Freezing of Gait detection in Parkinson's Disease: Comparison of Deep Learning Frameworks

Parkinson's Disease (PD) is a neurodegenerative disorder characterized by various motor symptoms. One of the most prominent is Freezing of Gait (FoG). In this paper, we explore different deep learning frameworks (deep convolutional neural networks and an autoencoder-based network) for the automatic detection of FoG episodes in PD. Our study focuses on 3D accelerometer signals from the Daphnet dataset, which were used for both the training and testing of our models. We propose several robust methods inspired from well-known deep learning frameworks such as AlexNet, VGG and LeNet as well as an algorithm that combines an autoencoder-based feature extraction and classic machine learning classification. All algorithms are independent of the input window size and utilize raw data as input. Individual accelerometer axis as well as the magnitude from sensors placed on the ankle, thigh, and trunk are considered for their potential impact on the classification results. The potential of each individual algorithm to accurately detect FoG episodes is investigated in a 10-fold cross-validation. We achieved the highest accuracy of 90.92% using both an autoencoder and various convolutional neural network architectures applied to the magnitude of the accelerometer signals. For the LeNet-based and VGG-16-based architectures, sensors positioned on the thigh and trunk provide better results, while other deep learning frameworks are sensor-independent.