Gesture detection towards real-time ergonomic analysis for intelligent automation assistance

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dc.contributor.authorMgbemena, Chika Edith
dc.contributor.authorOyekan, John
dc.contributor.authorTiwari, Ashutosh
dc.contributor.authorXu, Yuchun
dc.contributor.authorFletcher, Sarah R.
dc.contributor.authorHutabarat, Windo
dc.contributor.authorPrabhu, Vinayak Ashok
dc.date.accessioned2016-09-13T09:22:36Z
dc.date.available2016-09-13T09:22:36Z
dc.date.issued2016-07-10
dc.description.abstractManual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator—precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks.en_UK
dc.identifier.citationMgbemena, C. E. et al. (2016) Gesture detection towards real-time ergonomic analysis for intelligent automation assistance, Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future: Proceedings of the AHFE 2016 International Conference on Human Aspects of Advanced Manufacturing, July 27-31, 2016, Walt Disney World, Florida, USA, Part IV, pp. 217-228en_UK
dc.identifier.issn2194-5357
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-41697-7_20
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10522
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution-Non-Commercial-No Derivatives 3.0 Unported (CC BY-NC-ND 3.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectMicrosoft kinecten_UK
dc.subjectKinect studioen_UK
dc.subjectVisual gesture builderen_UK
dc.subjectRobotsen_UK
dc.titleGesture detection towards real-time ergonomic analysis for intelligent automation assistanceen_UK
dc.typeBook chapteren_UK

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