Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments

Stephan Sigg; Sameera Palipana; Stefano Savazzi; Sanaz Kianoush

doi:10.1007/978-3-030-37453-2_35

Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments

Stephan Sigg, Sameera Palipana^*, Stefano Savazzi, Sanaz Kianoush

^*Corresponding author for this work

Ambient Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

199 Downloads (Pure)

Abstract

In manufacturing environments, human workers interact with increasingly autonomous machinery. To ensure workspace safety and production efficiency during human-robot cooperation, continuous and accurate tracking and perception of workers’ activities is required. The RadioSense project intends to move forward the state-of-the-art in advanced sensing and perception for next generation manufacturing workspace. In this paper, we describe our ongoing efforts towards multi-subject recognition cases with multiple persons conducting several simultaneous activities. Perturbations induced by moving bodies/objects on the electromagnetic wavefield can be processed for environmental perception by leveraging next generation (5G) New Radio (NR) technologies, including MIMO systems, high performance edge-cloud computing and novel (or custom designed) deep learning tools.

Original language	English
Title of host publication	Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers
Editors	Chiara Di Francescomarino, Remco Dijkman, Uwe Zdun
Publisher	Springer
Pages	430-435
Number of pages	6
ISBN (Print)	9783030374525
DOIs	https://doi.org/10.1007/978-3-030-37453-2_35
Publication status	Published - 1 Jan 2019
MoE publication type	A4 Conference publication
Event	International Conference on Business Process Management - Vienna, Austria Duration: 1 Sept 2019 → 6 Sept 2019 Conference number: 17

Publication series

Name	Lecture Notes in Business Information Processing
Volume	362 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	International Conference on Business Process Management
Abbreviated title	BPM
Country/Territory	Austria
City	Vienna
Period	01/09/2019 → 06/09/2019

Keywords

5G
Collaborative Robotics
Industry 4.0
Radio sensing

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10.1007/978-3-030-37453-2_35

ELEC_Sigg_Capturing_human-machine_LNBIPAccepted author manuscript, 3.13 MB

Cite this

Sigg, S., Palipana, S., Savazzi, S., & Kianoush, S. (2019). Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments. In C. Di Francescomarino, R. Dijkman, & U. Zdun (Eds.), Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers (pp. 430-435). (Lecture Notes in Business Information Processing; Vol. 362 LNBIP). Springer. https://doi.org/10.1007/978-3-030-37453-2_35

Sigg, Stephan ; Palipana, Sameera ; Savazzi, Stefano et al. / Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments. Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. editor / Chiara Di Francescomarino ; Remco Dijkman ; Uwe Zdun. Springer, 2019. pp. 430-435 (Lecture Notes in Business Information Processing).

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title = "Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments",

abstract = "In manufacturing environments, human workers interact with increasingly autonomous machinery. To ensure workspace safety and production efficiency during human-robot cooperation, continuous and accurate tracking and perception of workers{\textquoteright} activities is required. The RadioSense project intends to move forward the state-of-the-art in advanced sensing and perception for next generation manufacturing workspace. In this paper, we describe our ongoing efforts towards multi-subject recognition cases with multiple persons conducting several simultaneous activities. Perturbations induced by moving bodies/objects on the electromagnetic wavefield can be processed for environmental perception by leveraging next generation (5G) New Radio (NR) technologies, including MIMO systems, high performance edge-cloud computing and novel (or custom designed) deep learning tools.",

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author = "Stephan Sigg and Sameera Palipana and Stefano Savazzi and Sanaz Kianoush",

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booktitle = "Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers",

address = "Germany",

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Sigg, S, Palipana, S, Savazzi, S & Kianoush, S 2019, Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments. in C Di Francescomarino, R Dijkman & U Zdun (eds), Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 362 LNBIP, Springer, pp. 430-435, International Conference on Business Process Management, Vienna, Austria, 01/09/2019. https://doi.org/10.1007/978-3-030-37453-2_35

Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments. / Sigg, Stephan; Palipana, Sameera; Savazzi, Stefano et al.
Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. ed. / Chiara Di Francescomarino; Remco Dijkman; Uwe Zdun. Springer, 2019. p. 430-435 (Lecture Notes in Business Information Processing; Vol. 362 LNBIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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T1 - Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments

AU - Sigg, Stephan

AU - Palipana, Sameera

AU - Savazzi, Stefano

AU - Kianoush, Sanaz

N1 - Conference code: 17

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In manufacturing environments, human workers interact with increasingly autonomous machinery. To ensure workspace safety and production efficiency during human-robot cooperation, continuous and accurate tracking and perception of workers’ activities is required. The RadioSense project intends to move forward the state-of-the-art in advanced sensing and perception for next generation manufacturing workspace. In this paper, we describe our ongoing efforts towards multi-subject recognition cases with multiple persons conducting several simultaneous activities. Perturbations induced by moving bodies/objects on the electromagnetic wavefield can be processed for environmental perception by leveraging next generation (5G) New Radio (NR) technologies, including MIMO systems, high performance edge-cloud computing and novel (or custom designed) deep learning tools.

AB - In manufacturing environments, human workers interact with increasingly autonomous machinery. To ensure workspace safety and production efficiency during human-robot cooperation, continuous and accurate tracking and perception of workers’ activities is required. The RadioSense project intends to move forward the state-of-the-art in advanced sensing and perception for next generation manufacturing workspace. In this paper, we describe our ongoing efforts towards multi-subject recognition cases with multiple persons conducting several simultaneous activities. Perturbations induced by moving bodies/objects on the electromagnetic wavefield can be processed for environmental perception by leveraging next generation (5G) New Radio (NR) technologies, including MIMO systems, high performance edge-cloud computing and novel (or custom designed) deep learning tools.

KW - 5G

KW - Collaborative Robotics

KW - Industry 4.0

KW - Radio sensing

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DO - 10.1007/978-3-030-37453-2_35

M3 - Conference article in proceedings

AN - SCOPUS:85078528120

SN - 9783030374525

T3 - Lecture Notes in Business Information Processing

SP - 430

EP - 435

BT - Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers

A2 - Di Francescomarino, Chiara

A2 - Dijkman, Remco

A2 - Zdun, Uwe

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T2 - International Conference on Business Process Management

Y2 - 1 September 2019 through 6 September 2019

ER -

Sigg S, Palipana S, Savazzi S, Kianoush S. Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments. In Di Francescomarino C, Dijkman R, Zdun U, editors, Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. Springer. 2019. p. 430-435. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-030-37453-2_35

Capturing Human-Machine Interaction Events from Radio Sensors in Industry 4.0 Environments

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