Testing the Performance of an Innovative Markerless Technique for Quantitative and Qualitative Gait Analysis.

Fast Fourier Transform gait analysis harmony markerless systems treadmill walking

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
20 Nov 2020
Historique:
received: 24 09 2020
revised: 13 11 2020
accepted: 17 11 2020
entrez: 25 11 2020
pubmed: 26 11 2020
medline: 10 4 2021
Statut: epublish

Résumé

Gait abnormalities such as high stride and step frequency/cadence (SF-stride/second, CAD-step/second), stride variability (SV) and low harmony may increase the risk of injuries and be a sentinel of medical conditions. This research aims to present a new markerless video-based technology for quantitative and qualitative gait analysis. 86 healthy individuals (mead age 32 years) performed a 90 s test on treadmill at self-selected walking speed. We measured SF and CAD by a photoelectric sensors system; then, we calculated average ± standard deviation (SD) and within-subject coefficient of variation (CV) of SF as an index of SV. We also recorded a 60 fps video of the patient. With a custom-designed web-based video analysis software, we performed a spectral analysis of the brightness over time for each pixel of the image, that reinstituted the frequency contents of the videos. The two main frequency contents (F1 and F2) from this analysis should reflect the forcing/dominant variables, i.e., SF and CAD. Then, a harmony index (HI) was calculated, that should reflect the proportion of the pixels of the image that move consistently with F1 or its supraharmonics. The higher the HI value, the less variable the gait. The correspondence SF-F1 and CAD-F2 was evaluated with both paired t-Test and correlation and the relationship between SV and HI with correlation. SF and CAD were not significantly different from and highly correlated with F1 (0.893 ± 0.080 Hz vs. 0.895 ± 0.084 Hz,

Identifiants

pubmed: 33233799
pii: s20226654
doi: 10.3390/s20226654
pmc: PMC7699971
pii:
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Laura Simoni (L)

Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy.
IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

Alessandra Scarton (A)

Microgate SRL, 39100 Bolzano, Italy.

Filippo Gerli (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

Claudio Macchi (C)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

Federico Gori (F)

Microgate SRL, 39100 Bolzano, Italy.

Guido Pasquini (G)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

Silvia Pogliaghi (S)

Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy.

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Classifications MeSH