Skip to main content
Springer Nature Link
Log in

Detection of freezing of gait for Parkinson’s disease patients with multi-sensor device and Gaussian neural networks

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Freezing of Gait (FoG) in Parkinson Disease (PD) is a sudden episode characterized by a brief failure to walk. The aim of this study is to detect FoG episodes using a multi-sensor device for data acquisition, and Gaussian neural networks as a classification tool. Thus we have built a multi sensor prototype that detects FoG using new indicators like the variation of the inter-foot distance or the knee angle. Data are acquired from PD patients having FoG as a major symptom. The major social challenge is obtaining the acknowledgment of patients to participate in our study, whereas the main technical difficulty is extracting efficient features from various walking behaviors. For that purpose, the acquired signals are analyzed in order to extract both time and frequency domain features that separate the FoG class from the other gaits modes. Due to the complexity of FoG episodes, the optimal features are then extracted using Principal Component Analysis technique. Another contribution is to introduce the combined data into the Gaussian Neural Network (GNN) classification method, that is a new technique used for FoG detection, and has been developed in our previous works. Moreover, the classical thresholding method is implemented to compare and validate the GNN method. Results showed the feasibility of integrating the chosen sensors, in addition to the effectiveness of combining data from different types of sensors on the classification rate. The efficiency rate of classification in the proposed method is about 87 %.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bachlin M, Plotnik M, Roggen D, Maidan I, Hausdorff J, Giladi N, Troster G (2010) Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 14(2):436–446

    Article  Google Scholar 

  2. Barakat M, Druaux F, Lefebvre D, Khalil M, Mustapha M (2011) Self adaptive growing neural network classifier for faults detection and diagnosis. Neurocomputing 74(18):3865–3876

    Article  Google Scholar 

  3. Bloem BR, Hausdorff JM, Visser JE, Giladi N (2004) Falls and freezing of gait in Parkinson’s disease: a review of two interconnected, episodic phenomena. Mov Disord 19(8):871–884

    Article  Google Scholar 

  4. Brayan TC, Sergy HR, Hamid N (2011) Detecting freezing-of-gait during unscripted and unconstrained activity. In: 33rd annual international conference of the IEEE engineering in medicine and biology society, Boston, USA, 30 Aug–03 Sept 2011

  5. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12(4):458–473

    Article  Google Scholar 

  6. Cheng CT, Chau K, Sun Y, Jianyi Lin (2005) Long-term prediction of discharges in Manwan reservoir using artificial neural network models. Lect Notes Comput Sci 3498:1040–1045

    Article  MATH  Google Scholar 

  7. Fahn S (1995) The freezing phenomenon in parkinsonism. Adv Neurol 67:53–63

    Google Scholar 

  8. Ghravian MH, Almas GF, Ohadi AR, Heidari BH (2013) Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes. Neurocomputing 121(9):150–159

    Article  Google Scholar 

  9. Giladi N, McMahon D, Przedborski S, Flaster E, Guillory S, Kostic V, Fahn S (1992) Motor blocks in Parkinson’s disease. PubMed Neurol 42:333–339

    Article  Google Scholar 

  10. Giladi N, Nieuwboer A (2008) Understanding and treating freezing of gait in Parkinsonism, proposed working definition and setting the stage. Mov Disord 23(11):23–25

    Google Scholar 

  11. Grimm LG (2002) Reading and understanding more multivariate statistics. American Psychological Association, Washington, DC

    Google Scholar 

  12. Han JH, Lee WJ, Ahn TB, Jeon BS, Park KS (2003) Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system. In: Proceedings of the 25th conference in engineering in medicine and biology society, Cancun, Mexico, 17–21 Sept 2003

  13. Handojoseno AM, Shine JM, Nguyen TN, Tran Y, Lewis SJ, Nguyen HT (2012) The detection of freezing of Gait in Parkinson’s disease patients using EEG signals based on Wavelet decomposition. In: 34th annual international conference of the IEEE engineering in medicine and biology society, San Diego, CA, USA, 28 Aug–01 Sept 2012

  14. Hausdorff JM, Balash Y, Giladi N (2003) Time series analysis of leg movements during freezing of gait in Parkinson’s disease: Rhyme or reason? Phys A 321(3–4):565–570

    Article  MathSciNet  MATH  Google Scholar 

  15. Horak F (1992) Postural inflexibility in Parkinsonian subjects. J NeurolSci 111(1):46–58

    Google Scholar 

  16. Huang ZK, Chau KW (2008) A new image thresholding method based on Gaussian mixture model. Appl Math Comput 205(2):899–907

    MathSciNet  MATH  Google Scholar 

  17. Jolliffe IT (2002) Properties of Population Principal Components. In: Bickel P, Diggle P, Fienberg SE, Gather U, Olkin I, Zeger S (eds) Principal component analysis, 2nd ed. Springer Science & Business Media, New York

  18. Jovanov E, Wang E, Verhagen L, Fredrickson M, Fratangelo R (2009) deFOG: a real time system for detection and unfreezing of gait of Parkinson’s patients. In: 31st annual international IEEE engineering in medicine and biology society Conference, Minneapolis, Minnesota, 3–6 Sept 2009

  19. Legendre L, Legendre P (1983) Numerical ecology: developments in environmental modeling. Elsevier Scientific Publication Co, Amsterdam

    MATH  Google Scholar 

  20. Lewis SJ, Dove A, Robbins TW, Barker RA, Owen AM (2003) Cognitive impairments in early Parkinson’s disease are accompanied by reductions in activity in frontostriatal neural circuitry. J Neurosci 23(15):6351–6356

    Google Scholar 

  21. Mazilu S, Blanke U, Hardegger M, Tröster G, Gazit E, Hausdorff JM (2014) GaitAssist: a daily-life support and training system for Parkinson’s disease patients with freezing of gait. In Proceedings of the 32nd annual ACM conference on human factors in computing systems (CHI ‘14). ACM, New York, USA, 28 April–1 May 2014

  22. Mazilu S, Blanke U, Roggen D, Tröster G, Gazit E, Hausdorff J (2013) Engineers meet clinicians: augmenting Parkinson’s disease patients to gather information for gait rehabilitation. In: Proceedings of the 4th augmented human international conference, Stuttgart, Germany 7–8 Mar 2013

  23. Mazilu S, Hardegger M, Zhu Z, Roggen D, Troster G, Plotnik M, Hausdorff J M (2012) Online detection of freezing of gait with smartphones and machine learning techniques. In: Proceedings of the 6th international conference on PervasiveHealth, San Diego, CA 21–24 May 2012

  24. Montgomery DC (2008) Introduction to statistical quality control. Wiley, New York

    MATH  Google Scholar 

  25. Moore O, Peretz C, Giladi N (2007) Freezing of gait affects quality of life of peoples with Parkinson’s disease beyond its relationships with mobility and gait. Mov Disord 22(15):2192–2195

    Article  Google Scholar 

  26. Moorea S, MacDougall H, Ondoc W (2008) Ambulatory monitoring of freezing of gait in Parkinson’s disease. J Neurosci Methods 167(2):304–308

    Google Scholar 

  27. Mustapha O, Lefebvre D, Khalil M, Hoblos G, Chafouk H (2009) Fault detection algorithm using DCS method combined with filters bank derived from the wavelet transform. Int J Innov Comput 5(5):1313–1327

    Google Scholar 

  28. Nutt JG, Bloem BR, Giladi N, Hallett M, Horak FB, Nieuwboer A (2011) Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol 10(8):734–744

    Article  Google Scholar 

  29. Okuma Y, Yanagisawa N (2008) The clinical spectrum of freezing of gait in Parkinson’s disease. Mov Disord 23:S426–S430

    Article  Google Scholar 

  30. Popovic MB, Jovicic MD, Radovanovic S, Petrovic I, Kostic V (2010) A simple method to assess freezing of gait in Parkinsons disease patients. Braz J Med Biol Res 43(9):883–889

    Article  Google Scholar 

  31. Ross GJ, Adams NM, Tasoulis DK, Hand DJ (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recognit Lett 33(2):191–198

    Article  Google Scholar 

  32. Sidibé Y, Druaux F, Lefebvre D, Leon F, Maze G (2013) Outils d’aide à la décision pour le diagnostic des structures immerges. JESA 47(4–8):339–360

    Article  Google Scholar 

  33. Snijders AH, Nijkrake MJ, Bakker M, Munneke M, Wind C, Bloem B (1995) Clinimetrics of freezing of gait. Mov Disord 23:S468–S474

    Article  Google Scholar 

  34. Taormina R, Chau KW (2015) Neural network river forecasting with multi-objective fully informed particle swarm optimization. J Hydroinform 17(1):99–113

    Article  Google Scholar 

  35. Tison F (1997) Other causes of parkinsonism. Baillieres Clin Neurol 6(1):205–218

    Google Scholar 

  36. Tripoliti E, Tzallas A, Tsipouras M, Rigas G, Bougia P, Leontiou M, Konitsiotis S, Chondrogiorgi M, Tsouli S, Fotiadis D (2013) Automatic detection of freezing of gait events in patients with Parkinson’s disease. Comput Methods Progr Biomed 110(1):12–26

    Article  Google Scholar 

  37. Vikas C, Anil K, Ahlawat RS Bhatia (2011) Growing neural networks using soft competitive learning. Int J Comput Appl 21(3):1–6

    Google Scholar 

  38. Wu CL, Chau KW (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of GREAH (Groupe de Recherche en Electrotechnique et Automatique), University of Le Havre, Le Havre, France

    Ali Saad, François Guerin & Dimitri Lefebvre

  2. Faculty of Business and Economical Sciences, Doctoral School of Science and Technology, Lebanese University, Beirut, Lebanon

    Iyad Zaarour

  3. Neuro-Science Parkinson Center, Holy Spirit University of Kaslik, Notre Dame de Secours University Hospital, Beirut, Lebanon

    Paul Bejjani

  4. Department of Biomedical, Faculty of Engineering, Islamic University of Lebanon, Khaldeh, Lebanon

    Mohammad Ayache

Authors
  1. Ali Saad
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Iyad Zaarour
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. François Guerin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Paul Bejjani
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Mohammad Ayache
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Dimitri Lefebvre
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ali Saad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saad, A., Zaarour, I., Guerin, F. et al. Detection of freezing of gait for Parkinson’s disease patients with multi-sensor device and Gaussian neural networks. Int. J. Mach. Learn. & Cyber. 8, 941–954 (2017). https://doi.org/10.1007/s13042-015-0480-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-015-0480-0

Keywords