A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease.

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson's Disease for Use in Tele-Assessments.

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation-supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution.