A Survey on Freezing of Gait Detection and Prediction in Parkinson's Disease

被引：1

作者：

Martinez-Villasenor, Lourdes ^{[1
]}

Ponce, Hiram ^{[1
]}

Miralles-Pechuan, Luis ^{[2
]}

机构：

[1] Univ Panamer, Fac Ingn, Augusto Rodin 498, Ciudad De Mexico 03920, Mexico

[2] Univ Coll Dublin, Irelands Ctr Appl Artificial Intelligence CeADAR, Dublin, Ireland

来源：

ADVANCES IN SOFT COMPUTING, MICAI 2020, PT I | 2020年 / 12468卷

关键词：

Freezing of gait; FOG detection; FOG prediction; Parkinson; Machine learning; FALLS;

D O I：

10.1007/978-3-030-60884-2_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most of Parkinson's disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems.

引用

页码：169 / 181

页数：13

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