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

Mgbemena, Chika Edith; Oyekan, John; Tiwari, Ashutosh; Xu, Yuchun; Fletcher, Sarah R.; Hutabarat, Windo; Prabhu, Vinayak Ashok

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dc.contributor.author	Mgbemena, Chika Edith
dc.contributor.author	Oyekan, John
dc.contributor.author	Tiwari, Ashutosh
dc.contributor.author	Xu, Yuchun
dc.contributor.author	Fletcher, Sarah R.
dc.contributor.author	Hutabarat, Windo
dc.contributor.author	Prabhu, Vinayak Ashok
dc.date.accessioned	2016-09-13T09:22:36Z
dc.date.available	2016-09-13T09:22:36Z
dc.date.issued	2016-07-10
dc.identifier.citation	Mgbemena, 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. 217-228	en_UK
dc.identifier.issn	2194-5357
dc.identifier.uri	http://dx.doi.org/10.1007/978-3-319-41697-7_20
dc.identifier.uri	https://dspace.lib.cranfield.ac.uk/handle/1826/10522
dc.description.abstract	Manual 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.language.iso	en	en_UK
dc.publisher	Springer	en_UK
dc.rights	Attribution-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.subject	Microsoft kinect	en_UK
dc.subject	Kinect studio	en_UK
dc.subject	Visual gesture builder	en_UK
dc.subject	Robots	en_UK
dc.title	Gesture detection towards real-time ergonomic analysis for intelligent automation assistance	en_UK
dc.type	Book chapter	en_UK