Das Samarjit, Trutoiu Laura, Murai Akihiko, Alcindor Dunbar, Oh Michael, De la Torre Fernando, Hodgins Jessica
The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6789-92. doi: 10.1109/IEMBS.2011.6091674.
Recent advancements in the portability and affordability of optical motion capture systems have opened the doors to various clinical applications. In this paper, we look into the potential use of motion capture data for the quantitative analysis of motor symptoms in Parkinson's Disease (PD). The standard of care, human observer-based assessments of the motor symptoms, can be very subjective and are often inadequate for tracking mild symptoms. Motion capture systems, on the other hand, can potentially provide more objective and quantitative assessments. In this pilot study, we perform full-body motion capture of Parkinson's patients with deep brain stimulator off-drugs and with stimulators on and off. Our experimental results indicate that the quantitative measure on spatio-temporal statistics learnt from the motion capture data reveal distinctive differences between mild and severe symptoms. We used a Support Vector Machine (SVM) classifier for discriminating mild vs. severe symptoms with an average accuracy of approximately 90%. Finally, we conclude that motion capture technology could potentially be an accurate, reliable and effective tool for statistical data mining on motor symptoms related to PD. This would enable us to devise more effective ways to track the progression of neurodegenerative movement disorders.
光学动作捕捉系统在便携性和可承受性方面的最新进展为各种临床应用打开了大门。在本文中，我们探讨了动作捕捉数据在帕金森病（PD）运动症状定量分析中的潜在用途。基于人类观察者的运动症状评估作为护理标准，可能非常主观，并且通常不足以跟踪轻微症状。另一方面，动作捕捉系统有可能提供更客观和定量的评估。在这项初步研究中，我们对帕金森病患者在脑深部刺激器关闭药物、刺激器开启和关闭的情况下进行全身动作捕捉。我们的实验结果表明，从动作捕捉数据中学到的时空统计量的定量测量揭示了轻度和重度症状之间的显著差异。我们使用支持向量机（SVM）分类器来区分轻度与重度症状，平均准确率约为90%。最后，我们得出结论，动作捕捉技术有可能成为一种准确、可靠且有效的工具，用于对与帕金森病相关的运动症状进行统计数据挖掘。这将使我们能够设计出更有效的方法来跟踪神经退行性运动障碍的进展。
