Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals
- 1Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
- 2Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
- 3Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States
- 4Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
A Corrigendum on
Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals
by Hu, B., Rouse, E., and Hargrove, L. (2018) Front. Robot. AI 5:14. doi: 10.3389/frobt.2018.00014
In the original article, there were two errors. In the text, the abbreviation for semitendinosus was omitted. In the text, the URL to the data repository available on Figshare was also incorrect.
Corrections have been made to Materials and Methods, Sub-section Instrumentation Setup, Paragraph one and Results, Paragraph one.
EMG signals were recorded using bipolar surface electrodes (DE2.1; Delsys, Boston, MA, USA) from the same seven muscles in each leg: tibialis anterior (TA), medial gastrocnemius (MG), soleus (SOL), vastus lateralis (VL), rectus femoris (RF), biceps femoris (BF), and semitendinosus (ST).
The data are saved in CSV format in subject-specific folders and are available to download from Figshare at https://doi.org/10.6084/m9.figshare.5362627.
The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: gait, locomotion, biomechanics, electromyography, benchmark
Citation: Hu B, Rouse E and Hargrove L (2018) Corrigendum: Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals. Front. Robot. AI 5:127. doi: 10.3389/frobt.2018.00127
Received: 12 September 2018; Accepted: 26 October 2018;
Published: 20 November 2018.
Edited and reviewed by: Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain
Copyright © 2018 Hu, Rouse and Hargrove. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Blair Hu, YmxhaXJodUB1Lm5vcnRod2VzdGVybi5lZHU=