Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance


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.

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Divisions: College of Engineering & Physical Sciences
Additional Information: © 2016 Springer Publishing. This is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at:
ISBN: 978-3-319-41696-0, 978-3-319-41697-7
Last Modified: 03 Jun 2024 08:01
Date Deposited: 16 Mar 2018 08:45
Full Text Link: http://link.spr ... -319-41697-7_20
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PURE Output Type: Other chapter contribution
Published Date: 2016-07-10
Authors: Mgbemena, Chika Edith
Oyekan, John
Tiwari, Ashutosh
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)
Fletcher, Sarah
Hutabarat, Windo
Prabhu, Vinayak



Version: Accepted Version

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