Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis.

Background Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS. Methods We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m. Results Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure. Conclusions The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.

[1]  Muhammad Moazam Fraz,et al.  Motion Reveal Emotions: Identifying Emotions From Human Walk Using Chest Mounted Smartphone , 2020, IEEE Sensors Journal.

[2]  Xiang Li,et al.  External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems , 2020, Micromachines.

[3]  Cem Ersoy,et al.  How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications , 2020, Sensors.

[4]  P. Aqueveque,et al.  Validation of a portable system for spatial-temporal gait parameters based on a single inertial measurement unit and a mobile application , 2020, European journal of translational myology.

[5]  M. Hunt,et al.  Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis , 2020, Journal of NeuroEngineering and Rehabilitation.

[6]  R. Mobbs,et al.  Analysis of Patterns of Gait Deterioration in Patients with Lumbar Spinal Stenosis , 2020 .

[7]  O. Gerke.  Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews , 2020, Diagnostics.

[8]  Jun Liu,et al.  Freezing of gait in Parkinson’s disease: pathophysiology, risk factors and treatments , 2020, Translational Neurodegeneration.

[9]  R. Mobbs.  Gait velocity (walking speed) is an indicator of spine health, and objective measure of pre and post intervention recovery for spine care providers. , 2020, Journal of spine surgery.

[10]  Sang Seok Yeo,et al.  Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System , 2020, Sensors.

[11]  Mirko Meboldt,et al.  Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments , 2020, Frontiers in Physiology.

[12]  S. Kyeong,et al.  Gait pattern analysis and clinical subgroup identification: a retrospective observational study , 2019, Medicine.

[13]  Shyamal Patel,et al.  GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back , 2019, J. Open Source Softw..

[14]  Monish M. Maharaj,et al.  The role of wearable devices and objective gait analysis for the assessment and monitoring of patients with lumbar spinal stenosis: systematic review , 2019, BMC Musculoskeletal Disorders.

[15]  Tele Tan,et al.  Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques , 2019, Sensors.

[16]  Å. Tornberg,et al.  Coherence and reliability of a wearable inertial measurement unit for measuring postural sway , 2019, BMC Research Notes.

[17]  M. Lee,et al.  Effects of Knee Osteoarthritis on Hip and Ankle Gait Mechanics , 2019, Advances in orthopedics.

[18]  Jeffrey M. Hausdorff,et al.  Analysis of Free-Living Gait in Older Adults With and Without Parkinson’s Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics , 2019, The journals of gerontology. Series A, Biological sciences and medical sciences.

[19]  Luca Palmerini,et al.  Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review , 2019, Diseases.

[20]  R. Mobbs,et al.  Objective monitoring of activity and Gait Velocity using wearable accelerometer following lumbar microdiscectomy to detect recurrent disc herniation. , 2018, Journal of spine surgery.

[21]  Hyeok Dong Lee,et al.  Evaluation of Validity and Reliability of Inertial Measurement Unit-Based Gait Analysis Systems , 2018, Annals of rehabilitation medicine.

[22]  Corina Nüesch,et al.  Decompression surgery improves gait quality in patients with symptomatic lumbar spinal stenosis. , 2018, The spine journal : official journal of the North American Spine Society.

[23]  Hee-Cheol Kim,et al.  A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer , 2018, Sensors.

[24]  Nurettin Özgür Doğan,et al.  Bland-Altman analysis: A paradigm to understand correlation and agreement , 2018, Turkish journal of emergency medicine.

[25]  Y. Ohki,et al.  Hand Dexterity Impairment in Patients with Cervical Myelopathy: A New Quantitative Assessment Using a Natural Prehension Movement , 2018, Behavioural neurology.

[26]  Hossein Rouhani,et al.  Detection of daily postures and walking modalities using a single chest-mounted tri-axial accelerometer. , 2018, Medical engineering & physics.

[27]  B. Zörner,et al.  Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time , 2018, Scientific Reports.

[28]  Jun Sun,et al.  Clinical Gait Evaluation of Patients with Lumbar Spine Stenosis , 2018, Orthopaedic surgery.

[29]  A. Tang,et al.  Validity of the Fitbit One for Measuring Activity in Community-Dwelling Stroke Survivors. , 2017, Physiotherapy Canada. Physiotherapie Canada.

[30]  Çağatay Berke Erdaş,et al.  Parkinson's disease monitoring from gait analysis via foot-worn sensors , 2018 .

[31]  T. Hortobágyi,et al.  Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic , 2017, PloS one.

[32]  P. Adamczyk,et al.  Validity and repeatability of inertial measurement units for measuring gait parameters. , 2017, Gait & posture.

[33]  P. Vidal,et al.  Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges , 2017, Front. Psychol..

[34]  De Wu,et al.  An Acceleration-Based Gait Assessment Method for Children with Cerebral Palsy , 2017, Sensors.

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

[36]  Amir Akramin Shafie,et al.  GAIT ANALYSIS: SYSTEMS, TECHNOLOGIES, AND IMPORTANCE , 2016 .

[37]  Regina Katzenschlager,et al.  Gait disorders in adults and the elderly , 2016, Wiener klinische Wochenschrift.

[38]  Alan H. Daniels,et al.  Differentiating Radiculopathy from Lower Extremity Arthropathy. , 2016, The American journal of medicine.

[39]  Alan Godfrey,et al.  Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age , 2016, Physiological measurement.

[40]  K. Rome,et al.  Effects of foot and ankle devices on balance, gait and falls in adults with sensory perception loss: a systematic review , 2016, JBI database of systematic reviews and implementation reports.

[41]  Jaap H. van Dieën,et al.  Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls , 2016, Journal of NeuroEngineering and Rehabilitation.

[42]  R. Mobbs,et al.  Physical Activity Measured with Accelerometer and Self-Rated Disability in Lumbar Spine Surgery: A Prospective Study , 2015, Global spine journal.

[43]  Robert W Motl,et al.  Stride-Time Variability and Fall Risk in Persons with Multiple Sclerosis , 2015, Multiple sclerosis international.

[44]  Christopher Neville,et al.  Measuring postural stability with an inertial sensor: validity and sensitivity , 2015, Medical devices.

[45]  M. Park,et al.  Lumbar Stenosis: A Recent Update by Review of Literature , 2015, Asian spine journal.

[46]  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.

[47]  B. Najafi,et al.  Gait behaviors as an objective surgical outcome in low back disorders: A systematic review. , 2015, Clinical biomechanics.

[48]  N. Mamizuka,et al.  Gait characterization of pre- and postoperative patients with lumbar spinal stenosis utilizing a body-mounted inertial measurement unit , 2014, 2014 IEEE Healthcare Innovation Conference (HIC).

[49]  M. Neo,et al.  Quantification of Changes in Gait Characteristics Associated With Intermittent Claudication in Patients With Lumbar Spinal Stenosis , 2014, Journal of spinal disorders & techniques.

[50]  Begonya Garcia-Zapirain,et al.  Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications , 2014, Sensors.

[51]  M. Fernández-Del-Olmo,et al.  Spatiotemporal parameters of gait during treadmill and overground walking in Parkinson's disease. , 2014, Journal of Parkinson's disease.

[52]  D. Kennedy,et al.  Associations of self-report measures with gait, range of motion and proprioception in patients with lumbar spinal stenosis. , 2013, Gait & posture.

[53]  Bijan Najafi,et al.  Novel Wearable Technology for Assessing Spontaneous Daily Physical Activity and Risk of Falling in Older Adults with Diabetes , 2013, Journal of diabetes science and technology.

[54]  Lorenzo Chiari,et al.  ISway: a sensitive, valid and reliable measure of postural control , 2012, Journal of NeuroEngineering and Rehabilitation.

[55]  Wiebren Zijlstra,et al.  Subjects with hip osteoarthritis show distinctive patterns of trunk movements during gait-a body-fixed-sensor based analysis , 2012, Journal of NeuroEngineering and Rehabilitation.

[56]  Amir Haim,et al.  Differences in gait patterns, pain, function and quality of life between males and females with knee osteoarthritis: a clinical trial , 2009, BMC musculoskeletal disorders.

[57]  N. Kampanis,et al.  Gait variability measurements in lumbar spinal stenosis patients: part A. Comparison with healthy subjects , 2009, Physiological measurement.

[58]  N. Kampanis,et al.  Gait variability measurements in lumbar spinal stenosis patients: part B. Preoperative versus postoperative gait variability , 2009, Physiological measurement.

[59]  O. Kwon,et al.  Gender differences in three dimensional gait analysis data from 98 healthy Korean adults. , 2004, Clinical biomechanics.

[60]  M. Hasue,et al.  Lumbar spinal stenosis , 1977, International Orthopaedics.

[61]  C R Cantor,et al.  Gait disorders. , 1999, Clinics in podiatric medicine and surgery.