Abstract
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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References
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. https://doi.org/10.1145/3351231
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. https://doi.org/10.1098/rsif.2011.0416
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
Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28(1):1–15. https://doi.org/10.1016/j.gaitpost.2007.10.010
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. https://doi.org/10.1109/86.486054
Dunn J, Runge R, Snyder M (2018) Wearables and the medical revolution. Per Med 15(5):429–448. https://doi.org/10.2217/pme-2018-0044
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. https://doi.org/10.1177/1541931213601871
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. https://doi.org/10.1109/PIMRCW.2019.8880821
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. https://doi.org/10.1055/s-0035-1565259
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. https://doi.org/10.1007/s11042-020-08928-0
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. https://doi.org/10.1111/exsy.12541
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. https://doi.org/10.1007/s13042-019-00947-0
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. https://doi.org/10.4018/JOEUC.2020040104
Ring EFJ, Ammer K (2012) Infrared thermal imaging in medicine. PhysiolMeas 33(3):R33–R46. https://doi.org/10.1088/0967-3334/33/3/r33
Yang CC, Hsu YL (2010) A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8). https://doi.org/10.3390/s100807772
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. https://doi.org/10.1109/IEMBS.2011.6091948
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
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. https://doi.org/10.1109/ACCESS.2016.2633304
Liu T, Inoue Y, Shibata K (2009) Development of a wearable sensor system for quantitative gait analysis. Measurement 42(7):978–988. https://doi.org/10.1016/j.measurement.2009.02.002
Graham JE, Ostir GV, Fisher SR, Ottenbacher KJ (2008) Assessing walking speed in clinical research: a systematic review. J EvalClin 14(4):552–562. https://doi.org/10.1111/j.1365-2753.2007.00917.x
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
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. https://doi.org/10.2522/ptj.20070214
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. https://doi.org/10.1038/sj.sc.3102134
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. https://doi.org/10.1016/j.apmr.2007.07.032
Manos A, Klein I, Hazan T (2019) Gravity-based methods for heading computation in pedestrian dead reckoning. Sensors 19(5):1170
Mizell D (2003) Using gravity to estimate accelerometer orientation. In: Proceedings of Seventh IEEE international symposium on wearable computers, pp 252–253. https://doi.org/10.1109/ISWC.2003.1241424
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. https://doi.org/10.1109/JBHI.2012.2235075
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of Pervasive 2014: Pervasive Computing, pp 1–17
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
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. https://doi.org/10.1016/j.clinbiomech.2007.07.013
Perry J, Davids JR (1992) Gait analysis: normal and pathological function. J PediatrOrthop 12(6):815
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. https://doi.org/10.1155/2013/918642
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. https://doi.org/10.1016/j.gaitpost.2014.03.189
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. https://doi.org/10.1186/1743-0003-9-21
Murray MP, Drought AB, Kory RC (1964) Walking patterns of normal men. J Bone Joint Surg Am 46(2):335–360
Root ML, Orien W, Weed J (1977) Normal and abnormal function of the foot:, vol II. Clinical Biomechanics Corporation, Los Angeles
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. https://doi.org/10.1109/ICASSP.2014.6854280
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
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. https://doi.org/10.1109/TNSRE.2013.2265887
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. https://doi.org/10.1177/1545968311424869
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. https://doi.org/10.3109/00016489309126208
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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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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). https://doi.org/10.1007/s11227-020-03587-2
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DOI: https://doi.org/10.1007/s11227-020-03587-2