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Improving Automatic Tremor and Movement Motor Disorder Severity Assessment for Parkinson's Disease with Deep Joint Training
Abstract
Parkinson's disease (PD) is one of the most severe and common disease globally. PD induces motor system impairment causing symptoms such as shaking, rigidity, slowness of movement, body tremor and difficulty with walking. Clinically, accurately and objectively assessing the severity of PD symptoms is critical in controlling appropriate dosage of Levodopa to prevent unwanted side effect of switching between Dyskinesia and PD. The unified Parkinson's disease rating scale published by the Movement Disorder Society (MDS-UPDRS) is an validated instrument regularly administrated by trained physician to assess the severity of a PD patient's motor disorder. In this work, we aim at advancing vision-based automatic motor disorder assessment, specifically hand tremor and movement, for PD patients during UPDRS. Our proposed method leverages information across the two behavior tasks simultaneously via deep joint training to improve each single task's, i.e., tremor and movement, severity classification rate. We evaluate our framework on a large cohort of 106 PD patients, and with our proposed deep joint training framework, we achieve accuracy of 78.01% and 80.60% in right and left hand movement binary classification; in terms of tremor severity classification, our approach obtains an enhanced recognition rates of 72.20% and 71.10% for right and left hand respectively.
Figures
OpenPose tool kit is used to to extract key points from hand, each hand is then encoded using task-level encoding method.
OpenPose tool kit is used to to extract key points from hand, each hand is then encoded using task-level encoding method.
Histograms on distribution of Tremor behaviors to Movement label or vice versa for normal (blue colored) vs. abnormal (orange colored).
Histograms on distribution of Tremor behaviors to Movement label or vice versa for normal (blue colored) vs. abnormal (orange colored).
Keywords
diseases | gait analysis | learning (artificial intelligence) | medical computing | medical disorders
Authors
Chun-Min Chang Jui-Cheng Chen Chi-Chun Lee
Publication Date
2019/07/23
Conference
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI
10.1109/embc.2019.8857472
Publisher
IEEE