Literature DB >> 23292821

Quantification of motor impairment in Parkinson's disease using an instrumented timed up and go test.

Luca Palmerini1, Sabato Mellone, Guido Avanzolini, Franco Valzania, Lorenzo Chiari.   

Abstract

The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.

Entities:  

Mesh:

Year:  2013        PMID: 23292821     DOI: 10.1109/TNSRE.2012.2236577

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  37 in total

Review 1.  Potential of APDM mobility lab for the monitoring of the progression of Parkinson's disease.

Authors:  Martina Mancini; Fay B Horak
Journal:  Expert Rev Med Devices       Date:  2016-05       Impact factor: 3.166

2.  Instrumenting the balance error scoring system for use with patients reporting persistent balance problems after mild traumatic brain injury.

Authors:  Laurie A King; Fay B Horak; Martina Mancini; Donald Pierce; Kelsey C Priest; James Chesnutt; Patrick Sullivan; Julie C Chapman
Journal:  Arch Phys Med Rehabil       Date:  2013-11-05       Impact factor: 3.966

3.  Role of body-worn movement monitor technology for balance and gait rehabilitation.

Authors:  Fay Horak; Laurie King; Martina Mancini
Journal:  Phys Ther       Date:  2014-12-11

Review 4.  Toward Automating Clinical Assessments: A Survey of the Timed Up and Go.

Authors:  Gina Sprint; Diane J Cook; Douglas L Weeks
Journal:  IEEE Rev Biomed Eng       Date:  2015-01-12

Review 5.  Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Authors:  Eni Halilaj; Apoorva Rajagopal; Madalina Fiterau; Jennifer L Hicks; Trevor J Hastie; Scott L Delp
Journal:  J Biomech       Date:  2018-09-13       Impact factor: 2.712

6.  Transition Between the Timed up and Go Turn to Sit Subtasks: Is Timing Everything?

Authors:  Aner Weiss; Anat Mirelman; Nir Giladi; Lisa L Barnes; David A Bennett; Aron S Buchman; Jeffrey M Hausdorff
Journal:  J Am Med Dir Assoc       Date:  2016-09-01       Impact factor: 4.669

7.  Identifying axial and cognitive correlates in patients with Parkinson's disease motor subtype using the instrumented Timed Up and Go.

Authors:  Talia Herman; Aner Weiss; Marina Brozgol; Nir Giladi; Jeffrey M Hausdorff
Journal:  Exp Brain Res       Date:  2013-11-30       Impact factor: 1.972

Review 8.  Wearable sensor-based objective assessment of motor symptoms in Parkinson's disease.

Authors:  Christiana Ossig; Angelo Antonini; Carsten Buhmann; Joseph Classen; Ilona Csoti; Björn Falkenburger; Michael Schwarz; Jürgen Winkler; Alexander Storch
Journal:  J Neural Transm (Vienna)       Date:  2015-08-08       Impact factor: 3.575

Review 9.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Front Neurosci       Date:  2017-10-06       Impact factor: 4.677

10.  Bio-inspired dimensionality reduction for Parkinson's disease (PD) classification.

Authors:  Akram Pasha; P H Latha
Journal:  Health Inf Sci Syst       Date:  2020-03-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.