Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability. In addition, class activation map (CAM), a visualization technique for CNNs, is applied for providing an interpretation of the results.
@article{arxiv.1808.02870,
title = {Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization},
author = {Terry Taewoong Um and Franz Michael Josef Pfister and Daniel Christian Pichler and Satoshi Endo and Muriel Lang and Sandra Hirche and Urban Fietzek and Dana Kulić},
journal= {arXiv preprint arXiv:1808.02870},
year = {2018}
}
Comments
This is a pre-print of an article published in Annals of Biomedical Engineering (ABME)