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Front. Robot. AI | doi: 10.3389/frobt.2018.00127

Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals

  • 1Northwestern University, United States
  • 2Shirley Ryan AbilityLab, United States
  • 3University of Michigan, United States

Text correction

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

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.

Keywords: Gait, Locomotion, Biomechanics, Electromyography, Benchmark data

Received: 12 Sep 2018; Accepted: 26 Oct 2018.

Edited by:

Diego Torricelli, Spanish National Research Council (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: Mr. Blair Hu, Northwestern University, Evanston, United States,