Abstract
Gait assessment is frequently used as an outcome measure to determine changes in an individual’s mobility and disease processes. Inertial measurement units (IMUs) are quickly becoming commonplace in gait analysis. The purpose of this study was to determine and compare the validity of shank and lumbar IMU mounting locations in the estimation of temporal gait features. Thirty-seven adults performed 20 walking trials each over a gold standard force platform while wearing shank and lumbar-mounted IMUs. Data from the IMUs were used to estimate step times using previously published algorithms and were compared with those derived from the force platform. There was an excellent level of correlation between the force platform and shank (r=0.95) and lumbar-mounted (r=0.99) IMUs. Bland-Altman analysis demonstrated high levels of agreement between the IMU and the force platform step times. Confidence interval widths were 0.0782 s for the shank and 0.0367 s for the lumbar. Both IMU mounting locations provided accurate step time estimations, with the lumbar demonstrating a marginally superior level of agreement with the force platform. This validation indicates that the IMU system is capable of providing step time estimates within 2% of the gold standard force platform measurement.
Acknowledgments
This work was supported by the Science Foundation of Ireland (SFI/12/RC/2289) and Shimmer, Dublin, Ireland.
Conflicts of interest statement: The authors declare that Dr. Matthew Patterson and Dr. Niamh O’Mahony were employees of Shimmer, Dublin, Ireland, at the time that this study was carried out.
References
[1] Ben Mansour K, Rezzoug N, Gorce P. Analysis of several methods and inertial sensors locations to assess gait parameters in able-bodied subjects. Gait Posture 2015; 42: 409–414.10.1016/j.gaitpost.2015.05.020Search in Google Scholar PubMed
[2] Bigelow EM, Elvin NG, Elvin AA, Arnoczky SP. Peak impact accelerations during track and treadmill running. J Appl Biomech 2013; 29: 639–644.10.1123/jab.29.5.639Search in Google Scholar PubMed
[3] Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud 2010; 47: 931–936.10.1016/j.ijnurstu.2009.10.001Search in Google Scholar
[4] Dobkin BH. Wearable motion sensors to continuously measure real-world physical activities. Curr Opin Neurol 2013; 26: 602–608.10.1097/WCO.0000000000000026Search in Google Scholar PubMed PubMed Central
[5] Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair 2011; 25: 788–798.10.1177/1545968311425908Search in Google Scholar PubMed PubMed Central
[6] Fraccaro P, Walsh L, Doyle J, O’Sullivan D. Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction; Presented at the the Sixth International Conference on eHealth, Telemedicine and Social Medicine, Barcelona, Spain, 2014.Search in Google Scholar
[7] Godfrey A, Del Din S, Barry G, Mathers JC, Rochester L. Instrumenting gait with an accelerometer: a system and algorithm examination. Med Eng Phys 2015; 37: 400–417.10.1016/j.medengphy.2015.02.003Search in Google Scholar PubMed PubMed Central
[8] González RC, López AM, Rodriguez-Uría J, Álvarez D, Alvarez JC. Real-time gait event detection for normal subjects from lower trunk accelerations. Gait Posture 2010; 31: 322–325.10.1016/j.gaitpost.2009.11.014Search in Google Scholar PubMed
[9] Greene BR, McGrath D, O’Neill R, O’Donovan KJ, Burns A, Caulfield B. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput 2010; 48: 1251–1260.10.1007/s11517-010-0692-0Search in Google Scholar PubMed
[10] Hinkle DE, Wiersma W, Jurs SG. Applied Statistics for the Behavioral Sciences. 5th ed. Boston, MA, USA: Houghton Mifflin 2003.Search in Google Scholar
[11] Jasiewicz JM, Allum JH, Middleton JW, et al. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait Posture 2006; 24: 502–509.10.1016/j.gaitpost.2005.12.017Search in Google Scholar PubMed
[12] Kavanagh JJ, Menz HB. Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 2008; 28: 1–15.10.1016/j.gaitpost.2007.10.010Search in Google Scholar PubMed
[13] Lau H, Tong K. The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. Gait Posture 2008; 27: 248–257.10.1016/j.gaitpost.2007.03.018Search in Google Scholar PubMed
[14] Lee JA, Cho SH, Lee JW, Lee KH, Yang HK. Wearable accelerometer system for measuring the temporal parameters of gait. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 483–486.10.1109/IEMBS.2007.4352328Search in Google Scholar PubMed
[15] Lee Rodgers J, Nicewander WA. Thirteen ways to look at the correlation coefficient. Am Stat 1988; 42: 59–66.10.2307/2685263Search in Google Scholar
[16] Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 327: 307–310.10.1016/S0140-6736(86)90837-8Search in Google Scholar
[17] Mayagoitia RE, Nene AV, Veltink PH. Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J Biomech 2002; 35: 537–542.10.1016/S0021-9290(01)00231-7Search in Google Scholar PubMed
[18] Moe-Nilssen R. A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: the instrument. Clin Biomech (Bristol, Avon) 1998; 13: 320–327.10.1016/S0268-0033(98)00089-8Search in Google Scholar PubMed
[19] Moore ST, MacDougall HG, Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson’s disease. J Neurosci Methods 2008; 167: 340–348.10.1016/j.jneumeth.2007.08.023Search in Google Scholar PubMed
[20] Patterson MR, Caulfield B. Comparing adaptive algorithms to measure temporal gait parameters using lower body mounted inertial sensors. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 4509–4512.10.1109/EMBC.2012.6346969Search in Google Scholar PubMed
[21] Perry J, Burnfield JM, Cabico LM. Gait analysis: normal and pathological function. J Pediatr 1992; 12: 815.10.1097/01241398-199211000-00023Search in Google Scholar
[22] Riley PO, Paolini G, Della Croce U, Paylo KW, Kerrigan DC. A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects. Gait Posture 2007; 26: 17–24.10.1016/j.gaitpost.2006.07.003Search in Google Scholar PubMed
[23] Sofuwa O, Nieuwboer A, Desloovere K, Willems A-M, Chavret F, Jonkers I. Quantitative gait analysis in Parkinson’s disease: comparison with a healthy control group. Arch Phys Med Rehabil 2005; 86: 1007–1013.10.1016/j.apmr.2004.08.012Search in Google Scholar PubMed
[24] Taylor JK, Cihon C. Statistical techniques for data analysis. 2nd ed. Boca Raton, FL, USA: Chapman & Hall/CRC Press 2004.10.1201/9780203492390Search in Google Scholar
[25] Tirosh O, Sparrow WA. Identifying heel contact and toe-off using forceplate thresholds with a range of digital-filter cutoff frequencies. J Appl Biomech 2003; 19: 178–184.10.1123/jab.19.2.178Search in Google Scholar
[26] Tong K, Granat MH. A practical gait analysis system using gyroscopes. Med Eng Phys 1999; 21: 87–94.10.1016/S1350-4533(99)00030-2Search in Google Scholar PubMed
[27] Trojaniello D, Cereatti A, Della Croce U. Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk. Gait Posture 2014; 40: 487–492.10.1016/j.gaitpost.2014.07.007Search in Google Scholar PubMed
[28] Trojaniello D, Cereatti A, Pelosin E, et al. Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait. J Neuroeng Rehabil 2014; 11: 152.10.1186/1743-0003-11-152Search in Google Scholar PubMed PubMed Central
[29] Trojaniello D, Ravaschio A, Hausdorff JM, Cereatti A. Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects. Gait Posture 2015; 42: 310–316.10.1016/j.gaitpost.2015.06.008Search in Google Scholar PubMed
[30] Weiss A, Brozgol M, Dorfman M, et al. Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair 2013; 27: 742–752.10.1177/1545968313491004Search in Google Scholar PubMed
[31] Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One 2014; 9: e96675.10.1371/journal.pone.0096675Search in Google Scholar PubMed PubMed Central
[32] Weiss A, Sharifi S, Plotnik M, van Vugt JP, Giladi N, Hausdorff JM. Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil Neural Repair 2011; 25: 810–818.10.1177/1545968311424869Search in Google Scholar PubMed
[33] Winter DA. Biomechanics and motor control of human movement (no. Book, Whole). Hoboken, NJ: John Wiley & Sons 2009.10.1002/9780470549148Search in Google Scholar
[34] Yang S, Zhang J-T, Novak AC, Brouwer B, Li Q. Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. Gait Posture 2013; 37: 354–358.10.1016/j.gaitpost.2012.07.032Search in Google Scholar PubMed
[35] Zeni JA, Jr, Richards JG, Higginson JS. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture 2008; 27: 710–714.10.1016/j.gaitpost.2007.07.007Search in Google Scholar PubMed
[36] Zijlstra W, Hof AL. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 2003; 18: 1–10.10.1016/S0966-6362(02)00190-XSearch in Google Scholar PubMed
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- Computation of spatio-temporal parameters in level walking using a single inertial system in lean and obese adolescents
- 445-nm diode laser-assisted debonding of self-ligating ceramic brackets
- A seepage outlet boundary condition in hemodynamics modeling
- The role of relative membrane capacitance and time delay in cerebellar Purkinje cells
- Validation and comparison of shank and lumbar-worn IMUs for step time estimation
Articles in the same Issue
- Frontmatter
- Editorial
- Engineering of viable implants
- Research articles
- Umbilical cord as human cell source for mitral valve tissue engineering – venous vs. arterial cells
- Individual construction of freeform-fabricated polycaprolactone scaffolds for osteogenesis
- Automated bioreactor system for cartilage tissue engineering of human primary nasal septal chondrocytes
- Effect of steroidal saponins-loaded nano-bioglass/phosphatidylserine/collagen bone substitute on bone healing
- Engineering of biodegradable magnesium alloy scaffolds to stabilize biological myocardial grafts
- Regular research articles
- Computation of spatio-temporal parameters in level walking using a single inertial system in lean and obese adolescents
- 445-nm diode laser-assisted debonding of self-ligating ceramic brackets
- A seepage outlet boundary condition in hemodynamics modeling
- The role of relative membrane capacitance and time delay in cerebellar Purkinje cells
- Validation and comparison of shank and lumbar-worn IMUs for step time estimation