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Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

The authors would also like to thank Dr Saninder Kaur, Kelsey Greenoe, Ashley Adamczyk, and Ryan Griesenauer for their assistance during participant recruitment and data collection.

Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman

JMIR Mhealth Uhealth 2017;5(12):e167


Evaluating Patient Perspectives of Provider Professionalism on Twitter in an Academic Obstetrics and Gynecology Clinic: Patient Survey

Evaluating Patient Perspectives of Provider Professionalism on Twitter in an Academic Obstetrics and Gynecology Clinic: Patient Survey

Twitter does not allow the creation of multiple profiles with the exact same name, thus the female provider profiles had three permutations of a similar first name but the same last name (Ashley, Ashlee, and Ashleigh Scott, MD).

Rosalyn E Maben-Feaster, R Brent Stansfield, AnneMarie Opipari, Maya M Hammoud

J Med Internet Res 2018;20(3):e78


Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Hence, the objective of this review is to answer the questions: To what extent do K-SRS enable PGHD? And to what effect?

Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli

JMIR Rehabil Assist Technol 2018;5(1):e11