V
Viprali Bhatkar
Publications - 4
Citations - 52
Viprali Bhatkar is an academic researcher. The author has contributed to research in topics: Internal medicine & Visual analogue scale. The author has an hindex of 2, co-authored 3 publications receiving 24 citations.
Papers
More filters
Journal ArticleDOI
A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments.
Ellora Sen-Gupta,Donald E. Wright,James W. Caccese,John A. Wright,Elise Jortberg,Viprali Bhatkar,Melissa Ceruolo,Roozbeh Ghaffari,Dennis L. Clason,James P. Maynard,Arthur H. Combs +10 more
TL;DR: The present study validated the BioStamp nPoint system’s performance and ease of use compared to FDA-cleared comparator devices in both the clinic and remote (home) environments.
Journal ArticleDOI
A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography.
Elise Jortberg,Ikaro Silva,Viprali Bhatkar,Ryan S. McGinnis,Ellora Sen-Gupta,Briana Morey,John A. Wright,Jesus Pindado,Matt T. Bianchi +8 more
TL;DR: It is demonstrated that BiostampRC® is a tolerable and accurate method for capturing respiration, ECG, and AT EMG time series signals during overnight sleep when compared with simultaneous PSG recordings.
Journal ArticleDOI
Real-Time Digital Biometric Monitoring during Elite Athletic Competition: System Feasibility with a Wearable Medical-Grade Sensor.
TL;DR: In this paper, the authors compared the performance of the BioStamp nPoint® sensor compared to the Polar chest strap HR sensor in 15 Professional Squash Association (PSA) tournament matches in 2019-2020.
Journal ArticleDOI
Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience
TL;DR: A novel measure that combines the subjectively-identified time of peak pain with capturing continuous physiological data to quantify the sympathetic nervous system response during a dynamic pain experience is proposed and studied, providing better discriminability than using self-reported eVAS.