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Trajectory-based gait pattern shift detection for assistive robotics applications

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Abstract

In the medical field, rehabilitation of the lower limbs is a slow and continuous process, where healthcare professionals have to follow each patient over long periods of time. In conventional rehabilitation, the progression of a patient is assessed by a professional, who analyzes visual tracking data. This assessment is dependent on each professional’s own experience. In this paper, we propose an approach to analyze tracking data, captured by our robotic walker’s gait tracking system, to detect shifts in the gait pattern over time automatically. For this purpose, the system takes in gait tracking data and segments it into gait cycles (heel strike to heel strike). Then, our approach handles each gait cycle considering it as a group of gait parameters that define trajectories in that time frame. From each gait parameter within the cycle, spatiotemporal features are extracted and similarity rates are computed using autoencoders. These spatiotemporal features and similarity rates are fused in a feature space which is fed to a one-class support vector machine that constructs a model of the observable gait cycle. Each posterior observed gait cycle is checked for shifts, using a set of proposed novelty detection techniques. Experimental tests using a dataset captured using our robotic walker platform revealed a promising performance when detecting if a gait cycle is ‘reference’ or ‘novel’ when compared to a previously trained model of a unique ‘reference’ gait pattern.

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References

  1. Harper S (2014) Ageing societies. Routledge, London

    Book  Google Scholar 

  2. WHO \(|\) Rehabilitation in health systems

  3. Bloch F, Thibaud M, Dugué B, Brèque C, Rigaud AS, Kemoun G (2010) Episodes of falling among elderly people: a systematic review and meta-analysis of social and demographic pre-disposing characteristics. Clinics 65(9):895

    Article  Google Scholar 

  4. Chou CH, Hwang CL, Wu YT (2012) Effect of exercise on physical function, daily living activities, and quality of life in the frail older adults: a meta-analysis. Arch Phys Med Rehabil 93(2):237

    Article  Google Scholar 

  5. Granger CV, Albrecht GL, Hamilton BB (1979) Outcome of comprehensive medical rehabilitation: measurement by PULSES profile and the Barthel index. Arch Phys Med Rehabil 60(4):145

    Google Scholar 

  6. Baker R (2006) Gait analysis methods in rehabilitation. J NeuroEng Rehabil 3(1):1

    Article  Google Scholar 

  7. Iosa M, Cereatti A, Merlo A, Campanini I, Paolucci S, Cappozzo A (2014) Assessment of waveform similarity in clinical gait data: the linear fit method. BioMed Res Int 2014:1–7

    Article  Google Scholar 

  8. Paulo J, Peixoto P, Nunes UJ (2017) ISR-AIWALKER: robotic walker for intuitive and safe mobility assistance and gait analysis. IEEE Trans Hum Mach Syst 47:1110–1122

    Article  Google Scholar 

  9. Paulo J, Asvadi A, Peixoto P, Amorim P (2017) Human gait pattern changes detection system: a multimodal vision-based and novelty detection learning approach. Biocybern Biomed Eng 37(4):701

    Article  Google Scholar 

  10. Perry J, Davids JR et al (1992) Gait analysis: normal and pathological function. J Pediatr Orthop 12(6):815

    Article  Google Scholar 

  11. Öberg T, Karsznia A, Öberg K (1993) Basic gait parameters: reference data for normal subjects, 10–79 years of age. J Rehabil Res Dev 30:210

    Google Scholar 

  12. Oberg T, Karsznia A, Oberg K (1994) Joint angle parameters in gait: reference data for normal subjects, 10–79 years of age. J Rehabil Res Dev 31(3):199

    Google Scholar 

  13. Melis EH, Torres-Moreno R, Barbeau H, Lemaire ED (1999) Analysis of assisted-gait characteristics in persons with incomplete spinal cord injury. Spinal Cord 37(6):430

    Article  Google Scholar 

  14. Martins M, Elias A, Cifuentes C, Alfonso M, Frizera A, Santos C, Ceres R (2014) Assessment of walker-assisted gait based on principal component analysis and wireless inertial sensors. Rev Bras Eng Bioméd 30(3):220

    Article  Google Scholar 

  15. Mezghani N, Husse S, Boivin K, Turcot K, Aissaoui R, Hagemeister N, De Guise JA (2008) Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier. IEEE Trans Biomed Eng 55(3):1230

    Article  Google Scholar 

  16. Djuric-Jovicic MD, Jovicic NS, Radovanovic SM, Stankovic ID, Popovic MB, Kostic VS (2014) Automatic identification and classification of freezing of gait episodes in Parkinson’s disease patients. IEEE Trans Neural Syst Rehabil Eng 22(3):685

    Article  Google Scholar 

  17. Cola G, Avvenuti M, Vecchio A, Yang GZ, Lo B (2015) An on-node processing approach for anomaly detection in gait. IEEE Sens J 15(11):6640

    Article  Google Scholar 

  18. Gardner AB, Krieger AM, Vachtsevanos G, Litt B (2006) One-class novelty detection for seizure analysis from intracranial EEG. J Mach Learn Res 7(Jun):1025

    MathSciNet  MATH  Google Scholar 

  19. Clifton L, Clifton DA, Watkinson PJ, Tarassenko L (2011) In: 2011 federated conference on computer science and information systems (FedCSIS). IEEE, pp 125–131

  20. Pimentel MA, Clifton DA, Tarassenko L (2013) In: 2013 IEEE international workshop on machine learning for signal processing (MLSP). IEEE, pp 1–6

  21. Pimentel MA, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215

    Article  Google Scholar 

  22. Solberg HE, Lahti A (2005) Detection of outliers in reference distributions: performance of Horn’s algorithm. Clin Chem 51(12):2326

    Article  Google Scholar 

  23. Angiulli F, Pizzuti C (2002) In: European conference on principles of data mining and knowledge discovery. Springer, pp 15–27

  24. Diaz I, Hollmén J (2002) In: Proceedings of the 2002 international joint conference on neural networks, 2002. IJCNN’02, vol 3. IEEE, pp 2070–2075

  25. Tax DM, Duin RP (1999) Support vector domain description. Pattern Recognit Lett 20(11):1191

    Article  Google Scholar 

  26. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443

    Article  MATH  Google Scholar 

  27. Hinton GE (1989) Connectionist learning procedures. Artif intell 40(1):185

    Article  Google Scholar 

  28. Meirovitch Y, Bennequin D, Flash T (2016) Geometrical invariance and smoothness maximization for task-space movement generation. IEEE Trans Robot 32(4):837. https://doi.org/10.1109/TRO.2016.2581208

    Article  Google Scholar 

  29. Kosinski RJ (2008) A literature review on reaction time, vol 10. Clemson University, Clemson

    Google Scholar 

  30. Müller M (2007) Dynamic time warping. In: Information retrieval for music and motion. Springer, Berlin, pp 69–84. https://doi.org/10.1007/2F978-3-540-74048-3

  31. Park SH, Goo JM, Jo CH (2004) Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 5(1):11

    Article  Google Scholar 

  32. Harato K, Nagura T, Matsumoto H, Otani T, Toyama Y, Suda Y (2008) A gait analysis of simulated knee flexion contracture to elucidate knee-spine syndrome. Gait Posture 28(4):687. https://doi.org/10.1016/j.gaitpost.2008.05.008

    Article  Google Scholar 

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Acknowledgements

This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the Ph.D. grant with Reference SFRH/BD/88672/2012 with funds from QREN POPH and the European Social Fund from the European Union.

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Correspondence to J. Paulo.

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Paulo, J., Peixoto, P. & Amorim, P. Trajectory-based gait pattern shift detection for assistive robotics applications. Intel Serv Robotics 12, 255–264 (2019). https://doi.org/10.1007/s11370-019-00280-z

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