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Gesture recognition for human-robot collaboration: A review
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-9618-8826
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2018 (English)In: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 68, p. 355-367Article, review/survey (Refereed) Published
Abstract [en]

Recently, the concept of human-robot collaboration has raised many research interests. Instead of robots replacing human workers in workplaces, human-robot collaboration allows human workers and robots working together in a shared manufacturing environment. Human-robot collaboration can release human workers from heavy tasks with assistive robots if effective communication channels between humans and robots are established. Although the communication channels between human workers and robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for long time. Covering some of the most important technologies and algorithms of gesture recognition, this paper is intended to provide an overview of the gesture recognition research and explore the possibility to apply gesture recognition in human-robot collaborative manufacturing. In this paper, an overall model of gesture recognition for human-robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human-robot collaboration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. Statistical analysis is also presented after technical analysis. Towards the end of this paper, future research trends are outlined.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 68, p. 355-367
Keywords [en]
Human-robot collaboration, Gesture, Gesture recognition
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-270824DOI: 10.1016/j.ergon.2017.02.004ISI: 000452343300038Scopus ID: 2-s2.0-85014161933OAI: oai:DiVA.org:kth-270824DiVA, id: diva2:1414585
Note

QC 20200313

Available from: 2020-03-13 Created: 2020-03-13 Last updated: 2020-03-13Bibliographically approved

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Liu, HongyiWang, Lihui

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