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

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

Research output: Chapter in Book/Published conference outputOther chapter contribution


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.
Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
Subtitle of host publicationManaging the Enterprise of the Future
EditorsC Schlick, S Trzcieliński
ISBN (Electronic)978-3-319-41697-7
ISBN (Print)978-3-319-41696-0
Publication statusPublished - 10 Jul 2016

Publication series

NameAdvances in Ergonomics of Manufacturing: Managing the Enterprise of the Future
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Bibliographical note

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