Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)

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

doi:10.2196/mhealth.9177

Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)

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

^*Corresponding author for this work

Physical Medicine and Rehabilitation

Research output: Contribution to journal › Comment/debate › peer-review

2 Scopus citations

Abstract

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.

Original language	English (US)
Article number	e167
Journal	JMIR mHealth and uHealth
Volume	5
Issue number	12
DOIs	https://doi.org/10.2196/mhealth.9177
State	Published - Dec 2017

Funding

The authors of the paper \u201CFall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications\u201D (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 \u2013 NIBIB grant 5 R01 EB019406-04 and by the Max N\u00E4der Rehabilitation Technologies and Outcomes Research Center of the Shirley Ryan Ability Lab (formerly Rehabilitation Institute of Chicago).

ASJC Scopus subject areas

Health Informatics

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Shawen, N., Lonini, L., Mummidisetty, C. K., Shparii, I., Albert, M. V., Kording, K., & Jayaraman, A. (2017). Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201). JMIR mHealth and uHealth, 5(12), Article e167. https://doi.org/10.2196/mhealth.9177

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title = "Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)",

abstract = "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 N{\"a}der 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.",

author = "Nicholas Shawen and Luca Lonini and Mummidisetty, {Chaithanya Krishna} and Ilona Shparii and Albert, {Mark V.} and Konrad Kording and Arun Jayaraman",

note = "Funding Information: 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 N{\"a}der Rehabilitation Technologies and Outcomes Research Center of the Shirley Ryan Ability Lab (formerly Rehabilitation Institute of Chicago). Publisher Copyright: {\textcopyright} 2017 JMIR Publications. All rights reserved.",

year = "2017",

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Shawen, N, Lonini, L, Mummidisetty, CK, Shparii, I, Albert, MV, Kording, K & Jayaraman, A 2017, 'Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)', JMIR mHealth and uHealth, vol. 5, no. 12, e167. https://doi.org/10.2196/mhealth.9177

Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201). / Shawen, Nicholas; Lonini, Luca; Mummidisetty, Chaithanya Krishna et al.
In: JMIR mHealth and uHealth, Vol. 5, No. 12, e167, 12.2017.

Research output: Contribution to journal › Comment/debate › peer-review

TY - JOUR

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T2 - Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)

AU - Shawen, Nicholas

AU - Lonini, Luca

AU - Mummidisetty, Chaithanya Krishna

AU - Shparii, Ilona

AU - Albert, Mark V.

AU - Kording, Konrad

AU - Jayaraman, Arun

N1 - Funding Information: 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). Publisher Copyright: © 2017 JMIR Publications. All rights reserved.

PY - 2017/12

Y1 - 2017/12

N2 - 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.

AB - 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.

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Shawen N, Lonini L, Mummidisetty CK, Shparii I, Albert MV, Kording K et al. Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201). JMIR mHealth and uHealth. 2017 Dec;5(12):e167. doi: 10.2196/mhealth.9177

Erratum: Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications (JMIR mHealth and uHealth (2017) 5:10 (e151) DOI: 10.2196/mhealth.8201)

Abstract

Funding

ASJC Scopus subject areas

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