risk. Med Sci Sports Exerc 46:1269–1276

    Google Scholar 

  • Robinson MA, Donnelly CJ, Tsao J (2014) Impact of knee modeling approach on indicators and classification of anterior cruciate ligament injury risk. Med Sci Sports Exerc 46(7):1269–1276

    Google Scholar 

  • Shabani B, Bytyqi D, Lustig S, Cheze L, Bytyqi C, Neyret P (2015) Gait changes of the ACL-deficient knee 3D kinematic assessment. Knee Surg Sports Traumatol Arthrosc 23(11):3259–3265

    Google Scholar 

  • Shultz SJ, Schmitz RJ, Benjaminse A, Collins M, Ford K, Kulas AS (2015) ACL research retreat VII: an update on anterior cruciate ligament injury risk factor identification, screening, and prevention. J Athl Train 50(10):1076–1093

    Google Scholar 

  • Slater LV, Hart JM, Kelly AR, Kuenze CM (2017) Progressive changes in walking kinematics and kinetics after anterior cruciate ligament injury and reconstruction: a review and meta-analysis. J Athl Train 52(9):847–860

    Google Scholar 

  • Stauffer RN, Chao EY, Gyory AN (1977) Biomechanical gait analysis of the diseased knee joint. Clin Orthop Relat Res 126:246–255

    Google Scholar 

  • Stergiou N, Decker LM (2011) Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 30(5):869–888

    Google Scholar 

  • Stergiou N, Moraiti C, Giakas G, Ristanis S, Georgoulis AD (2004) The effect of the walking speed on the stability of the anterior cruciate ligament deficient knee. Clin Biomech 19(9):957–963

    Google Scholar 

  • Taga G (1995) A model of the neuro-musculo-skeletal system for human locomotion. Biol Cybern 73(2):97–111

    MATH  Google Scholar 

  • Takeda Y, Xerogeanes JW, Livesay GA, Fu FH, Woo SL (1994) Biomechanical function of the human anterior cruciate ligament. Arthroscopy 10(2):140–147

    Google Scholar 

  • Takeda K, Hasegawa T, Kiriyama Y, Matsumoto H, Otani T, Toyama Y, Nagura T (2014) Kinematic motion of the anterior cruciate ligament deficient knee during functionally high and low demanding tasks. J Biomech 47(10):2526–2530

    Google Scholar 

  • Veiga JJD, O’Reilly M, Whelan D, Caulfield B, Ward TE (2017) Feature-free activity classification of inertial sensor data with machine vision techniques: method, development, and evaluation. JMIR mHealth uHealth 5(8):e115

    Google Scholar 

  • Wagner M, Slijepcevic D, Horsak B, Rind A, Zeppelzauer M, Aigner W (2018) KAVAGait: knowledge-assisted visual analytics for clinical gait analysis. IEEE Trans Vis Comput Gr 25(3):1528–1542

    Google Scholar 

  • Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130–146

    Google Scholar 

  • Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617–630

    Google Scholar 

  • Wang C, Hill DJ (2009) Deterministic learning theory for identification, recognition and control. CRC Press, Boca Raton

    Google Scholar 

  • Wellsandt E, Zeni JA, Axe MJ, Snyder-Mackler L (2017) Hip joint biomechanics in those with and without post-traumatic knee osteoarthritis after anterior cruciate ligament injury. Clin Biomech 50:63–69

    Google Scholar 

  • West BJ, Scafetta N (2003) Nonlinear dynamical model of human gait. Phys Rev E 67(5):051917

    MathSciNet  Google Scholar 

  • Xiang Y, Arora JS, Abdel-Malek K (2010) Physics-based modeling and simulation of human walking: a review of optimization-based and other approaches. Struct Multidiscip Optim 42(1):1–23

    MathSciNet  MATH  Google Scholar 

  • Xing Z, Qu J, Chai Y, Tang Q, Zhou Y (2017) Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 31(2):545–553

    Google Scholar 

  • Xu G, Wang Z, Huang H, Li W, Liu C, Liu S (2018) A model for medical diagnosis based on plantar pressure. arXiv preprint arXiv:1802.10316

  • Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N (2013) The appropriate use of approximate entropy and sample entropy with short data sets. Ann Biomed Eng 41(2):349–365

    Google Scholar 

  • Yu J, Cao JY, Li CG (2017) Dynamic modeling and complexity analysis of human lower limb under various speeds. Appl Mech Mater 868:212–217

    Google Scholar 

  • Yuan Q, Cai C, Xiao H, Liu X, Wen Y (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. In: Huang DS, Heutte L, Loog M (eds) Advanced intelligent computing theories and applications: with aspects of contemporary intelligent computing techniques. Springer, Berlin, pp 1250–1260

    Google Scholar 

  • Zampeli F, Moraiti CO, Xergia S, Tsiaras VA, Stergiou N, Georgoulis AD (2010) Stride-to-stride variability is altered during backward walking in anterior cruciate ligament deficient patients. Clin Biomech 25(10):1037–1041

    Google Scholar 

  • Zantop T, Herbort M, Raschke MJ, Fu FH, Petersen W (2007) The role of the anteromedial and posterolateral bundles of the anterior cruciate ligament in anterior tibial translation and internal rotation. Am J Sports Med 35(2):223–227

    Google Scholar 

  • Zeng W, Wang C (2012) Human gait recognition via deterministic learning. Neural Netw 35:92–102

    Google Scholar 

  • Zeng W, Wang C (2015) Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf Sci 317:246–258

    Google Scholar 

  • Zhang Y, Ji X, Liu B, Huang D, Xie F, Zhang Y (2017) Combined feature extraction method for classification of EEG signals. Neural Comput Appl 28(11):3153–3161

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773194, 61304084), by the Natural Science Foundation of Fujian Province of China (Grant No. 2018J01542), by Fujian Provincial Training Foundation For “Bai-Qian-Wan Talents Engineering” and by the Program for New Century Excellent Talents in Fujian Province University.

Author information

Authors and Affiliations

  1. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, People’s Republic of China

    Wei Zeng

  2. Discipline of Physiotherapy, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, 2141, Australia

    Wei Zeng, Shiek Abdullah Ismail & Evangelos Pappas

Authors
  1. Wei Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiek Abdullah Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evangelos Pappas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Zeng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, W., Ismail, S.A. & Pappas, E. Detecting the presence of anterior cruciate ligament deficiency based on a double pendulum model, intrinsic time-scale decomposition (ITD) and neural networks. Artif Intell Rev 53, 3231–3253 (2020). https://doi.org/10.1007/s10462-019-09761-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09761-0

Keywords

Search

Navigation