Ambulatory assessment of walking balance after stroke using instrumented shoes

BackgroundFor optimal guidance of walking rehabilitation therapy of stroke patients in an in-home setting, a small and easy to use wearable system is needed. In this paper we present a new shoe-integrated system that quantifies walking balance during activities of daily living and is not restricted to a lab environment. Quantitative parameters were related to clinically assessed level of balance in order to assess the additional information they provide.MethodsData of 13 participants who suffered a stroke were recorded while walking 10 meter trials and wearing special instrumented shoes. The data from 3D force and torque sensors, 3D inertial sensors and ultrasound transducers were fused to estimate 3D (relative) position, velocity, orientation and ground reaction force of each foot. From these estimates, center of mass and base of support were derived together with a dynamic stability margin, which is the (velocity) extrapolated center of mass with respect to the front-line of the base of support in walking direction. Additionally, for each participant step lengths and stance times for both sides as well as asymmetries of these parameters were derived.ResultsUsing the proposed shoe-integrated system, a complete reconstruction of the kinematics and kinetics of both feet during walking can be made. Dynamic stability margin and step length symmetry were not significantly correlated with Berg Balance Scale (BBS) score, but participants with a BBS score below 45 showed a small-positive dynamic stability margin and more asymmetrical step lengths. More affected participants, having a lower BBS score, have a lower walking speed, make smaller steps, longer stance times and have more asymmetrical stance times.ConclusionsThe proposed shoe-integrated system and data analysis methods can be used to quantify daily-life walking performance and walking balance, in an ambulatory setting without the use of a lab restricted system. The presented system provides additional insight about the balance mechanism, via parameters describing walking patterns of an individual subject. This information can be used for patient specific and objective evaluation of walking balance and a better guidance of therapies during the rehabilitation.Trial registrationThe study protocol is a subset of a larger protocol and registered in the Netherlands Trial Registry, number NTR3636.

[1]  Mark Latt,et al.  Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. , 2004, Gait & posture.

[2]  John R. Rebula,et al.  Measurement of foot placement and its variability with inertial sensors. , 2013, Gait & posture.

[3]  Alessandro Tognetti,et al.  Daily-life tele-monitoring of motor performance in stroke survivors , 2014 .

[4]  D. Robertson Body Segment Parameters , 2014 .

[5]  J. Eng,et al.  Clinical measurement of walking balance in people post stroke: a systematic review , 2011, Clinical rehabilitation.

[6]  S. Olney,et al.  Hemiparetic gait following stroke. Part I: Characteristics , 1996 .

[7]  M. Orendurff,et al.  The effect of walking speed on center of mass displacement. , 2004, Journal of rehabilitation research and development.

[8]  Li-Shan Chou,et al.  Center of mass and base of support interaction during gait. , 2011, Gait & posture.

[9]  Tao Liu,et al.  A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability , 2010, Sensors.

[10]  Peter H. Veltink,et al.  Ambulatory Assessment of Ankle and Foot Dynamics , 2007, IEEE Transactions on Biomedical Engineering.

[11]  K. Berg Measuring balance in the elderly: preliminary development of an instrument , 1989 .

[12]  K Aminian,et al.  Ambulatory assessment of 3D ground reaction force using plantar pressure distribution. , 2010, Gait & posture.

[13]  Michael D Lewek,et al.  The relationship between spatiotemporal gait asymmetry and balance in individuals with chronic stroke. , 2014, Journal of applied biomechanics.

[14]  Edwin van Asseldonk,et al.  Ambulatory Estimation of Center of Mass Displacement During Walking , 2009, IEEE Transactions on Biomedical Engineering.

[15]  J. Duysens,et al.  A review of standing balance recovery from stroke. , 2005, Gait & posture.

[16]  Peter H. Veltink,et al.  Ambulatory gait analysis in stroke patients using ultrasound and inertial sensors , 2014 .

[17]  G. Kwakkel,et al.  Understanding the pattern of functional recovery after stroke: facts and theories. , 2004, Restorative neurology and neuroscience.

[18]  Fokke B. van Meulen,et al.  Assessment of Daily-Life Reaching Performance After Stroke , 2014, Annals of Biomedical Engineering.

[19]  P. Tang,et al.  Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke. , 2003, Archives of physical medicine and rehabilitation.

[20]  A L Hof,et al.  The condition for dynamic stability. , 2005, Journal of biomechanics.

[21]  R DRILLIS,et al.  BODY SEGMENT PARAMETERS; A SURVEY OF MEASUREMENT TECHNIQUES. , 1964, Artificial limbs.

[22]  A. Członkowska,et al.  Risk factors for falls in stroke patients during inpatient rehabilitation , 2009, Clinical rehabilitation.

[23]  Peter J Beek,et al.  Stepping strategies used by post-stroke individuals to maintain margins of stability during walking. , 2013, Clinical biomechanics.

[24]  R A Liston,et al.  Reliability and validity of measures obtained from stroke patients using the Balance Master. , 1996, Archives of physical medicine and rehabilitation.

[25]  F Huxham,et al.  Defining spatial parameters for non-linear walking. , 2006, Gait & posture.

[26]  A. Hof Scaling gait data to body size , 1996 .

[27]  A. Mcgregor,et al.  Body-Worn Sensor Design: What Do Patients and Clinicians Want? , 2011, Annals of Biomedical Engineering.

[28]  Peter H. Veltink,et al.  Influence of the instrumented force shoe on gait pattern in patients with osteoarthritis of the knee , 2011, Medical & Biological Engineering & Computing.

[29]  Ching-yi Wu,et al.  Gait Performance with Compensatory Adaptations in Stroke Patients with Different Degrees of Motor Recovery , 2003, American journal of physical medicine & rehabilitation.

[30]  G. Baer,et al.  Achievement of simple mobility milestones after stroke. , 1999, Archives of physical medicine and rehabilitation.

[31]  F C T van der Helm,et al.  Use of pressure insoles to calculate the complete ground reaction forces. , 2004, Journal of biomechanics.

[32]  Isaac Skog,et al.  Zero-Velocity Detection—An Algorithm Evaluation , 2010, IEEE Transactions on Biomedical Engineering.

[33]  E. Gutierrez-Farewik,et al.  Comparison and evaluation of two common methods to measure center of mass displacement in three dimensions during gait. , 2006, Human movement science.

[34]  Kenton Kaufman,et al.  Dynamic stability margin using a marker based system and Tekscan: a comparison of four gait conditions. , 2014, Gait & posture.

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

[36]  R. Tallis,et al.  Balance disability after stroke. , 2006, Physical therapy.

[37]  P. Veltink,et al.  Ambulatory Estimation of Relative Foot Positions by Fusing Ultrasound and Inertial Sensor Data , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Gijsbertus J.M. Krijnen,et al.  Miniature large range multi-axis force-torque sensor for biomechanical applications , 2015 .

[39]  Hermie Hermens,et al.  Ultrasonic range measurements on the human body , 2013, 2013 Seventh International Conference on Sensing Technology (ICST).