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

Giulio Masinelli; Farnaz Forooghifar; Adriana Arza; David Atienza; Amir Aminifar

doi:10.1109/MDAT.2020.2977070

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

Giulio Masinelli, Farnaz Forooghifar, Adriana Arza, David Atienza, Amir Aminifar

Research output: Contribution to journal › Article › peer-review

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

Original language	English
Article number	9018161
Pages (from-to)	58-66
Number of pages	9
Journal	IEEE Design and Test
Volume	37
Issue number	5
DOIs	https://doi.org/10.1109/MDAT.2020.2977070
Publication status	Published - 2020 Oct
Externally published	Yes

Subject classification (UKÄ)

Computer Science

Free keywords

Edge wearable systems
Machine learning
Manual labor
Multimodal
Self-awareness
Workload monitoring

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MDAT.2020.2977070

Cite this

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

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 ",

keywords = "Edge wearable systems, Machine learning, Manual labor, Multimodal, Self-awareness, Workload monitoring",

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doi = "10.1109/MDAT.2020.2977070",

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AU - Masinelli, Giulio

AU - Forooghifar, Farnaz

AU - Arza, Adriana

AU - Atienza, David

AU - Aminifar, Amir

PY - 2020/10

Y1 - 2020/10

N2 - 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

AB - 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

KW - Edge wearable systems

KW - Machine learning

KW - Manual labor

KW - Multimodal

KW - Self-awareness

KW - Workload monitoring

U2 - 10.1109/MDAT.2020.2977070

DO - 10.1109/MDAT.2020.2977070

M3 - Article

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M1 - 9018161

ER -

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

Abstract

Subject classification (UKÄ)

Free keywords

UN SDGs

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Cite this