2017 conference paper

Energy-efficient activity recognition via multiple time-scale analysis

2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1466–1472.

By: N. Lokare n, S. Samadi n, B. Zhong n, L. Gonzalez n, F. Mohammadzadeh n & E. Lobaton n

TL;DR: This work proposes a novel power-efficient strategy for supervised human activity recognition using a multiple time-scale approach, which takes into account various window sizes, and shows that the proposed approach Sequential Maximum-Likelihood achieves high F1 score across all activities while providing lower power consumption than the standard Maximum- likelihood approach. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: NC State University Libraries, NC State University Libraries, ORCID
Added: August 6, 2018

2016 conference paper

Comparing wearable devices with wet and textile electrodes for activity recognition

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3539–3542.

By: N. Lokare n, L. Gonzalez n & E. Lobaton n

MeSH headings : Artifacts; Electrocardiography / instrumentation; Electrocardiography / methods; Electrodes; Equipment Design; Humans; Muscle, Skeletal / physiology; Principal Component Analysis; Signal Processing, Computer-Assisted; Textiles
TL;DR: It is observed that signals from the dry textile electrodes introduce less artifacts associated with muscle activation, so it is possible to obtain good performance for both the wet and dry electrodes. (via Semantic Scholar)
Sources: NC State University Libraries, ORCID, NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.