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
(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:
https://lup.lub.lu.se/record/8de30684-a49c-427e-8a89-b6e9756542a2
- author
- Masinelli, Giulio
; Forooghifar, Farnaz
; Arza, Adriana
; Atienza, David
and Aminifar, Amir
LU
- publishing date
- 2020-10
- 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 Computer Society
- 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
- 2025-10-14 12:41:32
@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 Computer Society}},
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}},
}