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

^*Tämän työn vastaava kirjoittaja

Ambient Intelligence

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

224 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers
Toimittajat	Chiara Di Francescomarino, Remco Dijkman, Uwe Zdun
Kustantaja	Springer
Sivut	430-435
Sivumäärä	6
ISBN (painettu)	9783030374525
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-030-37453-2_35
Tila	Julkaistu - 1 tammik. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Business Process Management - Vienna, Itävalta Kesto: 1 syysk. 2019 → 6 syysk. 2019 Konferenssinumero: 17

Julkaisusarja

Nimi	Lecture Notes in Business Information Processing
Vuosikerta	362 LNBIP
ISSN (painettu)	1865-1348
ISSN (elektroninen)	1865-1356

Conference

Conference	International Conference on Business Process Management
Lyhennettä	BPM
Maa/Alue	Itävalta
Kaupunki	Vienna
Ajanjakso	01/09/2019 → 06/09/2019

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

SDG 9 – Teollisuus, innovaatiot ja infrastruktuuri

Pääsy asiakirjaan

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

ELEC_Sigg_Capturing_human-machine_LNBIPAccepted author manuscript, 3,13 MB

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Link to publication in Scopus

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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. teoksessa C. Di Francescomarino, R. Dijkman, & U. Zdun (Toimittajat), Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers (Sivut 430-435). (Lecture Notes in Business Information Processing; Vuosikerta 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. Toimittaja / Chiara Di Francescomarino ; Remco Dijkman ; Uwe Zdun. Springer, 2019. Sivut 430-435 (Lecture Notes in Business Information Processing).

@inproceedings{1fed45119fcd452c831831a2309b91e0,

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.",

keywords = "5G, Collaborative Robotics, Industry 4.0, Radio sensing",

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

year = "2019",

month = jan,

day = "1",

doi = "10.1007/978-3-030-37453-2\_35",

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series = "Lecture Notes in Business Information Processing",

publisher = "Springer",

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editor = "\{Di Francescomarino\}, Chiara and Remco Dijkman and Uwe Zdun",

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

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note = "International Conference on Business Process Management, BPM ; Conference date: 01-09-2019 Through 06-09-2019",

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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. julkaisussa C Di Francescomarino, R Dijkman & U Zdun (toim), Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. Lecture Notes in Business Information Processing, Vuosikerta 362 LNBIP, Springer, Sivut 430-435, International Conference on Business Process Management, Vienna, Itävalta, 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. toim. / Chiara Di Francescomarino; Remco Dijkman; Uwe Zdun. Springer, 2019. s. 430-435 (Lecture Notes in Business Information Processing; Vuosikerta 362 LNBIP).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

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

UR - https://www.scopus.com/pages/publications/85078528120

U2 - 10.1007/978-3-030-37453-2_35

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

PB - Springer

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. julkaisussa Di Francescomarino C, Dijkman R, Zdun U, toimittajat, Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. Springer. 2019. s. 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

Abstrakti

Julkaisusarja

Conference

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