Skip to main content

Advertisement

SpringerLink
Log in

Selection of clinical features for pattern recognition applied to gait analysis

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadence and stride may add useful information for the movement evaluation.

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

Similar content being viewed by others

References

  1. Ahlrichs C, Samà A, Lawo M, Cabestany J, Rodríguez-Martín D, Pérez-López C, Sweeney D, Quinlan LR, Laighin GÒ, Counihan T et al (2016) Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients. Med Biol Eng Comput 54(1):223–233

    Article  PubMed  Google Scholar 

  2. Almuallim H, Dietterich TG (1994) Learning boolean concepts in the presence of many irrelevant features. Artif Intell 69(1–2):279–305

    Article  Google Scholar 

  3. Altilio R, Liparulo L, Panella M, Proietti A, Paoloni M (2015) Multimedia and gaming technologies for telerehabilitation of motor disabilities [leading edge]. IEEE Technol Soc Mag 34(4):23–30. doi:10.1109/MTS.2015.2494279

    Article  Google Scholar 

  4. Altman DG, Bland JM (1994) Diagnostic tests. 1: sensitivity and specificity. BMJ Br Med J 308(6943):1552

    Article  CAS  Google Scholar 

  5. Ayachi F, Boudaoud S, Marque C (2014) Evaluation of muscle force classification using shape analysis of the semg probability density function: a simulation study. Med Biol Eng Comput 52(8):673–684

    Article  CAS  PubMed  Google Scholar 

  6. Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38(3):401–408

    Article  CAS  PubMed  Google Scholar 

  7. Bhanu B, Lee S, Ming J (1995) Adaptive image segmentation using a genetic algorithm. IEEE Trans Syst Man Cybern 25(12):1543–1567

    Article  Google Scholar 

  8. Broadhurst D, Goodacre R, Jones A, Rowland JJ, Kell DB (1997) Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal Chim Acta 348(1):71–86

    Article  CAS  Google Scholar 

  9. Chester VL, Biden EN, Tingley M (2005) Gait analysis. Biomed Instrum Technol 39(1):64–74

    PubMed  Google Scholar 

  10. Cho CW, Chao WH, Lin SH, Chen YY (2009) A vision-based analysis system for gait recognition in patients with parkinsons disease. Expert Syst Appl 36(3):7033–7039

    Article  Google Scholar 

  11. Chun DN, Yang HS (1996) Robust image segmentation using genetic algorithm with a fuzzy measure. Pattern Recogn 29(7):1195–1211

    Article  Google Scholar 

  12. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46

    Article  Google Scholar 

  13. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  Google Scholar 

  14. Dikovski B, Madjarov G, Gjorgjevikj D (2014) Evaluation of different feature sets for gait recognition using skeletal data from kinect. In: 2014 37th international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, pp 1304–1308

  15. Ertuğrul ÖF, Kaya Y, Tekin R (2015) A novel approach for semg signal classification with adaptive local binary patterns. Med Biol Eng Comput 1–10

  16. Exell T, Freeman C, Meadmore K, Kutlu M, Rogers E, Hughes AM, Hallewell E, Burridge J (2013) Goal orientated stroke rehabilitation utilising electrical stimulation, iterative learning and Microsoft Kinect. In: 2013 IEEE international conference on rehabilitation robotics (ICORR), pp 1–6. doi:10.1109/ICORR.2013.6650493

  17. Fang J, Hunt KJ, Xie L, Yang GY (2015) Modelling of the toe trajectory during normal gait using circle-fit approximation. Med Biol Eng Comput 1–9. doi:10.1007/s11517-015-1414-4

  18. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  Google Scholar 

  19. Fisher RA (1938) The statistical utilization of multiple measurements. Ann Eugen 8(4):376–386

    Article  Google Scholar 

  20. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 1. Springer, Berlin

    Google Scholar 

  21. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  22. Ibrahim S, Chowriappa P, Dua S, Acharya UR, Noronha K, Bhandary S, Mugasa H (2015) Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Med Biol Eng Comput 53(12):1345–1360

    Article  PubMed  Google Scholar 

  23. Joshi CD, Lahiri U, Thakor NV (2013) Classification of gait phases from lower limb emg: application to exoskeleton orthosis. In: Point-of-care healthcare technologies (PHT). IEEE, pp 228–231

  24. Joyseeree R, Sabha RA, Mueller H (2014) Applying machine learning to gait analysis data for disease identification. Stud Health Technol Inf 210:850–854

    Google Scholar 

  25. Krishnan C, Washabaugh EP, Seetharaman Y (2015) A low cost real-time motion tracking approach using webcam technology. J Biomech 48(3):544–548

    Article  PubMed  Google Scholar 

  26. Krzanowski W (2000) Principles of multivariate analysis. Oxford University Press, Oxford

    Google Scholar 

  27. Kwak N, Choi CH (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 13(1):143–159

    Article  CAS  PubMed  Google Scholar 

  28. Lauer RT, Smith BT, Coiro D, Betz RR, McCarthy J (2004) Feasibility of gait event detection using intramuscular electromyography in the child with cerebral palsy. Neuromodul Technol Neural Interface 7(3):205–213

    Article  Google Scholar 

  29. Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in pls regression: how and when to use them. Chemom Intell Lab Syst 41(2):195–207

    Article  CAS  Google Scholar 

  30. Lin SH, Chen SW, Lo YC, Lai HY, Yang CH, Chen SY, Chang YJ, Chen CH, Huang WT, Jaw FS et al. (2016) Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm. Med Biol Eng Comput 54(2–3):485–496

    Article  PubMed  Google Scholar 

  31. Liu H, Motoda H (2012) Feature selection for knowledge discovery and data mining, vol 454. Springer, New York

    Google Scholar 

  32. Maisto M, Panella M, Liparulo L, Proietti A (2013) An accurate algorithm for the identification of fingertips using an RGB-D camera. IEEE J Emerg Sel Top Circuits Syst 3(2):272–283. doi:10.1109/JETCAS.2013.2256830

    Article  Google Scholar 

  33. Mangone M, Scettri P, Paoloni M, Procaccianti R, Spadaro A, Santilli V (2011) Pelvis-shoulder coordination during level walking in patients with ankylosing spondylitis. Gait Posture 34(1):1–5

    Article  PubMed  Google Scholar 

  34. Mazzone P, Paoloni M, Mangone M, Santilli V, Insola A, Fini M, Scarnati E (2014) Unilateral deep brain stimulation of the pedunculopontine tegmental nucleus in idiopathic parkinsons disease: effects on gait initiation and performance. Gait Posture 40(3):357–362

    Article  CAS  PubMed  Google Scholar 

  35. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Article  Google Scholar 

  36. Muro-de-la Herran A, Garcia-Zapirain B, Mendez-Zorrilla A (2014) Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2):3362–3394

    Article  PubMed  PubMed Central  Google Scholar 

  37. Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 100(9):917–922

    Article  Google Scholar 

  38. O’Malley MJ, Abel MF, Damiano DL, Vaughan CL (1997) Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters. IEEE Trans Rehabil Eng 5(4):300–309

    Article  PubMed  Google Scholar 

  39. Panella M, Rizzi A, Martinelli G (2003) Refining accuracy of environmental data prediction by MoG neural networks. Neurocomputing 55(3–4):521–549. doi:10.1016/S0925-2312(03)00392-8

    Article  Google Scholar 

  40. Panella M (2012) A hierarchical procedure for the synthesis of ANFIS networks. Adv Fuzzy Syst 2012:1–12. doi:10.1155/2012/491237

    Article  Google Scholar 

  41. Panella M, Martinelli G (2011) Neural networks with quantum architecture and quantum learning. Int J Circuit Theory Appl 39(1):61–77. doi:10.1002/cta.619

    Article  Google Scholar 

  42. Papaleo E, Zollo L, Garcia-Aracil N, Badesa F, Morales R, Mazzoleni S, Sterzi S, Guglielmelli E (2015) Upper-limb kinematic reconstruction during stroke robot-aided therapy. Med Biol Eng Comput 53(9):815–828

    Article  CAS  PubMed  Google Scholar 

  43. Patterson SL, Forrester LW, Rodgers MM, Ryan AS, Ivey FM, Sorkin JD, Macko RF (2007) Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil 88(1):115–119

    Article  PubMed  Google Scholar 

  44. Pereira T, Paiva JS, Correia C, Cardoso J (2016) An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers. Med Biol Eng Comput 54(7):1049–1059

    Article  PubMed  Google Scholar 

  45. Piramuthu S (2004) Evaluating feature selection methods for learning in data mining applications. Eur J Oper Res 156(2):483–494

    Article  Google Scholar 

  46. Pradhan C, Wuehr M, Akrami F, Neuhaeusser M, Huth S, Brandt T, Jahn K, Schniepp R (2015) Automated classification of neurological disorders of gait using spatio-temporal gait parameters. J Electromyogr Kinesiol 25(2):413–422

    Article  PubMed  Google Scholar 

  47. Proietti A, Panella M, Leccese F, Svezia E (2015) Dust detection and analysis in museum environment based on pattern recognition. Measurement 66:62–72. doi:10.1016/j.measurement.2015.01.019

    Article  Google Scholar 

  48. Purushotham S, Tripathy B (2012) Evaluation of classifier models using stratified tenfold cross validation techniques. In: Krishna PV, Babu MR, Ariwa E (eds) Global trends in information systems and software applications, Springer, Berlin, Heidelberg, pp 680–690. doi:10.1007/978-3-642-29216-3_74

  49. Rish I (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, no 22. IBM, New York, pp 41–46

  50. Rizzi A, Panella M, Mascioli FF, Martinelli G (2000) A recursive algorithm for fuzzy Min-Max networks. In: Proceedings of international joint conference on neural networks (IJCNN 2000), vol 6, pp 541–546. doi:10.1109/IJCNN.2000.859451

  51. Roerdink M, Lamoth CJ, Beek PJ et al (2008) Online gait event detection using a large force platform embedded in a treadmill. J Biomech 41(12):2628–2632

    Article  PubMed  Google Scholar 

  52. Rogati M, Yang Y (2002) High-performing feature selection for text classification. In: Proceedings of the eleventh international conference on Information and knowledge management. ACM, pp 659–661

  53. Rokach L, Maimon O (2014) Data mining with decision trees: theory and applications. World Scientific, Singapore

    Book  Google Scholar 

  54. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517

    Article  CAS  PubMed  Google Scholar 

  55. Saeys Y, Abeel T, Van de Peer Y (2008) Robust feature selection using ensemble feature selection techniques. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 313–325. doi:10.1007/978-3-540-87481-2_21

  56. Scardapane S, Wang D, Panella M (2016) A decentralized training algorithm for echo state networks in distributed big data applications. Neural Netw 78:65–74. doi:10.1016/j.neunet.2015.07.006

    Article  PubMed  Google Scholar 

  57. Schmid A, Duncan PW, Studenski S, Lai SM, Richards L, Perera S, Wu SS (2007) Improvements in speed-based gait classifications are meaningful. Stroke 38(7):2096–2100

    Article  PubMed  Google Scholar 

  58. Shirakawa T, Sugiyama N, Sato H, Sakurai K, Sato E (2015) Gait analysis and machine learning classification on healthy subjects in normal walking. Int J Parallel Emerg Distrib Syst 1–10. doi:10.1080/17445760.2015.1044007

    Google Scholar 

  59. Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Article  CAS  Google Scholar 

  60. Wu J, Wang J, Liu L (2007) Feature extraction via KPCA for classification of gait patterns. Hum Mov Sci 26(3):393–411

    Article  PubMed  Google Scholar 

  61. Xia Y, Gao Q, Lu Y, Ye Q (2015) A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis. Med Biol Eng Comput 1–10. doi: 10.1007/s11517-015-1413-5

    PubMed  Google Scholar 

  62. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. ICML 97:412–420

    Google Scholar 

  63. Yang M, Zheng H, Wang H, McClean S (2009) Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. In: 3rd international conference on pervasive computing technologies for healthcare. Pervasive health 2009. IEEE, pp 1–7

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

    Article  Google Scholar 

  65. Zeni JA, Higginson JS (2009) Differences in gait parameters between healthy subjects and persons with moderate and severe knee osteoarthritis: a result of altered walking speed? Clin Biomech 24(4):372–378

    Article  Google Scholar 

  66. Zhang Z, Liparulo L, Panella M, Gu X, Fang Q (2016) A fuzzy kernel motion classifier for autonomous stroke rehabilitation. IEEE J Biomed Health Inf 20(3):893–901. doi:10.1109/JBHI.2015.2430524

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, Via Eudossiana, 18, 00184, Rome, Italy

    Rosa Altilio & Massimo Panella

  2. Biomechanics and Movement Analysis Laboratory, Physical Medicine and Rehabilitation, University of Rome “La Sapienza”, Piazzale Aldo Moro, 5, 00185, Rome, Italy

    Marco Paoloni

Authors
  1. Rosa Altilio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Paoloni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Massimo Panella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosa Altilio.

Ethics declarations

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altilio, R., Paoloni, M. & Panella, M. Selection of clinical features for pattern recognition applied to gait analysis. Med Biol Eng Comput 55, 685–695 (2017). https://doi.org/10.1007/s11517-016-1546-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-016-1546-1

Keywords

Search

Navigation