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Sujata Pradhan

Researcher at University of Washington

Publications -  18
Citations -  290

Sujata Pradhan is an academic researcher from University of Washington. The author has contributed to research in topics: Medicine & Usability. The author has an hindex of 7, co-authored 16 publications receiving 216 citations. Previous affiliations of Sujata Pradhan include University of Pittsburgh.

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“Kinect-ing” With Clinicians: A Knowledge Translation Resource to Support Decision Making About Video Game Use in Rehabilitation

TL;DR: The development and preliminary evaluation of a knowledge translation (KT) resource to support clinical decision making about selection and use of Kinect games in physical therapy and the process and results of an exploratory usability evaluation of the KWiC resource by clinicians through interviews and focus groups at 4 clinical sites are described.
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Assessment of fine motor control in individuals with Parkinson's disease using force tracking with a secondary cognitive task.

TL;DR: The results of this project will serve as a preliminary work for the development of a clinical biomarker for PD that may help to identify subtle deficits in fine motor control early in the disease process and facilitate tracking of disease progression with time.
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Quantifying physical activity in early Parkinson disease using a commercial activity monitor.

TL;DR: People with mild PD demonstrated reduced quantity and intensity of PA compared to HOA, and both the PD and the HOA groups had good adherence wearing a commercial activity monitor that provided feedback regarding activity levels.
Journal Article

Effects of Parkinson's Disease on Fundamental Frequency Variability in Running Speech.

TL;DR: F0 variability changes over the course of reading a paragraph may not be indicative of PD but rather dependent on non-disease factors such as the linguistic characteristics of the text.
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Application of Modified Regression Techniques to a Quantitative Assessment for the Motor Signs of Parkinson's Disease

TL;DR: This work has used commercially available sensors to create a protocol called Advanced Sensing for Assessment of Parkinson's disease (ASAP) to obtain a quantitative and reliable measure of motor impairment in early to moderate PD and demonstrates that the ASAP protocol can measure differences for individuals who are clinically different.