Deep Learning for Medication Assessment of Individuals with Parkinson's Disease Using Wearable Sensors

Motor fluctuations between "OFF" state (with no benefit from medication) and " ON" state (with optimum benefit from medication) are a major focus of clinical managements in individuals with mid-stage and advance Parkinson's disease (PD). In this work, an automated algorithm based on Long Short-Term Memory (LSTM) as a deep learning method is developed to identify motor fluctuations in individuals with PD using wearable sensors during a variety of daily living activities. This network was evaluated on two datasets i.e., Dataset 1 and Dataset 2) that included recordings of 19 individuals with PD using subject-based leave-one-out cross-validation. The designed LSTM network yielded promising results using only one ankle sensor with an average classification rate of 73% and 77% for Dataset 1 and Dataset 2, respectively.