Atypical Gait Cycles in Parkinson's Disease

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2021 Aug 10

PMID 34372315

Citations 3

Authors

Marco Ghislieri

Valentina Agostini

Laura Rizzi

Marco Knaflitz

Michele Lanotte

Affiliations

Soon will be listed here.

Abstract

It is important to find objective biomarkers for evaluating gait in Parkinson's Disease (PD), especially related to the foot and lower leg segments. Foot-switch signals, analyzed through Statistical Gait Analysis (SGA), allow the foot-floor contact sequence to be characterized during a walking session lasting five-minutes, which includes turnings. Gait parameters were compared between 20 PD patients and 20 age-matched controls. PDs showed similar straight-line speed, cadence, and double-support compared to controls, as well as typical gait-phase durations, except for a small decrease in the flat-foot contact duration (-4% of the gait cycle, = 0.04). However, they showed a significant increase in atypical gait cycles (+42%, = 0.006), during both walking straight and turning. A forefoot strike, instead of a "normal" heel strike, characterized the large majority of PD's atypical cycles, whose total percentage was 25.4% on the most-affected and 15.5% on the least-affected side. Moreover, we found a strong correlation between the atypical cycles and the motor clinical score UPDRS-III ( = 0.91, = 0.002), in the subset of PD patients showing an abnormal number of atypical cycles, while we found a moderate correlation ( = 0.60, = 0.005), considering the whole PD population. Atypical cycles have proved to be a valid biomarker to quantify subtle gait dysfunctions in PD patients.

Citing Articles

Foot-Floor Contact Sequences: A Metric for Gait Assessment in Parkinson's Disease after Deep Brain Stimulation.

Ghislieri M, Agostini V, Rizzi L, Fronda C, Knaflitz M, Lanotte M Sensors (Basel). 2024; 24(20).

PMID: 39460074 PMC: 11510800. DOI: 10.3390/s24206593.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.

Jovanovic L, Damasevicius R, Matic R, Kabiljo M, Simic V, Kunjadic G PeerJ Comput Sci. 2024; 10:e2031.

PMID: 38855236 PMC: 11157549. DOI: 10.7717/peerj-cs.2031.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders.

Salchow-Hommen C, Skrobot M, Jochner M, Schauer T, Kuhn A, Wenger N Front Hum Neurosci. 2022; 16:768575.

PMID: 35185496 PMC: 8850274. DOI: 10.3389/fnhum.2022.768575.

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