Literature DB >> 29300700

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis.

Sriram Raju Dandu, Matthew M Engelhard, Asma Qureshi, Jiaqi Gong, John C Lach, Maite Brandt-Pearce, Myla D Goldman.   

Abstract

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.

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Year:  2018        PMID: 29300700      PMCID: PMC5774022          DOI: 10.1109/JBHI.2017.2773629

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  14 in total

1.  Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty.

Authors:  K Aminian; K Rezakhanlou; E De Andres; C Fritsch; P F Leyvraz; P Robert
Journal:  Med Biol Eng Comput       Date:  1999-11       Impact factor: 2.602

2.  Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients.

Authors:  Claas Ahlrichs; Albert Samà; Michael Lawo; Joan Cabestany; Daniel Rodríguez-Martín; Carlos Pérez-López; Dean Sweeney; Leo R Quinlan; Gearòid Ò Laighin; Timothy Counihan; Patrick Browne; Lewy Hadas; Gabriel Vainstein; Alberto Costa; Roberta Annicchiarico; Sheila Alcaine; Berta Mestre; Paola Quispe; Àngels Bayes; Alejandro Rodríguez-Molinero
Journal:  Med Biol Eng Comput       Date:  2015-10-01       Impact factor: 2.602

3.  Six-minute walk distance as a measure of functional exercise capacity in multiple sclerosis.

Authors:  Sema Savci; Deniz Inal-Ince; Hulya Arikan; Arzu Guclu-Gunduz; Nilufer Cetisli-Korkmaz; Kadriye Armutlu; Rana Karabudak
Journal:  Disabil Rehabil       Date:  2005-11-30       Impact factor: 3.033

4.  A phase 3 trial of extended release oral dalfampridine in multiple sclerosis.

Authors:  Andrew D Goodman; Theodore R Brown; Keith R Edwards; Lauren B Krupp; Randall T Schapiro; Ron Cohen; Lawrence N Marinucci; Andrew R Blight
Journal:  Ann Neurol       Date:  2010-10       Impact factor: 10.422

5.  Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement.

Authors:  Jiaqi Gong; Yanjun Qi; Myla D Goldman; John Lach
Journal:  IEEE J Biomed Health Inform       Date:  2016-07-09       Impact factor: 5.772

6.  Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects.

Authors:  Matthew M Engelhard; Sriram Raju Dandu; Stephen D Patek; John C Lach; Myla D Goldman
Journal:  Gait Posture       Date:  2016-07-27       Impact factor: 2.840

7.  Walking is more like catching than tapping: gait in the elderly as a complex cognitive task.

Authors:  Jeffrey M Hausdorff; Galit Yogev; Shmuel Springer; Ely S Simon; Nir Giladi
Journal:  Exp Brain Res       Date:  2005-04-28       Impact factor: 1.972

8.  Clinical assessment of gait in individuals with multiple sclerosis using wearable inertial sensors: Comparison with patient-based measure.

Authors:  Massimiliano Pau; Silvia Caggiari; Alessandro Mura; Federica Corona; Bruno Leban; Giancarlo Coghe; Lorena Lorefice; Maria Giovanna Marrosu; Eleonora Cocco
Journal:  Mult Scler Relat Disord       Date:  2016-10-27       Impact factor: 4.339

9.  Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).

Authors:  J F Kurtzke
Journal:  Neurology       Date:  1983-11       Impact factor: 9.910

10.  Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria.

Authors:  Chris H Polman; Stephen C Reingold; Brenda Banwell; Michel Clanet; Jeffrey A Cohen; Massimo Filippi; Kazuo Fujihara; Eva Havrdova; Michael Hutchinson; Ludwig Kappos; Fred D Lublin; Xavier Montalban; Paul O'Connor; Magnhild Sandberg-Wollheim; Alan J Thompson; Emmanuelle Waubant; Brian Weinshenker; Jerry S Wolinsky
Journal:  Ann Neurol       Date:  2011-02       Impact factor: 10.422

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  5 in total

1.  Using Body-Worn Sensors to Detect Changes in Balance and Mobility After Acute Aerobic Exercise in Adults with Multiple Sclerosis.

Authors:  Susan L Kasser; Jesse V Jacobs; Jeremy Sibold; Avery Marcus; Laurel Cole
Journal:  Int J MS Care       Date:  2020 Jan-Feb

2.  Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review.

Authors:  Fabio Alexander Storm; Ambra Cesareo; Gianluigi Reni; Emilia Biffi
Journal:  Sensors (Basel)       Date:  2020-05-06       Impact factor: 3.576

3.  Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds.

Authors:  Charlotte Werner; Chris Awai Easthope; Armin Curt; László Demkó
Journal:  Sensors (Basel)       Date:  2021-11-06       Impact factor: 3.576

4.  Body fat prediction through feature extraction based on anthropometric and laboratory measurements.

Authors:  Zongwen Fan; Raymond Chiong; Zhongyi Hu; Farshid Keivanian; Fabian Chiong
Journal:  PLoS One       Date:  2022-02-22       Impact factor: 3.240

5.  The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice.

Authors:  Katrin Trentzsch; Marie Luise Weidemann; Charlotte Torp; Hernan Inojosa; Maria Scholz; Rocco Haase; Dirk Schriefer; Katja Akgün; Tjalf Ziemssen
Journal:  Front Neurosci       Date:  2020-10-26       Impact factor: 4.677

  5 in total

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