Literature DB >> 35178440

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review.

Ralph Jasper Mobbs1,2,3, Jordan Perring1,2, Suresh Mahendra Raj1, Monish Maharaj1,2, Nicole Kah Mun Yoong1,2, Luke Wicent Sy4, Rannulu Dineth Fonseka1,2, Pragadesh Natarajan1,2, Wen Jie Choy1,2.   

Abstract

BACKGROUND: Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications.
METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.
RESULTS: A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls. DISCUSSION: This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected. 2022 mHealth. All rights reserved.

Entities:  

Keywords:  Accelerometry; gait analysis; wearable electronic devices

Year:  2022        PMID: 35178440      PMCID: PMC8800203          DOI: 10.21037/mhealth-21-17

Source DB:  PubMed          Journal:  Mhealth        ISSN: 2306-9740


  133 in total

1.  Concurrent validity and intrasession reliability of the IDEEA accelerometry system for the quantification of spatiotemporal gait parameters.

Authors:  Nicola A Maffiuletti; Mark Gorelick; Ines Kramers-de Quervain; Mario Bizzini; Jeannette Petrich Munzinger; Samuele Tomasetti; Alex Stacoff
Journal:  Gait Posture       Date:  2007-03-01       Impact factor: 2.840

2.  Concurrent Validity of a Commercial Wireless Trunk Triaxial Accelerometer System for Gait Analysis.

Authors:  Roel De Ridder; Julien Lebleu; Tine Willems; Cedric De Blaiser; Christine Detrembleur; Philip Roosen
Journal:  J Sport Rehabil       Date:  2019-08-01       Impact factor: 1.931

3.  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.

Authors:  Silvia Del Din; Brook Galna; Alan Godfrey; Esther M J Bekkers; Elisa Pelosin; Freek Nieuwhof; Anat Mirelman; Jeffrey M Hausdorff; Lynn Rochester
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2019-03-14       Impact factor: 6.053

4.  Walking speed estimation using a shank-mounted inertial measurement unit.

Authors:  Q Li; M Young; V Naing; J M Donelan
Journal:  J Biomech       Date:  2010-02-24       Impact factor: 2.712

5.  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

6.  Accelerometer-based determination of gait variability in older adults with knee osteoarthritis.

Authors:  Christian A Clermont; John M Barden
Journal:  Gait Posture       Date:  2016-08-24       Impact factor: 2.840

7.  Quantitative gait markers and incident fall risk in older adults.

Authors:  Joe Verghese; Roee Holtzer; Richard B Lipton; Cuiling Wang
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2009-04-06       Impact factor: 6.053

8.  PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

Authors:  Alexandros T Tzallas; Markos G Tsipouras; Georgios Rigas; Dimitrios G Tsalikakis; Evaggelos C Karvounis; Maria Chondrogiorgi; Fotis Psomadellis; Jorge Cancela; Matteo Pastorino; María Teresa Arredondo Waldmeyer; Spiros Konitsiotis; Dimitrios I Fotiadis
Journal:  Sensors (Basel)       Date:  2014-11-11       Impact factor: 3.576

9.  A Wearable Magneto-Inertial System for Gait Analysis (H-Gait): Validation on Normal Weight and Overweight/Obese Young Healthy Adults.

Authors:  Valentina Agostini; Laura Gastaldi; Valeria Rosso; Marco Knaflitz; Shigeru Tadano
Journal:  Sensors (Basel)       Date:  2017-10-21       Impact factor: 3.576

10.  Inertial Sensor-Based Variables Are Indicators of Frailty and Adverse Post-Operative Outcomes in Cardiovascular Disease Patients.

Authors:  Rahul Soangra; Thurmon E Lockhart
Journal:  Sensors (Basel)       Date:  2018-06-02       Impact factor: 3.576

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  1 in total

1.  Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.

Authors:  Dante Trabassi; Mariano Serrao; Tiwana Varrecchia; Alberto Ranavolo; Gianluca Coppola; Roberto De Icco; Cristina Tassorelli; Stefano Filippo Castiglia
Journal:  Sensors (Basel)       Date:  2022-05-12       Impact factor: 3.847

  1 in total

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