Concurrent Validity of Accelerations Measured Using a Tri-Axial Inertial Measurement Unit while Walking on Firm, Compliant and Uneven Surfaces

Although accelerometers are extensively used for assessing gait, limited research has evaluated the concurrent validity of these devices on less predictable walking surfaces or the comparability of different methods used for gravitational acceleration compensation. This study evaluated the concurrent validity of trunk accelerations derived from a tri-axial inertial measurement unit while walking on firm, compliant and uneven surfaces and contrasted two methods used to remove gravitational accelerations; i) subtraction of the best linear fit from the data (detrending); and ii) use of orientation information (quaternions) from the inertial measurement unit. Twelve older and twelve younger adults walked at their preferred speed along firm, compliant and uneven walkways. Accelerations were evaluated for the thoracic spine (T12) using a tri-axial inertial measurement unit and an eleven-camera Vicon system. The findings demonstrated excellent agreement between accelerations derived from the inertial measurement unit and motion analysis system, including while walking on uneven surfaces that better approximate a real-world setting (all differences <0.16 m.s−2). Detrending produced slightly better agreement between the inertial measurement unit and Vicon system on firm surfaces (delta range: −0.05 to 0.06 vs. 0.00 to 0.14 m.s−2), whereas the quaternion method performed better when walking on compliant and uneven walkways (delta range: −0.16 to −0.02 vs. −0.07 to 0.07 m.s−2). The technique used to compensate for gravitational accelerations requires consideration in future research, particularly when walking on compliant and uneven surfaces. These findings demonstrate trunk accelerations can be accurately measured using a wireless inertial measurement unit and are appropriate for research that evaluates healthy populations in complex environments.

[1]  P. Hendrick,et al.  Construct validity of RT3 accelerometer: a comparison of level-ground and treadmill walking at self-selected speeds. , 2010, Journal of rehabilitation research and development.

[2]  R. Moe-Nilssen,et al.  A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument. , 1998, Clinical biomechanics.

[3]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[4]  P. Veltink,et al.  Estimating orientation with gyroscopes and accelerometers. , 1999, Technology and health care : official journal of the European Society for Engineering and Medicine.

[5]  Steven Morrison,et al.  The role of the neck and trunk in facilitating head stability during walking , 2006, Experimental Brain Research.

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

[7]  Steven Morrison,et al.  Lumbar and cervical erector spinae fatigue elicit compensatory postural responses to assist in maintaining head stability during walking. , 2006, Journal of applied physiology.

[8]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[9]  John S. Wilson,et al.  Sensor Technology Handbook , 2004 .

[10]  W. Zijlstra,et al.  Wearable systems for monitoring mobility-related activities in older people: a systematic review , 2008, Clinical rehabilitation.

[11]  Vincent Bonnet,et al.  Estimate of lower trunk angles in pathological gaits using gyroscope data. , 2013, Gait & posture.

[12]  M. Moy,et al.  Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[13]  R. Moe-Nilssen,et al.  Trunk accelerometry as a measure of balance control during quiet standing. , 2002, Gait & posture.

[14]  Marko Topič,et al.  Calibration and data fusion solution for the miniature attitude and heading reference system , 2007 .

[15]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[16]  Mark Latt,et al.  Walking speed, cadence and step length are selected to optimize the stability of head and pelvis accelerations , 2007, Experimental Brain Research.

[17]  Angelo M. Sabatini,et al.  Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis , 2006, Medical and Biological Engineering and Computing.

[18]  Leslie G. Portney Dpt PhD Fapta,et al.  Foundations of Clinical Research: Applications to Practice , 2015 .

[19]  Lara Allet,et al.  Wearable Systems for Monitoring Mobility-Related Activities in Chronic Disease: A Systematic Review , 2010, Sensors.

[20]  Peter Wolf,et al.  Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on Challenging Surfaces , 2012, Journal of aging research.

[21]  Peter H. Veltink,et al.  Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation , 2009, Medical & Biological Engineering & Computing.

[22]  E. D. de Bruin,et al.  Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. , 2009, Gait & posture.

[23]  J. Kavanagh,et al.  Coordination of head and trunk accelerations during walking , 2005, European Journal of Applied Physiology.

[24]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[25]  Steven Morrison,et al.  Age-related differences in head and trunk coordination during walking. , 2005, Human movement science.

[26]  Peter H Veltink,et al.  Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. , 2002, Journal of biomechanics.

[27]  R. Fitzpatrick,et al.  Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. , 2003, Gait & posture.

[28]  Eling D de Bruin,et al.  Reproducibility of spatio-temporal gait parameters under different conditions in older adults using a trunk tri-axial accelerometer system. , 2009, Gait & posture.

[29]  Hylton B Menz,et al.  Accelerometry: a technique for quantifying movement patterns during walking. , 2008, Gait & posture.

[30]  Steven Morrison,et al.  Reliability of segmental accelerations measured using a new wireless gait analysis system. , 2006, Journal of biomechanics.

[31]  R. Fitzpatrick,et al.  Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. , 2003, The journals of gerontology. Series A, Biological sciences and medical sciences.

[32]  R. Moe-Nilssen Test-retest reliability of trunk accelerometry during standing and walking. , 1998, Archives of physical medicine and rehabilitation.

[33]  R S Barrett,et al.  Upper body accelerations during walking in healthy young and elderly men. , 2004, Gait & posture.

[34]  R. Moe-Nilssen,et al.  Test-retest reliability of trunk accelerometric gait analysis. , 2004, Gait & posture.

[35]  Jaap H van Dieën,et al.  Local dynamic stability and variability of gait are associated with fall history in elderly subjects. , 2012, Gait & posture.

[36]  R. Fitzpatrick,et al.  Age-related differences in walking stability. , 2003, Age and ageing.

[37]  G. Kerr,et al.  Falls in Parkinson's disease: evidence for altered stepping strategies on compliant surfaces. , 2011, Parkinsonism & related disorders.

[38]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[39]  Vincent Bonnet,et al.  Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data , 2013, Journal of NeuroEngineering and Rehabilitation.