Back to Search Start Over

Detection of Freezing of Gait Using Convolutional Neural Networks and Data From Lower Limb Motion Sensors.

Authors :
Shi B
Tay A
Au WL
Tan DML
Chia NSY
Yen SC
Source :
IEEE transactions on bio-medical engineering [IEEE Trans Biomed Eng] 2022 Jul; Vol. 69 (7), pp. 2256-2267. Date of Electronic Publication: 2022 Jun 17.
Publication Year :
2022

Abstract

Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.

Details

Language :
English
ISSN :
1558-2531
Volume :
69
Issue :
7
Database :
MEDLINE
Journal :
IEEE transactions on bio-medical engineering
Publication Type :
Academic Journal
Accession number :
34986092
Full Text :
https://doi.org/10.1109/TBME.2022.3140258