S
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.
Papers
More filters
Journal ArticleDOI
“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.
Journal ArticleDOI
Assessment of fine motor control in individuals with Parkinson's disease using force tracking with a secondary cognitive task.
Sujata Pradhan,Bambi R. Brewer,George E. Carvell,Patrick J. Sparto,Anthony Delitto,Yoky Matsuoka +5 more
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.
Journal ArticleDOI
Quantifying physical activity in early Parkinson disease using a commercial activity monitor.
Sujata Pradhan,Valerie E. Kelly +1 more
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.
Journal ArticleDOI
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.