Literature DB >> 29940494

Gait evaluation using inertial measurement units in subjects with Parkinson's disease.

Matteo Zago1, Chiarella Sforza2, Ilaria Pacifici3, Veronica Cimolin4, Filippo Camerota5, Claudia Celletti5, Claudia Condoluci6, Maria Francesca De Pandis7, Manuela Galli8.   

Abstract

We investigated whether a wearable system based on a commercial Inertial Measurement Unit (IMU) can reliably provide the main spatiotemporal gait parameters in subjects with Parkinson's disease (PD), compared to a gold-standard optoelectronic motion capture system. The gait of 22 subjects with PD (Age: 69.4 (6.1) years; UPDRS-III: 28.0 (9.2)) was recorded simultaneously with an optoelectronic system and a commercial IMU-based wearable system. Eight spatiotemporal parameters describing the step cycle (cadence, velocity, stride length, stride duration, step length, stance, swing and double support duration) were compared between the two systems. The IMU and the optical system reported comparable gait parameters, with the exception of walking velocity (optical system, 0.72 (0.27) m∙s-1 vs. IMU: 0.86 (0.26) m∙s-1, p < 0.05). Although most parameters detected by the two systems were not statistically different, some of them like stride length, double support and step duration showed notable root mean square and mean absolute errors. In conclusion, the algorithm embedded in the current release of the commercial IMU requires further improvements to be properly used with subjects with PD. Overall, the IMU system was sufficiently accurate in the assessment of fundamental gait spatiotemporal parameters. The fast and simplified data recording process allowed by wearables makes this technology appealing and represents a possible solution for the quantification of gait in the clinical context, especially when using a traditional 3D optoelectronic gait analysis is not possible, and when subjects are not fully cooperative.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Gait analysis; Gait parameters; IMU; Optoelectronic motion capture; Wearables

Mesh:

Year:  2018        PMID: 29940494     DOI: 10.1016/j.jelekin.2018.06.009

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  7 in total

Review 1.  Gait metrics analysis utilizing single-point inertial measurement units: a systematic review.

Authors:  Ralph Jasper Mobbs; Jordan Perring; Suresh Mahendra Raj; Monish Maharaj; Nicole Kah Mun Yoong; Luke Wicent Sy; Rannulu Dineth Fonseka; Pragadesh Natarajan; Wen Jie Choy
Journal:  Mhealth       Date:  2022-01-20

2.  Agreement between the GAITRite® System and the Wearable Sensor BTS G-Walk® for measurement of gait parameters in healthy adults and Parkinson's disease patients.

Authors:  Slávka Vítečková; Hana Horáková; Kamila Poláková; Radim Krupička; Evžen Růžička; Hana Brožová
Journal:  PeerJ       Date:  2020-05-22       Impact factor: 2.984

3.  Functional Balance and Gait Characteristics in Men With Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia.

Authors:  Emad Al-Yahya; Maha T Mohammad; Jennifer Muhaidat; Saddam Al Demour; Dania Qutishat; Lara Al-Khlaifat; Rasha Okasheh; Sophie Lawrie; Patrick Esser; Helen Dawes
Journal:  Am J Mens Health       Date:  2019 May-Jun

4.  Fuzzy Protoform for Hyperactive Behaviour Detection Based on Commercial Devices.

Authors:  Antonio-Pedro Albín-Rodríguez; Adrián-Jesús Ricoy-Cano; Yolanda-María de-la-Fuente-Robles; Macarena Espinilla-Estévez
Journal:  Int J Environ Res Public Health       Date:  2020-09-16       Impact factor: 3.390

5.  A Comparative Analysis of Shoes Designed for Subjects with Obesity Using a Single Inertial Sensor: Preliminary Results.

Authors:  Veronica Cimolin; Michele Gobbi; Camillo Buratto; Samuele Ferraro; Andrea Fumagalli; Manuela Galli; Paolo Capodaglio
Journal:  Sensors (Basel)       Date:  2022-01-20       Impact factor: 3.576

6.  Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.

Authors:  Sanghee Moon; Hyun-Je Song; Vibhash D Sharma; Kelly E Lyons; Rajesh Pahwa; Abiodun E Akinwuntan; Hannes Devos
Journal:  J Neuroeng Rehabil       Date:  2020-09-11       Impact factor: 4.262

7.  Split-Belt Training but Not Cerebellar Anodal tDCS Improves Stability Control and Reduces Risk of Fall in Patients with Multiple Sclerosis.

Authors:  Carine Nguemeni; Shawn Hiew; Stefanie Kögler; György A Homola; Jens Volkmann; Daniel Zeller
Journal:  Brain Sci       Date:  2021-12-31
  7 in total

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