Detection of Freezing of Gait using Convolutional Neural Networks and
Data from Lower Limb Motion Sensors
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%.