A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease
Journal
Advances in Soft Computing
Lecture Notes in Computer Science
ISSN
0302-9743
1611-3349
Date Issued
2020
Author(s)
Miralles-Pechuán, Luis
Type
Resource Types::text::book::book part
Abstract
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. © 2020, Springer Nature Switzerland AG.