Convolutional Neural Network for Freezing of Gait Detection Leveraging the Continuous Wavelet Transform on Lower Extremities Wearable Sensors Data.
Annu Int Conf IEEE Eng Med Biol Soc
; 2020: 5410-5415, 2020 07.
Article
in En
| MEDLINE
| ID: mdl-33019204
ABSTRACT
Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Parkinson Disease
/
Gait Disorders, Neurologic
/
Wearable Electronic Devices
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2020
Document type:
Article