Atypical Gait Cycles in Parkinson’s Disease

Author:

Ghislieri MarcoORCID,Agostini ValentinaORCID,Rizzi Laura,Knaflitz MarcoORCID,Lanotte MicheleORCID

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, p = 0.04). However, they showed a significant increase in atypical gait cycles (+42%, p = 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 (r = 0.91, p = 0.002), in the subset of PD patients showing an abnormal number of atypical cycles, while we found a moderate correlation (r = 0.60, p = 0.005), considering the whole PD population. Atypical cycles have proved to be a valid biomarker to quantify subtle gait dysfunctions in PD patients.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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1. Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics;PeerJ Computer Science;2024-05-13

2. Automatic Gait Gender Classification Using Convolutional Neural Networks;Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision;2023-01-13

3. Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

4. A Tool for Home Monitoring in Parkinson's Disease;2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet);2022-12-12

5. U-Turn Detection during Walking;2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA);2022-06-22

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