Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease.
Detection
Freezing of gait
Parkinson’s disease
Prediction
Wearable sensors
Journal
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
ISSN: 1590-3478
Titre abrégé: Neurol Sci
Pays: Italy
ID NLM: 100959175
Informations de publication
Date de publication:
16 Oct 2023
16 Oct 2023
Historique:
received:
11
02
2023
accepted:
06
08
2023
pubmed:
16
10
2023
medline:
16
10
2023
entrez:
16
10
2023
Statut:
aheadofprint
Résumé
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
Identifiants
pubmed: 37843692
doi: 10.1007/s10072-023-07017-y
pii: 10.1007/s10072-023-07017-y
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. Fondazione Società Italiana di Neurologia.
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