This article is a correction to:
CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset
Hui Liu
Yale Hartmann
Tanja Schultz- Cognitive Systems Lab, Department for Mathematics and Computer Science, University of Bremen, Bremen, Germany
A Corrigendum on
CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset
by Liu, H., Hartmann, Y., and Schultz, T. (2021) Front. Comput. Sci. 3:759136. doi:10.3389/fcomp.2021.759136
In the original article, there were two error numbers the Background, paragraph 3. The number 21 was used where 18 and 22 should have been used. The errors do not alter the conclusions: the former introduces another dataset, which is not published in this article; the latter is correctly referred to several times in the following text and the abstract.
A correction has been made to Background, paragraph 3:
“In this article, we disclose our in-house collected sensor-based dataset, CSL-SHARE (Cognitive Systems Lab Sensor-based Human Activity REcordings). Based on the improvement of the recording plan and organization through the experience gathered from the pilot datasets’ collection of CSL17 (one subject, seven activities of daily living, 15 minutes) and CSL18 (four subjects, 18 activities of daily living and sports, 90 minutes), the CSL-SHARE dataset covers 22 types of activities of daily living and sports from 20 subjects in a total time of 691 minutes, of which 363 minutes are segmented and annotated. In this dataset, we used two triaxial accelerometers, two triaxial gyroscopes, four surface electromyography (sEMG) sensors, one biaxial electrogoniometer, and one airborne microphone integrated into a knee bandage, bringing the total number of channels to 19, as these sensors can provide usable and reliable biosignals for HAR research, gait analysis, and health assessment according to existing studies, such as Whittle (1996), Rowe et al. (2000), Mathie et al. (2003), Kwapisz et al. (2010), Rebelo et al. (2013), and Teague et al. (2016). We also tried to use a piezoelectric microphone and a force sensor for sensing the acoustic and physical pressure signals from the knee during the acquisition. Nevertheless, in subsequent analysis and research, we did not have evidence to support their contribution to HAR research. Therefore, we removed these two channels of signal from the public dataset. In addition, although our two pilot datasets mentioned above, CSL17 and CSL18, are not publicly available due to the relatively smaller data volume, they can also be obtained from us for scientific research purposes.”
There was an error in the link the Data Availability Statement. The correct link “https://www.uni-bremen.de/en/csl/research/human-activity-recognition.” The link has been updated in the statement.
The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s Note
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Keywords: human activity recognition, activity of daily living, EMG, inertial sensor, internal sensing, wearable sensing, accelerometer, gyroscope
Citation: Liu H, Hartmann Y and Schultz T (2021) Corrigendum: CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset. Front. Comput. Sci. 3:800056. doi: 10.3389/fcomp.2021.800056
Received: 22 October 2021; Accepted: 17 November 2021;
Published: 23 December 2021.
Edited and reviewed by:
Pekka Siirtola, University of Oulu, FinlandCopyright © 2021 Liu, Hartmann and Schultz. 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: Hui Liu, hui.liu@uni-bremen.de