Advertisement

Detecting Parkinson’s Disease Using Gait Analysis with Particle Swarm Optimization

  • Xu Chen
  • Xiaohui Yao
  • Chen Tang
  • Yining Sun
  • Xun Wang
  • Xi Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10927)

Abstract

Gait analysis is the study of human movements by analyzing temporal and spatial gait features. Research has shown that Parkinson’s disease can degenerate human mobility, thereby causing afflicted individuals to behave differently in terms of gait characteristics. In this work, we propose an optimized method that assists us in better distinguishing people with Parkinson’s disease from normal subjects. The spatial-temporal gait features are extracted by using a real U-shaped pressure-sensitive gait-sensing walkway. After pre-processing optimizations, including nondimensionalization and normalization of the raw features, we feed the features to an SVM classifier for training. The Particle Swarm Optimization algorithm is adopted to optimize the classification model. Experimental results show that the optimized method outperforms its predecessor by improving the accuracy from 87.12% to 95.66%, which shows the effectiveness of our proposed method in detecting Parkinson’s Disease patients.

Keywords

Gait analysis PSO Support vector machine Parkinson’s disease 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 71661167004, the Anhui Key Project of Research and Development Plan under Grant No. 1704e1002221, the Foshan Science and Technology Innovation Project under Grant No. 2015IT100095, and the Program of Introducing Talents of Discipline to Universities (“111 Program”) under Grant No. B14025.

References

  1. 1.
    Whittle, M.W.: Gait analysis: an introduction - 3rd edition. Physiotherapy 77(11), 786 (2003)Google Scholar
  2. 2.
    Knutsson, E.: An analysis of parkinsonian gait. Brain J. Neurol. 95(3), 475–486 (1972)CrossRefGoogle Scholar
  3. 3.
    Sejdic, E., Lowry, K.A., Bellanca, J., Redfern, M.S., Brach, J.S.: A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains. IEEE Trans. Neural Syst. Rehabil. Eng. 22(3), 603–612 (2014)CrossRefGoogle Scholar
  4. 4.
    Bae, J., Kong, K., Byl, N., Tomizuka, M.: A mobile gait monitoring system for gait analysis. In: 11th International Conference on Rehabilitation Robotics, pp. 73–79. IEEE, Kyoto (2009)Google Scholar
  5. 5.
    Wang, A., Chen, G., Yang, J., Zhao, S., Chang, C.Y.: A comparative study on human activity recognition using inertial sensors in a smartphone. IEEE Sens. J. 16(11), 4566–4578 (2016)CrossRefGoogle Scholar
  6. 6.
    An, N., Que, X., Ding, H., Yuan, J., Tang, X., Farrer, L.A., Au, R.: Application of deep learning methods in identifying proteomic risk markers for Alzheimer’s disease. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCCB) (2018) (To appear)Google Scholar
  7. 7.
    Yang, J., Xu, Y., Sun, G., Shang, Y.: A new progressive algorithm for a multiple longest common subsequences problem and its efficient parallelization. IEEE Trans. Parallel Distrib. Syst. 24(5), 862–870 (2013)CrossRefGoogle Scholar
  8. 8.
    Eastlack, M.E., Arvidson, J., Snyder-Mackler, L., Danoff, J.V.: Interrater reliability of videotaped observational gait-analysis assessments. Phys. Ther. 71(6), 465–472 (1991)CrossRefGoogle Scholar
  9. 9.
    Clarkson, B.H.: Absorbent paper method for recording foot placement during gait. Suggestion from the field. Phys. Ther. 63(3), 345 (1983)CrossRefGoogle Scholar
  10. 10.
    Beijer, T.R., Lord, S.R., Brodie, M.A.: Comparison of handheld video camera and GAITRite® measurement of gait impairment in people with early stage Parkinson’s disease: a pilot study. J. Parkinsons Dis. 3(2), 199–203 (2013)Google Scholar
  11. 11.
    Cho, C.W., Chao, W.H., Lin, S.H., Chen, Y.Y.: A vision-based analysis system for gait recognition in patients with Parkinson’s disease. Expert Syst. Appl. 36(3), 7033–7039 (2009)CrossRefGoogle Scholar
  12. 12.
    Bamberg, S., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–423 (2008)CrossRefGoogle Scholar
  13. 13.
    Tien, I., Glaser, S.D., Aminoff, M.J.: Characterization of gait abnormalities in Parkinson’s disease using a wireless inertial sensor system. In: 32rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3353–3356. IEEE, Buenos Aires (2010)Google Scholar
  14. 14.
    Yoneyama, M., Kurihara, Y., Watanabe, K., Mitoma, H.: Accelerometry-based gait analysis and its application to Parkinson’s disease assessment— part 1: detection of stride event. IEEE Trans. Neural Syst. Rehabil. Eng. 22(3), 613 (2014)CrossRefGoogle Scholar
  15. 15.
    Morris, T.R., Cho, C., Dilda, V., Naismith, S.L.: Clinical assessment of freezing of gait in Parkinson’s disease from computer-generated animation. Gait & Posture 38(2), 326 (2013)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Wu, X., Xu, S., Yang, X., Wang, X.: U-shape electronic walkway system: dyskinesia assessment in Parkinson’s disease. Comput. Eng. Appl. 54(1), 166–171 (2018)Google Scholar
  17. 17.
    Hof, A.L.: Scaling gait data to body size. Gait & Posture 4(3), 222–223 (1996)CrossRefGoogle Scholar
  18. 18.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  19. 19.
    Cherkassky, V.: The nature of statistical learning theory. IEEE Trans. Neural Netw. 38(4), 409 (2002)Google Scholar
  20. 20.
    Ranaee, V., Ebrahimzadeh, A., Ghaderi, R.: Application of the PSO-SVM model for recognition of control chart patterns. ISA Trans. 49(4), 577–586 (2010)CrossRefGoogle Scholar
  21. 21.
    Ren, C., An, N., Wang, J., Li, L., Hu, B., Shang, D.: Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl.-Based Syst. 56(3), 226–239 (2014)CrossRefGoogle Scholar
  22. 22.
    Wu, X., Chen, X., Duan, Y., Xu, S., Cheng, N., An, N.: A study on gait-based Parkinson’s disease detection using a force sensitive platform. In: 8th IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2330–2332. IEEE, Kansan City (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xu Chen
    • 1
  • Xiaohui Yao
    • 2
  • Chen Tang
    • 1
  • Yining Sun
    • 3
  • Xun Wang
    • 4
  • Xi Wu
    • 2
  1. 1.Institute of Industrial and Equipment TechnologyHefei University of TechnologyHefeiChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina
  3. 3.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  4. 4.Hospital Affiliated to Institute of NeurologyAnhui University of Chinese MedicineHefeiChina

Personalised recommendations