research-article

COGNITO: a cognitive assistance and training system for manual tasks in industry

Authors:

Dominic Gorecky,

Simon F. Worgan,

Gerrit MeixnerAuthors Info & Claims

ECCE '11: Proceedings of the 29th Annual European Conference on Cognitive Ergonomics

Pages 53 - 56

https://doi.org/10.1145/2074712.2074723

Published: 24 August 2011 Publication History

Abstract

In this paper we present a novel concept for cognitive assistance and training in manual industrial assembly. In the European FP7 project COGNITO, our aim is to design a mobile, personal system, which instructs operators in task solving and tool handling. The system not only provides instructions, but it is also able to understand and induce human workflows. Due to its high sensing capabilities, the system automatically analyzes and records assembly workflows by observing advanced users to build-up a system-internal understanding of assembly processes. The captured knowledge is then used by the system to assist and train inexperienced operators. The overall approach is based on state-of-the-art techniques in motion and object tracking, task analysis, decision-making and user-adaptive visualisation by means of augmented reality. The COGNITO system is an important step towards cognitive operator support. Enterprise knowledge can be documented, shared and applied in a cooperative and interactive manner, enabling human operators to keep pace with increased complexity in industrial processes.

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Cited By

Singhaphandu RPannakkong WHuynh vBoonkwan P(2024)A Manual Assembly Virtual Training System With Automatically Generated Augmented Feedback: Using the Comparison of Digitized Operator’s SkillIEEE Access10.1109/ACCESS.2024.343691012(133356-133391)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3436910
Schacht SLanquillon CHenne SSchmid EMehlin V(2023)Kognitive AssistenzsystemeKnowledge Science – Fallstudien10.1007/978-3-658-41155-8_3(21-32)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-3-658-41155-8_3
Besginow ABüttner SUkita NRöcker C(2022)Deep Learning-Based Action Detection for Continuous Quality Control in Interactive Assistance SystemsHuman-Technology Interaction10.1007/978-3-030-99235-4_5(127-149)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-030-99235-4_5
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Index Terms

COGNITO: a cognitive assistance and training system for manual tasks in industry
1. Applied computing
  1. Operations research
    1. Industry and manufacturing
      1. Command and control
2. Human-centered computing
  1. Accessibility
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Published In

cover image ACM Other conferences

ECCE '11: Proceedings of the 29th Annual European Conference on Cognitive Ergonomics

August 2011

291 pages

ISBN:9781450310291

DOI:10.1145/2074712

General Chairs:
Anke Dittmar
University of Rostock, Germany
,
Peter Forbrig
University of Rostock, Germany

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

EACE: European Association for Cognitive Ergonomics
Rostock: University of Rostock

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2011

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ECCE '11

Sponsor:

EACE
Rostock

ECCE '11: European Conference on Cognitive Ergonomics

August 24 - 26, 2011

Rostock, Germany

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Overall Acceptance Rate 56 of 91 submissions, 62%

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Cited By

Singhaphandu RPannakkong WHuynh vBoonkwan P(2024)A Manual Assembly Virtual Training System With Automatically Generated Augmented Feedback: Using the Comparison of Digitized Operator’s SkillIEEE Access10.1109/ACCESS.2024.343691012(133356-133391)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3436910
Schacht SLanquillon CHenne SSchmid EMehlin V(2023)Kognitive AssistenzsystemeKnowledge Science – Fallstudien10.1007/978-3-658-41155-8_3(21-32)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-3-658-41155-8_3
Besginow ABüttner SUkita NRöcker C(2022)Deep Learning-Based Action Detection for Continuous Quality Control in Interactive Assistance SystemsHuman-Technology Interaction10.1007/978-3-030-99235-4_5(127-149)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-030-99235-4_5
Wagner PLengenfelder CHolzbach GBecker MBirnstill PVoit MBejhad ASamorei TBeyerer J(2021)Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality AssuranceApplied Sciences10.3390/app1116733911:16(7339)Online publication date: 10-Aug-2021
https://doi.org/10.3390/app11167339
Olguín Muñoz MKlatzky RWang JPillai PSatyanarayanan MGross J(2021)Impact of delayed response on wearable cognitive assistancePLOS ONE10.1371/journal.pone.024869016:3(e0248690)Online publication date: 23-Mar-2021
https://doi.org/10.1371/journal.pone.0248690
Wang ZBai XZhang SHe WWang YHan DWei SWei BChen C(2021)M-AR: A Visual Representation of Manual Operation Precision in AR AssemblyInternational Journal of Human–Computer Interaction10.1080/10447318.2021.190927837:19(1799-1814)Online publication date: 8-Apr-2021
https://doi.org/10.1080/10447318.2021.1909278
Hadi MBrillinger MWeinzerl M(2021)Parametric Evaluation and Cost Analysis in an e-Axle Assembly Layout5th EAI International Conference on Management of Manufacturing Systems10.1007/978-3-030-67241-6_4(39-55)Online publication date: 5-Jan-2021
https://doi.org/10.1007/978-3-030-67241-6_4
Tao YColtey EWang TAlonso MShyu MChen SAlhaffar HElias ABogosian BVassigh S(2020)Confidence Estimation Using Machine Learning in Immersive Learning Environments2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR49039.2020.00058(247-252)Online publication date: Aug-2020
https://doi.org/10.1109/MIPR49039.2020.00058
Abdul Hadi MBrillinger MHaas FWeinzerl M(2020)Roadmap to an adaptive assembly line for e-axlesWireless Networks10.1007/s11276-020-02356-6Online publication date: 15-May-2020
https://doi.org/10.1007/s11276-020-02356-6
Dostert JMüller R(2020)Motivational assistance system design for industrial production: from motivation theories to design strategiesCognition, Technology & Work10.1007/s10111-020-00643-yOnline publication date: 20-Jul-2020
https://doi.org/10.1007/s10111-020-00643-y
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