Validity and reliability of inertial measurement units for gait assessment within a post stroke population

References

• Patterson KK, Parafianowicz I, Danells CJ, Closson V, Verrier MC, Staines WR, et al. Gait asymmetry in community-ambulating stroke survivors. Arch Phys Med Rehabil. 2008;89(2):304–310. doi:10.1016/j.apmr.2007.08.142.
   PubMed Web of Science ®Google Scholar
• Hornby TG, Reisman DS, Ward IG, Scheets PL, Miller A, Haddad D, et al. Clinical practice guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury. J Neurol Phys Ther. Jan 2020;44(1):49–100. doi:10.1097/NPT.0000000000000303.
   PubMed Web of Science ®Google Scholar
• Kautz SA, Bowden MG, Clark DJ, Neptune RR. Comparison of motor control deficits during treadmill and overground walking poststroke. Neurorehabil Neural Repair. Oct, 2011;25(8):756–765. doi:10.1177/1545968311407515.
   PubMed Web of Science ®Google Scholar
• Miller E, Kaufman K, Kingsbury T, Wolf E, Wilken J, Wyatt M. Mechanical testing for three-dimensional motion analysis reliability. Gait Posture. Oct 2016;50:116–119. doi:10.1016/j.gaitpost.2016.08.017.
   PubMed Web of Science ®Google Scholar
• Lee H-K, Kim J-H, Myoung H-S, Lee J-H, Lee K-J. Repeatability of the accelerometric-based method to detect step events for hemiparetic stroke patients. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society [Internet]. Boston, MA: IEEE; p. 5195–5198. 2011 http://ieeexplore.ieee.org/document/6091285/. Accessed Mar 24, 2021.
   Google Scholar
• Moore SA, Hickey AH, Lord S, Del Din S, Godfrey A, Rochester L. Quantifying temporal and spatial gait characteristics in the clinic and community with a low-cost accelerometer-based wearable: A feasibility, validity and reliability study. Int J Stroke. 2017;12(suppl 5): 7–59.
   Google Scholar
• Bernhard FP, Sartor J, Bettecken K, Hobert MA, Arnold C, Weber YG, et al. Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients. BMC Neurol. 2018;18(1). doi:10.1186/s12883-018-1111-7.
   PubMedGoogle Scholar
• Simoes MA. Feasibility of wearable sensors to determine gait parameters. Department of Mechanical Engineering. Tampa, FL: USF Tampa Graduate Theses and Dissertations; 2011.
   Google Scholar
• Liu T, Inoue Y, Shibata K. Development of a wearable sensor system for quantitative gait analysis. Meas J Int Meas Confed. 2009;42(7):978–988. doi:10.1016/j.measurement.2009.02.002.
   Web of Science ®Google Scholar
• Al-Amri M, Nicholas K, Button K, Sparkes V, Sheeran L, Davies J. Inertial measurement units for clinical movement analysis: Reliability and concurrent validity. Sensors. 2018 Feb 28;18(3):719. doi:10.3390/s18030719.
   PubMed Web of Science ®Google Scholar
• Mancini M, Horak FB. Potential of APDM mobility lab for the monitoring of the progression of Parkinson’s disease. Expert Rev Med Devices. 2016;13(5):455–462. doi:10.1586/17434440.2016.1153421.
   PubMed Web of Science ®Google Scholar
• Washabaugh EP, Kalyanaraman T, Adamczyk PG, Claflin ES, Krishnan C. Validity and repeatability of inertial measurement units for measuring gait parameters. Gait Posture. 2017;55:87–93. doi10.1016/j.gaitpost.2017.04.013.
   PubMed Web of Science ®Google Scholar
• Morris R, Stuart S, McBarron G, Fino PC, Mancini M, Curtze C. Validity of mobility lab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease. Physiol Meas. Sep, 2019;40(9):095003. doi:10.1088/1361-6579/ab4023.
   PubMed Web of Science ®Google Scholar
• Mancini M, King L, Salarian A, Holmstrom L, McNames J, Horak FB. Mobility lab to assess balance and gait with synchronized body-worn sensors. J Bioeng Biomed Sci. 2011 Dec;12(Suppl 1):007.
   Google Scholar
• Rashid U, Kumari N, Taylor D, David T, Signal N. Gait event anomaly detection and correction during a split-belt treadmill task. IEEE Access. 2019;7:68469–68478. doi10.1109/ACCESS.2019.2918559.
   Web of Science ®Google Scholar
• Bruening DA, Ridge ST. Automated event detection algorithms in pathological gait. Gait Posture. 2014;39(1):472–477. doi:10.1016/j.gaitpost.2013.08.023.
   PubMed Web of Science ®Google Scholar
• Kim H, Kim YH, Kim SJ, Choi MT. Pathological gait clustering in post-stroke patients using motion capture data. Gait Posture. 2022 May 1;94:210–216. doi:10.1016/j.gaitpost.2022.03.007.
   PubMed Web of Science ®Google Scholar
• Cruz TH, Lewek MD, Dhaher YY. Biomechanical impairments and gait adaptations post-stroke: multi-factorial associations. J Biomech. 2009 Aug 7;42(11):1673–1677. doi:10.1016/j.jbiomech.2009.04.015.
   PubMed Web of Science ®Google Scholar
• Donlin MC, Ray NT, Higginson JS. User-driven treadmill walking promotes healthy step width after stroke. Gait Posture. May 2021;86:256–259. doi:10.1016/j.gaitpost.2021.03.031.
   PubMed Web of Science ®Google Scholar
• Zeni JA, Richards JG, Higginson JS. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture. May, 2008;27(4):710–714. doi:10.1016/j.gaitpost.2007.07.007.
   PubMed Web of Science ®Google Scholar
• Patterson KK, Gage WH, Brooks D, Black SE, McIlroy WE. Evaluation of gait symmetry after stroke: A comparison of current methods and recommendations for standardization. Gait Posture. 2010 Feb 1;31(2):241–246. doi:10.1016/j.gaitpost.2009.10.014.
   PubMed Web of Science ®Google Scholar
• Lin LIK. A concordance correlation coefficient to evaluate reproducibility. Biometrics. Mar, 1989;45(1):255. doi:10.2307/2532051.
   PubMed Web of Science ®Google Scholar
• Lin LI, McBride G, Bland JM, Altman DG. A proposal for strength-of-agreement criteria for Lin’s concordance correlation coefficient. Hamilton, NZ: National Institute of Water & Atmospheric Research; 2005.
   Google Scholar
• Papi E, Osei-Kuffour D, Chen YMA, McGregor AH. Use of wearable technology for performance assessment: A validation study. Med Eng Phys. Jul, 2015;37(7):698–704. doi:10.1016/j.medengphy.2015.03.017.
   PubMed Web of Science ®Google Scholar
• Fusca M, Negrini F, Perego P, Magoni L, Molteni F, Andreoni G. Validation of a wearable IMU system for gait analysis: Protocol and application to a new system. Appl Sci. 2018 Jul 18;8(7):1167. doi:10.3390/app8071167.
   Google Scholar
• Row Lazzarini BS, Kataras TJ. Treadmill walking is not equivalent to overground walking for the study of walking smoothness and rhythmicity in older adults. Gait Posture. May 2016;46:42–46. doi:10.1016/j.gaitpost.2016.02.012.
   PubMed Web of Science ®Google Scholar
• An CM, Son YL, Park YH, Moon SJ. Relationship between dynamic balance and spatiotemporal gait symmetry in hemiplegic patients with chronic stroke. Hong Kong Physiother J. Dec 2017;37:19–24. doi:10.1016/j.hkpj.2017.01.002.
   PubMedGoogle Scholar
• Rozanski GM, Wong JS, Inness EL, Patterson KK, Mansfield A. Longitudinal change in spatiotemporal gait symmetry after discharge from inpatient stroke rehabilitation. Disabil Rehabil. 2020 Feb 27;42(5):705–711. doi:10.1080/09638288.2018.1508508.
   PubMed Web of Science ®Google Scholar
• Singh AK, Farmer C, Den Berg MLE V, Killington M, Barr CJ. Accuracy of the FitBit at walking speeds and cadences relevant to clinical rehabilitation populations. Disabil Health J. Apr, 2016;9(2):320–323. doi:10.1016/j.dhjo.2015.10.011.
   PubMed Web of Science ®Google Scholar
• Klassen TD, Semrau JA, Dukelow SP, Bayley MT, Hill MD, Eng JJ. Consumer-based physical activity monitor as a practical way to measure walking intensity during inpatient stroke rehabilitation. Stroke. Sep, 2017;48(9):2614–2617. doi:10.1161/STROKEAHA.117.018175.
   PubMed Web of Science ®Google Scholar
• Clay L, Webb M, Hargest C, Adhia DB. Gait quality and velocity influences activity tracker accuracy in individuals post-stroke. Top Stroke Rehabil. Sep, 2019;26(6):412–417. doi:10.1080/10749357.2019.1623474.
   PubMed Web of Science ®Google Scholar