mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance

Florian Jungwirth; Michaela Murauer; Johannes Selymes; Michael Haslgrübler-Huemer; Benedikt Gollan; Alois Ferscha

doi:10.1145/3341162.3350842

mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance

Florian Jungwirth
, Michaela Murauer
, Johannes Selymes
, Michael Haslgrübler-Huemer
, Benedikt Gollan
, Alois Ferscha

Institute of Pervasive Computing

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

Abstract

The eyes are a particularly interesting modality for cognitive industrial assistance systems, as gaze analysis can reveal cognition- and task-related aspects, while gaze interaction depicts a lightweight and fast method for hands-free machine control. In this paper, we present mobEYEle, a body-worn eye tracking platform that performs the entire computation directly on the user, as opposed to primarily streaming the data to a centralized unit for online processing and hence restricting its pervasiveness. The applicability of the platform is demonstrated throughout extensive performance and battery runtime tests. Moreover, a self-contained calibration method is outlined that enables the usage of mobEYEle without any supervisor nor digital screen.

Original language	English
Title of host publication	Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct)
Editors	Harle, Farrahi, Lane
Publisher	ACM
Pages	1113-1119
Number of pages	7
ISBN (Electronic)	9781450368698
DOIs	https://doi.org/10.1145/3341162.3350842
Publication status	Published - Sept 2019

Fields of science

202017 Embedded systems
102 Computer Sciences
102009 Computer simulation
102013 Human-computer interaction
102019 Machine learning
102020 Medical informatics
102021 Pervasive computing
102022 Software development
102025 Distributed systems
211902 Assistive technologies
211912 Product design

JKU Focus areas

Digital Transformation

Access to Document

10.1145/3341162.3350842

Cite this

Jungwirth, F., Murauer, M., Selymes, J., Haslgrübler-Huemer, M., Gollan, B., & Ferscha, A. (2019). mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance. In Harle, Farrahi, Lane (Ed.), Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct) (pp. 1113-1119). ACM. https://doi.org/10.1145/3341162.3350842

Jungwirth, Florian ; Murauer, Michaela ; Selymes, Johannes et al. / mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance. Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). editor / Harle, Farrahi, Lane. ACM, 2019. pp. 1113-1119

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title = "mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance",

abstract = "The eyes are a particularly interesting modality for cognitive industrial assistance systems, as gaze analysis can reveal cognition- and task-related aspects, while gaze interaction depicts a lightweight and fast method for hands-free machine control. In this paper, we present mobEYEle, a body-worn eye tracking platform that performs the entire computation directly on the user, as opposed to primarily streaming the data to a centralized unit for online processing and hence restricting its pervasiveness. The applicability of the platform is demonstrated throughout extensive performance and battery runtime tests. Moreover, a self-contained calibration method is outlined that enables the usage of mobEYEle without any supervisor nor digital screen.",

author = "Florian Jungwirth and Michaela Murauer and Johannes Selymes and Michael Haslgr{\"u}bler-Huemer and Benedikt Gollan and Alois Ferscha",

year = "2019",

month = sep,

doi = "10.1145/3341162.3350842",

language = "English",

pages = "1113--1119",

editor = "\{Harle, Farrahi, Lane\}",

booktitle = "Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC {\textquoteright}19 Adjunct)",

publisher = "ACM",

}

Jungwirth, F, Murauer, M, Selymes, J, Haslgrübler-Huemer, M, Gollan, B & Ferscha, A 2019, mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance. in Harle, Farrahi, Lane (ed.), Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). ACM, pp. 1113-1119. https://doi.org/10.1145/3341162.3350842

mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance. / Jungwirth, Florian; Murauer, Michaela; Selymes, Johannes et al.
Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). ed. / Harle, Farrahi, Lane. ACM, 2019. p. 1113-1119.

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

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AU - Murauer, Michaela

AU - Selymes, Johannes

AU - Haslgrübler-Huemer, Michael

AU - Gollan, Benedikt

AU - Ferscha, Alois

PY - 2019/9

Y1 - 2019/9

N2 - The eyes are a particularly interesting modality for cognitive industrial assistance systems, as gaze analysis can reveal cognition- and task-related aspects, while gaze interaction depicts a lightweight and fast method for hands-free machine control. In this paper, we present mobEYEle, a body-worn eye tracking platform that performs the entire computation directly on the user, as opposed to primarily streaming the data to a centralized unit for online processing and hence restricting its pervasiveness. The applicability of the platform is demonstrated throughout extensive performance and battery runtime tests. Moreover, a self-contained calibration method is outlined that enables the usage of mobEYEle without any supervisor nor digital screen.

AB - The eyes are a particularly interesting modality for cognitive industrial assistance systems, as gaze analysis can reveal cognition- and task-related aspects, while gaze interaction depicts a lightweight and fast method for hands-free machine control. In this paper, we present mobEYEle, a body-worn eye tracking platform that performs the entire computation directly on the user, as opposed to primarily streaming the data to a centralized unit for online processing and hence restricting its pervasiveness. The applicability of the platform is demonstrated throughout extensive performance and battery runtime tests. Moreover, a self-contained calibration method is outlined that enables the usage of mobEYEle without any supervisor nor digital screen.

UR - https://www.pervasive.jku.at/Research/Publications/?do=abstract&key=575

U2 - 10.1145/3341162.3350842

DO - 10.1145/3341162.3350842

M3 - Conference proceedings

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BT - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct)

A2 - Harle, Farrahi, Lane, null

PB - ACM

ER -

Jungwirth F, Murauer M, Selymes J, Haslgrübler-Huemer M, Gollan B, Ferscha A. mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance. In Harle, Farrahi, Lane, editor, Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). ACM. 2019. p. 1113-1119 doi: 10.1145/3341162.3350842

mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Pro2Future - Products and Production Systems of the Future

EyeControl: Eye-Controlled Machines

Cite this

mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

Pro2Future - Products and Production Systems of the Future

EyeControl: Eye-Controlled Machines

Cite this