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Self-aware machine learning for multimodal workload monitoring during manual labor on edge wearable sensors

Masinelli, Giulio ; Forooghifar, Farnaz ; Arza, Adriana ; Atienza, David and Aminifar, Amir LU orcid (2020) In IEEE Design and Test 37(5). p.58-66
Abstract

Editor's notes: This article discusses self-awareness in wearable edge devices to enable real-time and long-term health monitoring. The authors use the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. This approach leads to a 27.6% lower energy consumption with less than 6% of performance loss. - Umit Y. Ogras, Arizona State University

Please use this url to cite or link to this publication:
author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Edge wearable systems, Machine learning, Manual labor, Multimodal, Self-awareness, Workload monitoring
in
IEEE Design and Test
volume
37
issue
5
article number
9018161
pages
9 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85081342835
ISSN
2168-2356
DOI
10.1109/MDAT.2020.2977070
language
English
LU publication?
no
additional info
Publisher Copyright: © 2013 IEEE.
id
8de30684-a49c-427e-8a89-b6e9756542a2
date added to LUP
2022-02-05 01:20:14
date last changed
2022-04-22 07:26:04
@article{8de30684-a49c-427e-8a89-b6e9756542a2,
  abstract     = {{<p>Editor's notes: This article discusses self-awareness in wearable edge devices to enable real-time and long-term health monitoring. The authors use the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. This approach leads to a 27.6% lower energy consumption with less than 6% of performance loss. - Umit Y. Ogras, Arizona State University </p>}},
  author       = {{Masinelli, Giulio and Forooghifar, Farnaz and Arza, Adriana and Atienza, David and Aminifar, Amir}},
  issn         = {{2168-2356}},
  keywords     = {{Edge wearable systems; Machine learning; Manual labor; Multimodal; Self-awareness; Workload monitoring}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{58--66}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Design and Test}},
  title        = {{Self-aware machine learning for multimodal workload monitoring during manual labor on edge wearable sensors}},
  url          = {{http://dx.doi.org/10.1109/MDAT.2020.2977070}},
  doi          = {{10.1109/MDAT.2020.2977070}},
  volume       = {{37}},
  year         = {{2020}},
}