  One major challenge in the medication of Parkinson's disease is that the
severity of the disease, reflected in the patients' motor state, cannot be
measured using accessible biomarkers. Therefore, we develop and examine a
variety of statistical models to detect the motor state of such patients based
on sensor data from a wearable device. We find that deep learning models
consistently outperform a classical machine learning model applied on
hand-crafted features in this time series classification task. Furthermore, our
results suggest that treating this problem as a regression instead of an
ordinal regression or a classification task is most appropriate. For consistent
model evaluation and training, we adopt the leave-one-subject-out validation
scheme to the training of deep learning models. We also employ a
class-weighting scheme to successfully mitigate the problem of high multi-class
imbalances in this domain. In addition, we propose a customized performance
measure that reflects the requirements of the involved medical staff on the
model. To solve the problem of limited availability of high quality training
data, we propose a transfer learning technique which helps to improve model
performance substantially. Our results suggest that deep learning techniques
offer a high potential to autonomously detect motor states of patients with
Parkinson's disease.
