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Gait analysis in patients with neurological disorders using ankle-worn accelerometers


The purpose of this study is to investigate gait in patients with neurological disorders using accelerometers. Accelerometers were placed on both ankles of participants undergoing gait analysis. Data were collected during the 10-min walk test from healthy participants (n = 20) and patients with neurological deficits (n = 22) scheduled for surgery. Additional data were obtained after surgery for comparison. Both the time and frequency domain features were compared between healthy participants and patients. The interval between successive heel-strikes differed significantly, as did that between successive toe-offs. These features were correlated in healthy participants but not in patients, for whom the correlation coefficients tended to increase after surgery, indicating that the correlations can be used to monitor gait recovery and ankle-worn accelerometers were effective in collecting data for gait monitoring.

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  1. 1.

    Anderson B, Shi M, Tan VYF, Wang Y (2019) Mobile gait analysis using foot-mounted UWB sensors. In: Proceedings of ACM Interact Mob Wearable Ubiquitous Technol 3(3):Article 73.

  2. 2.

    Hamacher D, Singh NB, Dieën JHV, Heller MO, Taylor WR (2011) Kinematic measures for assessing gait stability in elderly individuals: a systematic review. J R Soc Interface 8(65):1682–1698.

    Article  Google Scholar 

  3. 3.

    Sorrentino I, Andrade Chavez FJ, Latella C, Fiorio L, Traversaro S, Rapetti L, Tirupachuri Y, Guedelha N, Maggiali M, Dussoni S, Metta G, Pucci D (2020) A novel sensorised insole for sensing feet pressure distributions. Sensors 20(3):747

    Article  Google Scholar 

  4. 4.

    Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28(1):1–15.

    Article  Google Scholar 

  5. 5.

    Kidder SM, Abuzzahab FS, Harris GF, Johnson JE (1996) A system for the analysis of foot and ankle kinematics during gait. IEEE Trans RehabilEng 4(1):25–32.

    Article  Google Scholar 

  6. 6.

    Dunn J, Runge R, Snyder M (2018) Wearables and the medical revolution. Per Med 15(5):429–448.

    Article  Google Scholar 

  7. 7.

    Chen K, Zdorova M, Nathan-Roberts D (2017) Implications of wearables, fitness tracking services, and quantified self on healthcare. Proc Human Factors Ergon Soc Annual Meeting 61(1):1066–1070.

    Article  Google Scholar 

  8. 8.

    Erdem NS, Ersoy C, Tunca C (2019) Gait analysis using smartwatches. In: Proceedings of 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), pp 1–6.

  9. 9.

    Mobbs RJ, Phan K, Maharaj M, Rao PJ (2016) Physical activity measured with accelerometer and self-rated disability in lumbar spine surgery: a prospective study. Global Spine J 6(5):459–464.

    Article  Google Scholar 

  10. 10.

    Mehmood A, Khan MA, Sharif M, Khan SA, Shaheen M, Saba T, Riaz N, Ashraf I (2020) Prosperous human gait recognition: an end-to-end system based on pre-trained CNN features selection. Multimed Tools Appl.

    Article  Google Scholar 

  11. 11.

    Arshad H, Khan MA, Sharif MI, Yasmin M, Tavares JMRS, Zhang YD, Satapathy SC (2020) A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition. Expert Syst e12541.

  12. 12.

    Arshad H, Khan MA, Sharif M, Yasmin M, Javed MY (2019) Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int J Mach Learn Cyb 10(12):3601–3618.

    Article  Google Scholar 

  13. 13.

    Muhammad S, Muhammad A, Muhammad Zeeshan T, Mussarat Y, Tanzila S, Urcun John T (2020) A machine learning method with treshold based parallel feature fusion and feature selection for automated gait recognition. J Organ End User Comput 32(2):67–92.

    Article  Google Scholar 

  14. 14.

    Ring EFJ, Ammer K (2012) Infrared thermal imaging in medicine. PhysiolMeas 33(3):R33–R46.

    Article  Google Scholar 

  15. 15.

    Yang CC, Hsu YL (2010) A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8).

  16. 16.

    Stamatakis J, Crémers J, Maquet D, Macq B, Garraux G (2011) Gait feature extraction in Parkinson's disease using low-cost accelerometers. In: Proceedings of 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 7900–7903.

  17. 17.

    Barth J, Oberndorfer C, Pasluosta C, Schülein S, Gassner H, Reinfelder S, Kugler P, Schuldhaus D, Winkler J, Klucken J, Eskofier BM (2015) Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data. Sensors 15(3):6419–6440

    Article  Google Scholar 

  18. 18.

    Chang H, Hsu Y, Yang S, Lin J, Wu Z (2016) A wearable inertial measurement system with complementary filter for gait analysis of patients with stroke or Parkinson’s disease. IEEE Access 4:8442–8453.

    Article  Google Scholar 

  19. 19.

    Liu T, Inoue Y, Shibata K (2009) Development of a wearable sensor system for quantitative gait analysis. Measurement 42(7):978–988.

    Article  Google Scholar 

  20. 20.

    Graham JE, Ostir GV, Fisher SR, Ottenbacher KJ (2008) Assessing walking speed in clinical research: a systematic review. J EvalClin 14(4):552–562.

    Article  Google Scholar 

  21. 21.

    Pirpiris M, Wilkinson AJ, Rodda J, Nguyen TC, Baker RJ, Nattrass GR, Graham HK (2003) Walking speed in children and young adults with neuromuscular disease: comparison between two assessment methods. J PediatrOrthop 23(3):302–307

    Google Scholar 

  22. 22.

    Steffen T, Seney M (2008) Test-retest reliability and minimal detectable change on balance and ambulation tests, the 36-item short-form health survey, and the unified Parkinson disease rating scale in people with parkinsonism. PhysTher 88(6):733–746.

    Article  Google Scholar 

  23. 23.

    Lam T, Noonan VK, Eng JJ, the SRT (2008) A systematic review of functional ambulation outcome measures in spinal cord injury. Spinal Cord 46(4):246–254.

    Article  Google Scholar 

  24. 24.

    Paltamaa J, Sarasoja T, Leskinen E, Wikström J, Mälkiä E (2007) Measures of physical functioning predict self-reported performance in self-care, mobility, and domestic life in ambulatory persons with multiple sclerosis. Arch Phys Med Rehab 88(12):1649–1657.

    Article  Google Scholar 

  25. 25.

    Manos A, Klein I, Hazan T (2019) Gravity-based methods for heading computation in pedestrian dead reckoning. Sensors 19(5):1170

    Article  Google Scholar 

  26. 26.

    Mizell D (2003) Using gravity to estimate accelerometer orientation. In: Proceedings of Seventh IEEE international symposium on wearable computers, pp 252–253.

  27. 27.

    Nam Y, Park JW (2013) Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor. IEEE J Biomed Health Inform 17(2):420–426.

    Article  Google Scholar 

  28. 28.

    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of Pervasive 2014: Pervasive Computing, pp 1–17

  29. 29.

    Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In: Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence 3, pp 1541–1546

  30. 30.

    Iosa M, Mazzà C, Frusciante R, Zok M, Aprile I, Ricci E, Cappozzo A (2007) Mobility assessment of patients with facioscapulohumeral dystrophy. ClinBiomech 22(10):1074–1082.

    Article  Google Scholar 

  31. 31.

    Perry J, Davids JR (1992) Gait analysis: normal and pathological function. J PediatrOrthop 12(6):815

    Google Scholar 

  32. 32.

    Iosa M, Fusco A, Marchetti F, Morone G, Caltagirone C, Paolucci S, Peppe A (2013) The Golden ratio of gait harmony: repetitive proportions of repetitive gait phases. Biomed Res Int 2013:918642.

    Article  Google Scholar 

  33. 33.

    Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL (2014) Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40(1):11–19.

    Article  Google Scholar 

  34. 34.

    Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J NeuroengRehabil 9(1):21.

    Article  Google Scholar 

  35. 35.

    Murray MP, Drought AB, Kory RC (1964) Walking patterns of normal men. J Bone Joint Surg Am 46(2):335–360

    Article  Google Scholar 

  36. 36.

    Root ML, Orien W, Weed J (1977) Normal and abnormal function of the foot:, vol II. Clinical Biomechanics Corporation, Los Angeles

    Google Scholar 

  37. 37.

    Arora S, Venkataraman V, Donohue S, Biglan KM, Dorsey ER, Little MA (2014) High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. In: Proceedings of 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 3641–3644.

  38. 38.

    Hsu WC, Sugiarto T, Lin YJ, Yang FC, Lin ZY, Sun CT, Hsu CL, Chou KN (2018) Multiple-wearable-sensor-based gait classification and analysis in patients with neurological disorders. Sensors 18(10):3397

    Article  Google Scholar 

  39. 39.

    Sejdić E, Lowry KA, Bellanca J, Redfern MS, Brach JS (2014) A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains. IEEE Trans Neural Syst Rehabil Eng 22(3):603–612.

    Article  Google Scholar 

  40. 40.

    Weiss A, Sharifi S, Plotnik M, van Vugt JPP, Giladi N, Hausdorff JM (2011) Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil Neural Repair 25(9):810–818.

    Article  Google Scholar 

  41. 41.

    Hirasaki E, Kubo T, Nozawa S, Matano S, Matsunaga T (1993) Analysis of head and body movements of elderly people during locomotion. ActaOto-Laryngol 113(sup501):25–30.

    Article  Google Scholar 

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This research was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012724, The Competency Development Program for Industry Specialist) and the Soonchunhyang University Research Fund.

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Correspondence to Yunyoung Nam.

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Kim, JY., Lee, S., Lee, H.B. et al. Gait analysis in patients with neurological disorders using ankle-worn accelerometers. J Supercomput 77, 8374–8390 (2021).

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