Machine learning-based classification of simple drawing movements in Parkinson's disease
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- 作者：C. Kotsavasilogloua ; chkot@cteam.gr" class="auth_mail" title="E-mail the corresponding author ; N. Kostikisb ; nikkostikis@gmail.com" class="auth_mail" title="E-mail the corresponding author ; D. Hristu-Varsakelisb ; dcv@uom.gr" class="auth_mail" title="E-mail the corresponding author ; M. Arnaoutoglouc ; marnaout@med.auth.gr" class="auth_mail" title="E-mail the corresponding author
- 关键词：Movement disorders ; Parkinson&rsquo ; s disease ; Handwriting ; Machine learning ; Normalized velocity
- 刊名：Biomedical Signal Processing and Control
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：31
- 期：Complete
- 页码：174-180
- 全文大小：492 K
文摘
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It is proven that handwriting markers can be used for Parkinson’s Disease symptoms profiling successfully.
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A very simple line drawing task can assess both hands’ flexion and extension, unaffected by dominant hand dexterity.
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A novel metric defined as the normalized velocity variability, is very potent in identifying pathological movement patterns.
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A machine learning model can assess performance in executing an unobtrusive yet information-rich line drawing task.