A novel method for automatic classification of Parkinson gait severity using front-view video analysis.
Technol Health Care
; 29(4): 643-653, 2021.
Article
em En
| MEDLINE
| ID: mdl-33427697
ABSTRACT
BACKGROUND:
Gait impairment is an essential symptom of Parkinson's disease (PD).OBJECTIVE:
This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis.METHODS:
Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson's disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal. Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation.RESULTS:
Features significantly (p< 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%.CONCLUSION:
Findings support the feasibility of this model for Parkinson's gait assessment in the home environment.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Doença de Parkinson
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Technol Health Care
Assunto da revista:
ENGENHARIA BIOMEDICA
/
SERVICOS DE SAUDE
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Suécia