Freezing of Gait Detection for Parkinson's Disease Patients using Accelerometer Data: Case Study

Freezing of Gait (FoG) is a common symptom of Parkinson's Disease (PD) and its automatic detection would allow for an improvement of disease tracking and rehabilitation possibilities. In this study, we investigate a deep convolutional neural network for the automatic detection of FoG episodes in PD patients. The Daphnet dataset, containing three 3D accelerometer signals, was used for training and testing the proposed algorithm. Some of the benefits of this approach include: (i) the use of the simple, raw data, for classification; (ii) developing a method which is independent of the input window size. Using a 10-fold cross validation, we achieve a sensitivity and specificity of up to 93.44% and 87.38%, respectively.