Inertial measurement unit compared to an optical motion capturing system in post-stroke individuals with foot-drop syndrome.

BACKGROUND Functional electrical stimulation (FES) can be used for compensation of foot-drop for post-stroke individuals by pre-programmed fixed stimulation; however, this stimulation seems no more effective than mechanical ankle foot orthoses. OBJECTIVE We evaluated the metrological quality of inertial sensors for movement reconstruction as compared with the gold-standard motion capturing system, to couple FES with inertial sensors to improve dorsiflexion on the paretic side, by using an adaptive stimulation taking into account individuals' performance post-stroke. METHODS Adults with ischemic or hemorrhagic stroke presenting foot-drop and able to walk 10m, were included from May 2016 to June 2017. Those with passive ankle dorsiflexion<0° with the knee stretched were excluded. Synchronous gait was analyzed with the VICON© system as the gold standard and inertial measurement units (IMUs) worn by participants. The main outcome was the dorsiflexion angle at the heel strike and mid-swing phase obtained from IMUs and the VICON system. Secondary outcomes were: stride length, walking speed, maximal ankle dorsiflexion velocity and fatigue detection. RESULTS We included 26 participants [18 males; mean age 58 (range 45-84) years]. During heel strike, the dorsiflexion angle measurements demonstrated a root mean square error (RMSE) of 5.5°; a mean average error (MAE) of 3.9°; Bland-Altman bias of -0.1° with limits of agreement -10.9° to+10.7° and good intra-class correlation coefficient (ICC) at 0.87 between the 2 techniques. During the mid-swing phase, the RMSE was 5.6; MAE 3.7°; Bland-Altman bias -0.9° with limits of agreement -11.7° to+9.8° and ICC 0.88. Good agreement was demonstrated for secondary outcomes and fatigue detection. CONCLUSIONS IMU-based reconstruction algorithms were effective in measuring ankle dorsiflexion with small biases and good ICCs in adults with ischemic or hemorrhagic stroke presenting foot-drop. The precision obtained is sufficient to observe the fatigue influence on the dorsiflexion and therefore to use IMUs to adapt FES.

[1]  J Y Goulermas,et al.  Predicting lower limb joint kinematics using wearable motion sensors. , 2008, Gait & posture.

[2]  Brian J Hafner,et al.  Effect of a Foot-Drop Stimulator and Ankle–Foot Orthosis on Walking Performance After Stroke , 2013, Neurorehabilitation and neural repair.

[3]  Eric Chalmers,et al.  Inertial sensing algorithms for long-term foot angle monitoring for assessment of idiopathic toe-walking. , 2014, Gait & posture.

[4]  V. Feigin,et al.  Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century , 2003, The Lancet Neurology.

[5]  David Andreu,et al.  Effects of Sensitive Electrical Stimulation‐Based Somatosensory Cueing in Parkinson's Disease Gait and Freezing of Gait Assessment , 2017, Artificial organs.

[6]  Winfried Ilg,et al.  Validation of enhanced kinect sensor based motion capturing for gait assessment , 2017, PloS one.

[7]  Roger Pissard-Gibollet,et al.  Continuous gait cycle index estimation for electrical stimulation assisted foot drop correction , 2014, Journal of NeuroEngineering and Rehabilitation.

[8]  Christine Azevedo Coste,et al.  Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease , 2015 .

[9]  Thomas Seel,et al.  The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait. , 2016, Medical engineering & physics.

[10]  Katja D. Mombaur,et al.  Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data , 2015, Sensors.

[11]  David Guiraud,et al.  A sensor fusion approach for inertial sensors based 3D kinematics and pathological gait assessments: toward an adaptive control of stimulation in post-stroke subjects* , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  A. Kralj,et al.  Enhancement of hemiplegic patient rehabilitation by means of functional electrical stimulation , 1993, Prosthetics and orthotics international.

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

[14]  Cem Ersoy,et al.  Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders , 2017, Sensors.

[15]  Steven A Kautz,et al.  The Effects of Peroneal Nerve Functional Electrical Stimulation Versus Ankle-Foot Orthosis in Patients With Chronic Stroke , 2014, Neurorehabilitation and neural repair.

[16]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[17]  Vivian Weerdesteyn,et al.  Is transcutaneous peroneal stimulation beneficial to patients with chronic stroke using an ankle-foot orthosis? A within-subjects study of patients' satisfaction, walking speed and physical activity level. , 2010, Journal of rehabilitation medicine.

[18]  Ginny M. Sliman.  Nanoscale Materials: What They Could Do for Sensing Technology , 2006 .

[19]  Daniel Hamacher,et al.  Towards clinical application: repetitive sensor position re-calibration for improved reliability of gait parameters. , 2014, Gait & posture.

[20]  Philippe Martin,et al.  Design and implementation of a low-cost observer-based attitude and heading reference system , 2010 .

[21]  Kari Dunning,et al.  Foot Drop Stimulation Versus Ankle Foot Orthosis After Stroke: 30-Week Outcomes , 2013, Stroke.

[22]  J. Patrick,et al.  Gait analysis to assist walking after stroke , 2007, The Lancet.

[23]  Katja D. Mombaur,et al.  Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[25]  Julius Hannink,et al.  Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters , 2017, Sensors.

[26]  Katja Orlowski,et al.  Examination of the reliability of an inertial sensor-based gait analysis system , 2017, Biomedizinische Technik. Biomedical engineering.

[27]  C. Sudlow,et al.  Comparable studies of the incidence of stroke and its pathological types: results from an international collaboration. International Stroke Incidence Collaboration. , 1997, Stroke.

[28]  Corina Nüesch,et al.  Mobile inertial sensor based gait analysis: Validity and reliability of spatiotemporal gait characteristics in healthy seniors. , 2016, Gait & posture.

[29]  Jeffrey M. Hausdorff,et al.  Neuroprosthesis for footdrop compared with an ankle-foot orthosis: effects on postural control during walking. , 2009, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[30]  T. Olsen,et al.  Recovery of walking function in stroke patients: the Copenhagen Stroke Study. , 1995, Archives of physical medicine and rehabilitation.

[31]  D. Cicchetti.  Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[32]  J. Harris,et al.  Review of therapeutic electrical stimulation for dorsiflexion assist and orthotic substitution from the American Congress of Rehabilitation Medicine stroke movement interventions subcommittee. , 2014, Archives of physical medicine and rehabilitation.