Classification of freezing of gait using accelerometer data: A systematic performance evaluation approach

Parkinson's disease is one of the most common neurodegenerative chronic diseases which can affect the patient's quality of life by creating several motor and non-motor impairments. The freezing of gait is one such motor impairment which can cause the inability to move forward despite the intention to walk. The identification of the freezing-of-gait events using sensor technology and machine-learning algorithms can result in an improvement in the quality of life and can decrease the risk of fall in Parkinson's patients. Our study focuses on a systematic performance evaluation of machine learning algorithms for developing a good fit and generalized model. In this work, we train time-domain and frequency-domain-transform-based features on fully connected artificial and deep neural network algorithm for classifying the events of freezing of gait in patients by using accelerometer data. We evaluate these algorithms for hyperparameters such as batch size, optimizer type, and window sizes in a step-wise process. We identify an optimal combination of parameters according to the accuracy and model fit optimality metrics, for artificial and deep neural network to classify freezing of gait events in Parkinson's patients. We were able to achieve classification accuracy of - with Adam optimizer, batch sizes (BS) of 256 and 8 and epochs of 60 and 40 for ANN and DNN respectively.