A practical step length algorithm using lower limb angular velocities.

The use of Inertial Measurement Units (IMUs) for spatial gait analysis has opened the door to unconstrained measurements within the home and community. Bandwidth, cost limitations, and ease of use has historically restricted the number and location of sensors worn on the body. In this paper, we describe a four-sensor configuration of IMUs placed on the shanks and thighs that is sufficient to provide an accurate measure of temporal gait parameters, spatial gait parameters, and joint angle dynamics during ambulation. Estimating spatial gait parameters solely from gyroscope data is preferred because gyroscopes are less susceptible to sensor noise and a system comprised of only gyroscopes uses decreased bandwidth compared to a typical 9 degree-of-freedom IMU. The purpose of this study was to determine the validity of a novel method of step length estimation using gyroscopes attached to the shanks and thighs. An Inverted Pendulum Model algorithm (IPM) was proposed to calculate step length, stride length, and gait speed. The algorithm incorporates heel-strike events and average forward velocity per step to make these assessments. IMU algorithm accuracy was determined via concurrent validity with an instrumented walkway and results explained via the collision model of gait. The IPM produced accurate estimates of step length, stride length, and gait speed with a mean difference of 3 cm and an RMSE of 6.6 cm for step length, thus establishing a new approach for spatial gait parameter calculation. The lack of numerical integration in IPM makes it well suited for use in continuous monitoring applications where sensor sampling rates are restricted.

[1]  Aurelio Cappozzo,et al.  An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking. , 2012, Gait & posture.

[2]  Adrian Burns,et al.  An adaptive gyroscope-based algorithm for temporal gait analysis , 2010, Medical & Biological Engineering & Computing.

[3]  S. Studenski,et al.  Gait speed and survival in older adults. , 2011, JAMA.

[4]  Wiebren Zijlstra,et al.  Trunk-acceleration based assessment of gait parameters in older persons: a comparison of reliability and validity of four inverted pendulum based estimations. , 2013, Gait & posture.

[5]  J. Donelan,et al.  Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. , 2002, The Journal of experimental biology.

[6]  Jeffrey M. Hausdorff,et al.  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. , 2015, Gait & posture.

[7]  A. L. Evans,et al.  Gait speed and activities of daily living function in geriatric patients. , 1995, Archives of physical medicine and rehabilitation.

[8]  E. Allseits,et al.  The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units. , 2017, Journal of biomechanics.

[9]  Wiebren Zijlstra,et al.  The step length–frequency relationship in physically active community-dwelling older women , 2008, European Journal of Applied Physiology.

[10]  Angelo M. Sabatini,et al.  Assessment of walking features from foot inertial sensing , 2005, IEEE Transactions on Biomedical Engineering.

[11]  Kamiar Aminian,et al.  3D gait assessment in young and elderly subjects using foot-worn inertial sensors. , 2010, Journal of biomechanics.

[12]  A. Kuo,et al.  Comparison of kinematic and kinetic methods for computing the vertical motion of the body center of mass during walking. , 2004, Human movement science.

[13]  Peter G Adamczyk,et al.  Redirection of center-of-mass velocity during the step-to-step transition of human walking , 2009, Journal of Experimental Biology.

[14]  Catherine Dehollain,et al.  Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring , 2004, IEEE Transactions on Biomedical Engineering.

[15]  S. Miyazaki,et al.  Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope , 1997, IEEE Transactions on Biomedical Engineering.

[16]  Annika Plate,et al.  Gait recording with inertial sensors--How to determine initial and terminal contact. , 2016, Journal of biomechanics.

[17]  Jeffrey M. Hausdorff,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 , 2014, Journal of NeuroEngineering and Rehabilitation.

[18]  Jeffrey M. Hausdorff,et al.  Gait variability and fall risk in community-living older adults: a 1-year prospective study. , 2001, Archives of physical medicine and rehabilitation.

[19]  Marco Donati,et al.  Measuring spatio-temporal features of level walking using a single waist mounted inertial sensor , 2012 .

[20]  Angelo M. Sabatini,et al.  A step toward GPS/INS personal navigation systems: real-time assessment of gait by foot inertial sensing , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[22]  Angelo Cappello,et al.  Kinematic strategy evaluation by single-axis accelerometers during voluntary oscillations , 2012 .

[23]  K. Aminian,et al.  Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. , 2004, Gait & posture.

[24]  S. Rubin,et al.  Prognostic Value of Usual Gait Speed in Well‐Functioning Older People—Results from the Health, Aging and Body Composition Study , 2005, Journal of the American Geriatrics Society.

[25]  Q Li,et al.  Walking speed estimation using a shank-mounted inertial measurement unit. , 2010, Journal of biomechanics.

[26]  Richard W. Bohannon Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. , 1997, Age and ageing.

[27]  Kamiar Aminian,et al.  A Novel Approach to Reducing Number of Sensing Units for Wearable Gait Analysis Systems , 2013, IEEE Transactions on Biomedical Engineering.

[28]  Andrea Cereatti,et al.  Bilateral step length estimation using a single inertial measurement unit attached to the pelvis , 2012, Journal of NeuroEngineering and Rehabilitation.

[29]  M H Granat,et al.  A practical gait analysis system using gyroscopes. , 1999, Medical engineering & physics.

[30]  Edwin van Asseldonk,et al.  Use of Inertial Sensors for Ambulatory Assessment of Center-of-Mass Displacements During Walking , 2012, IEEE Transactions on Biomedical Engineering.

[31]  Chan Gook Park,et al.  Adaptive step length estimation algorithm using optimal parameters and movement status awareness. , 2011, Medical engineering & physics.

[32]  B. Galna,et al.  Quantification of soft tissue artifact in lower limb human motion analysis: a systematic review. , 2010, Gait & posture.

[33]  Vibhor Agrawal,et al.  Missing Sample Recovery for Wireless Inertial Sensor-Based Human Movement Acquisition , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.