Literature DB >> 28408157

Microsoft Kinect can distinguish differences in over-ground gait between older persons with and without Parkinson's disease.

Moataz Eltoukhy1, Christopher Kuenze2, Jeonghoon Oh3, Marco Jacopetti4, Savannah Wooten5, Joseph Signorile6.   

Abstract

Gait patterns differ between healthy elders and those with Parkinson's disease (PD). A simple, low-cost clinical tool that can evaluate kinematic differences between these populations would be invaluable diagnostically; since gait analysis in a clinical setting is impractical due to cost and technical expertise. This study investigated the between group differences between the Kinect and a 3D movement analysis system (BTS) and reported validity and reliability of the Kinect v2 sensor for gait analysis. Nineteen subjects participated, eleven without (C) and eight with PD (PD). Outcome measures included spatiotemporal parameters and kinematics. Ankle range of motion for C was significantly less during ankle swing compared to PD (p=0.04) for the Kinect. Both systems showed significant differences for stride length (BTS (C 1.24±0.16, PD=1.01±0.17, p=0.009), Kinect (C=1.24±0.17, PD=1.00±0.18, p=0.009)), gait velocity (BTS (C=1.06±0.14, PD=0.83±0.15, p=0.01), Kinect (C=1.06±0.15, PD=0.83±0.16, p=0.01)), and swing velocity (BTS (C=2.50±0.27, PD=2.12±0.36, p=0.02), Kinect (C=2.32±0.25, PD=1.95±0.31, p=0.01)) between groups. Agreement (RangeICC =0.93-0.99) and consistency (RangeICC =0.94-0.99) were excellent between systems for stride length, stance duration, swing duration, gait velocity, and swing velocity. The Kinect v2 can was sensitive enough to detect between group differences and consistently produced results similar to the BTS system.
Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Gait analysis; Kinect; Motion capture; Parkinson's disease

Mesh:

Year:  2017        PMID: 28408157     DOI: 10.1016/j.medengphy.2017.03.007

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  21 in total

1.  Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras.

Authors:  Guoliang Luo; Yean Zhu; Rui Wang; Yang Tong; Wei Lu; Haolun Wang
Journal:  Med Biol Eng Comput       Date:  2019-12-18       Impact factor: 2.602

2.  Consensus Paper: Ataxic Gait.

Authors:  Pierre Cabaraux; Sunil K Agrawal; Huaying Cai; Rocco Salvatore Calabro; Carlo Casali; Loic Damm; Sarah Doss; Christophe Habas; Anja K E Horn; Winfried Ilg; Elan D Louis; Hiroshi Mitoma; Vito Monaco; Maria Petracca; Alberto Ranavolo; Ashwini K Rao; Serena Ruggieri; Tommaso Schirinzi; Mariano Serrao; Susanna Summa; Michael Strupp; Olivia Surgent; Matthis Synofzik; Shuai Tao; Hiroo Terasi; Diego Torres-Russotto; Brittany Travers; Jaimie A Roper; Mario Manto
Journal:  Cerebellum       Date:  2022-04-12       Impact factor: 3.847

Review 3.  Brain Monitoring Devices in Neuroscience Clinical Research: The Potential of Remote Monitoring Using Sensors, Wearables, and Mobile Devices.

Authors:  Bill Byrom; Marie McCarthy; Peter Schueler; Willie Muehlhausen
Journal:  Clin Pharmacol Ther       Date:  2018-04-18       Impact factor: 6.875

4.  Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data.

Authors:  Andrea Sabo; Sina Mehdizadeh; Kimberley-Dale Ng; Andrea Iaboni; Babak Taati
Journal:  J Neuroeng Rehabil       Date:  2020-07-14       Impact factor: 4.262

5.  Using Kinect to classify Parkinson's disease stages related to severity of gait impairment.

Authors:  Lacramioara Dranca; Lopez de Abetxuko Ruiz de Mendarozketa; Alfredo Goñi; Arantza Illarramendi; Irene Navalpotro Gomez; Manuel Delgado Alvarado; María Cruz Rodríguez-Oroz
Journal:  BMC Bioinformatics       Date:  2018-12-10       Impact factor: 3.169

6.  Recent advances in rehabilitation for Parkinson's Disease with Exergames: A Systematic Review.

Authors:  Augusto Garcia-Agundez; Ann-Kristin Folkerts; Robert Konrad; Polona Caserman; Thomas Tregel; Mareike Goosses; Stefan Göbel; Elke Kalbe
Journal:  J Neuroeng Rehabil       Date:  2019-01-29       Impact factor: 4.262

7.  Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke.

Authors:  Jorge Latorre; Carolina Colomer; Mariano Alcañiz; Roberto Llorens
Journal:  J Neuroeng Rehabil       Date:  2019-07-26       Impact factor: 4.262

8.  Age Matters: Objective Gait Assessment in Early Parkinson's Disease Using an RGB-D Camera.

Authors:  Beatriz Muñoz Ospina; Jaime Andrés Valderrama Chaparro; Juan David Arango Paredes; Yor Jaggy Castaño Pino; Andrés Navarro; Jorge Luis Orozco
Journal:  Parkinsons Dis       Date:  2019-06-13

9.  Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor.

Authors:  Daphne Geerse; Bert Coolen; Detmar Kolijn; Melvyn Roerdink
Journal:  Sensors (Basel)       Date:  2017-10-10       Impact factor: 3.576

10.  Quantifying Spatiotemporal Gait Parameters with HoloLens in Healthy Adults and People with Parkinson's Disease: Test-Retest Reliability, Concurrent Validity, and Face Validity.

Authors:  Daphne J Geerse; Bert Coolen; Melvyn Roerdink
Journal:  Sensors (Basel)       Date:  2020-06-05       Impact factor: 3.576

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