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Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation

机译:基于视觉的帕金森病和左旋多巴诱发的运动障碍的姿势估计

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摘要

BackgroundDespite the effectiveness of levodopa for treatment of Parkinson’s disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation.
机译:背景技术尽管左旋多巴治疗帕金森氏病(PD)有效,但长期使用会导致运动并发症,尤其是左旋多巴诱发的运动障碍(LID)。 PD患者及其医生必须定期修改治疗方案和时机,以最佳缓解症状。尽管存在用于评估PD症状严重程度的标准化临床评分量表,但它们必须由经过培训的医学专业人员进行管理,并且固有地是主观的。计算机视觉是自动评估PD的一种有吸引力的,非接触式的潜在解决方案,其计算能力和深度学习算法的最新进展使之成为可能。本文的目的是通过姿势估计来评估基于视觉的帕金森病和LID评估的可行性。

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