Bringing spatiotemporal gait analysis into clinical practice: Instrument validation and pilot study of a commercial sensorized carpet.


Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
May 2020
Historique:
received: 26 07 2019
revised: 01 12 2019
accepted: 20 12 2019
pubmed: 11 1 2020
medline: 9 2 2021
entrez: 11 1 2020
Statut: ppublish

Résumé

We illustrate a low-cost platform easing the estimation of spatio-temporal parameters (GA-STP) ready for large-scale deployment in fall prevention. We used a commercial sensorized carpet with a limited cost and a very coarse resolution. An instrument validation test was accomplished: the Wilcoxon test for paired samples and the correlation test with Spearman method were used to compare the values computed by the platform with reference ones. Hierarchical clustering using Ward's method and ROC curves have been used to assess the performance in a pilot study enrolling patients. Validation shows no significant difference between computed and reference values of gait speed (ρ-value:0.99; p-value:2.2E-16), step number (ρ-value:0.91; p-value:5.8E-16) and stride-length (ρ-value:0.92; p-value:7.5E-9). The clinical pilot study confirms that the platform may also be used to track the improvements occurring during a rehabilitation program. We believe that the use of a commercial carpet makes the solution proposed ready to be deployed on a large scale for effectively bringing GA into the clinical practice.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
We illustrate a low-cost platform easing the estimation of spatio-temporal parameters (GA-STP) ready for large-scale deployment in fall prevention.
METHODS METHODS
We used a commercial sensorized carpet with a limited cost and a very coarse resolution. An instrument validation test was accomplished: the Wilcoxon test for paired samples and the correlation test with Spearman method were used to compare the values computed by the platform with reference ones. Hierarchical clustering using Ward's method and ROC curves have been used to assess the performance in a pilot study enrolling patients.
RESULTS RESULTS
Validation shows no significant difference between computed and reference values of gait speed (ρ-value:0.99; p-value:2.2E-16), step number (ρ-value:0.91; p-value:5.8E-16) and stride-length (ρ-value:0.92; p-value:7.5E-9). The clinical pilot study confirms that the platform may also be used to track the improvements occurring during a rehabilitation program.
CONCLUSIONS CONCLUSIONS
We believe that the use of a commercial carpet makes the solution proposed ready to be deployed on a large scale for effectively bringing GA into the clinical practice.

Identifiants

pubmed: 31923818
pii: S0169-2607(19)31165-4
doi: 10.1016/j.cmpb.2019.105292
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105292

Informations de copyright

Copyright © 2019. Published by Elsevier B.V.

Auteurs

Giordano Lanzola (G)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy. Electronic address: giordano.lanzola@unipv.it.

Riccardo Bagarotti (R)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Lucia Sacchi (L)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Elisa Salvi (E)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Anna Alloni (A)

Biomeris Srl, Pavia, Italy.

Michela Picardi (M)

Department of Neurorehabilitation Sciences, Casa di Cura del Policlinico (CCP), Milano, Italy.

Irma Sterpi (I)

Department of Neurorehabilitation Sciences, Casa di Cura del Policlinico (CCP), Milano, Italy.

Rosangela Boninsegna (R)

Department of Neurorehabilitation Sciences, Casa di Cura del Policlinico (CCP), Milano, Italy.

Massimo Corbo (M)

Department of Neurorehabilitation Sciences, Casa di Cura del Policlinico (CCP), Milano, Italy.

Silvana Quaglini (S)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

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