Quantitative assessment of dynamic movement reveals deficits due to hemiparetic stroke
Aleksandra Kalinowska, Millicent Schlafly, Kyra Rudy, Julius P. A. Dewald, Todd D. Murphey
- 发表年份
- 2025
- 引用次数
- 1
- 访问权限
- 开放获取
摘要
The absence of sensitive tools for quantifying movement dysfunction hinders our ability to study the underlying causes of motor impairments and makes it difficult to demonstrate the effectiveness of therapeutic approaches. Consequently, it slows down progress in developing novel treatment protocols, including personalized impairment-targeted interventions. While there exist well-established metrics of static and quasi-static motion, such as reaching range, little emphasis has been placed on quantifying dynamic response-controlled and timing-sensitive movements where the continuous modulation of motor activity is required to respond to real-time stimuli. In this study, we employ robot-assisted virtual tasks that require dynamic motion in the upper limb, and develop metrics that assess dynamic capabilities by quantifying the frequency spectra of movement during these tasks. We assess chronic survivors of hemiparetic stroke across three dynamic tasks (n=13 for the first two tasks and n=48 for the third task). We find that hemiparetic stroke causes a significant decline in dynamic response at frequencies above 1.5Hz in the paretic upper limb, with the degree of functional loss dependent on the clinically assessed severity of motor impairment. Frequency-based metrics of dynamic motion could be used to assess performance during everyday activities, such as brushing teeth, cooking, or cleaning. A versatile quantitative assessment of dynamic motion can accelerate progress in understanding and rehabilitating functional dynamic motion in individuals with neuromotor disorders.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992