Workstation-Operator Interaction in 4.0 Era: WOI 4.0

被引:12
作者
Cohen, Yuval [1 ]
Golan, Maya [1 ]
Singer, Gonen [1 ]
Faccio, Maurizio [2 ]
机构
[1] Tel Aviv Afeka Acad Coll Engn, IL-69988 Tel Aviv, Israel
[2] Univ Padua, I-35131 Padua, Italy
关键词
HMI; Industry; 4.0; Cognitive Manufacturing; Affect Computing; Context Aware Computing; PREDICTION;
D O I
10.1016/j.ifacol.2018.08.327
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Currently machine operator interface is mainly focused on providing the operator with easy control over the production processes and easy access to related information. However, myriad of recent technological advances in variety of fields including AI, raise the question of what could be added to the operator-machine interaction capabilities and how. This article explores the possibilities to harness new capabilities in cognitive and behavioral knowledge as well as AI and "Industry 4.0" literature in order to outline the architectural framework and capabilities of future work-station operator interaction as a principal component of the human machine interaction in the "Industry 4.0" era. The proposed system is named "Workstation Operator Interaction 4.0" (WOI 4.0). The equipment's capabilities allows an adaptive ongoing interaction that aims to improve operator performance, safety, well-being, and satisfaction, as well as production measures. The paper describes the main elements of the proposed WOI 4.0 architecture, and illustrates a case of smart machine operator interactions. The contributions, limitations, and implications of the proposed WOI 4.0 system in the "Industry 4.0" arena are discussed, and future research directions are presented. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:399 / 404
页数:6
相关论文
共 25 条
[1]  
[Anonymous], 2003, PERSONALITY ADULTHOO, DOI DOI 10.4324/9780203428412
[2]  
Bouchner P, 2009, NEURAL NETW WORLD, V19, P109
[3]  
Chastagnol C, 2014, NATURAL INTERACTION, P199
[4]   Prevention and Correction in Post-Error Performance: An Ounce of Prevention, a Pound of Cure [J].
Crump, Matthew J. C. ;
Logan, Gordon D. .
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL, 2013, 142 (03) :692-709
[5]  
Davis R. D., 2014, U. S. Patent Application, Patent No. [14/ 325,529, 14325529]
[6]  
Delean B., 2016, U. S. Patent, Patent No. [9,472,082, 9472082]
[7]   Accidental Fall Detection Based on Skeleton Joint Correlation and Activity Boundary [J].
Flores-Barranco, Martha Magali ;
Ibarra-Mazano, Mario-Alberto ;
Cheng, Irene .
ADVANCES IN VISUAL COMPUTING, PT II (ISVC 2015), 2015, 9475 :489-498
[8]  
Gorecky D., 2014, 12 IEEE INT C IND IN, P289
[9]   Detecting stress during real-world driving tasks using physiological sensors [J].
Healey, JA ;
Picard, RW .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2005, 6 (02) :156-166
[10]  
Jesus RM, 2002, LECT NOTES COMPUT SC, V2492, P155