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Corrigenda and Addenda (44)

Published on in Vol 5, No 12 (2017): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9177, first published .
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

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

Authors of this article:

Nicholas Shawen1 Author Orcid Image ;   Luca Lonini1, 2 Author Orcid Image ;   Chaithanya Krishna Mummidisetty1 Author Orcid Image ;   Ilona Shparii1, 3 Author Orcid Image ;   Mark V Albert1, 2, 3 Author Orcid Image ;   Konrad Kording4, 5 Author Orcid Image ;   Arun Jayaraman1, 2, 6 Author Orcid Image
Article Authors Cited by (5) Tweetations (5) Metrics

    Corrigenda and Addenda

    1Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States

    2Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States

    3Department of Computer Science, Loyola University Chicago, Chicago, IL, United States

    4Department of Bioengineering, University of Pennsylvania, Philadelphia, IL, United States

    5Department of Neuroscience, University of Pennsylvania, Philadelphia, IL, United States

    6Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States

    *these authors contributed equally

    Corresponding Author:

    Luca Lonini, PhD

    Max Nader Lab for Rehabilitation Technologies and Outcomes Research

    Shirley Ryan AbilityLab

    355 E Erie St

    Suite #11-1101

    Chicago, IL, 60611

    United States

    Phone: 1 312 238 1619

    Email: llonini@ricres.org



    The authors of the paper “Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications” (JMIR Mhealth Uhealth 2017;5(10):e151) inadvertently omitted a funding source in the Acknowledgments. Therefore, we would like to change the acknowledgments section of their paper to the following:

    The authors are thankful to Mark Begale and Christopher J Karr from CBITs for their technical assistance with the Purple Robot phone app. 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. This study was funded by the National Institute of Health – NIBIB grant 5 R01 EB019406-04 and by the Max Näder Rehabilitation Technologies and Outcomes Research Center of the Shirley Ryan Ability Lab (formerly Rehabilitation Institute of Chicago).

    The corrected article will appear in the online version of the paper on the JMIR website on December 20, 2017, together with the publication of this correction notice. Because this was made after submission to PubMed Central, the corrected article will also be re-submitted to PubMed Central.

    Edited by G Eysenbach; This is a non–peer-reviewed article. submitted 14.10.17; accepted 16.10.17; published 20.12.17.

    Copyright

    ©Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.12.2017.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.