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Proceedings Paper

Posture recognition associated with lifting of heavy objects using Kinect and Adaboost
Author(s): Sayli Raut; Navaneethakrishna M.; Ramakrishnan S.
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Paper Abstract

Lifting of heavy objects is the common task in the industries. Recent statistics from the Bureau of Labour indicate, back injuries account for one of every five injuries in the workplace. Eighty per cent of these injuries occur to the lower back and are associated with manual materials handling tasks. According to the Industrial ergonomic safety manual, Squatting is the correct posture for lifting a heavy object. In this work, an attempt has been made to monitor posture of the workers during squat and stoop using 3D motion capture and machine learning techniques. For this, Microsoft Kinect V2 is used for capturing the depth data. Further, Dynamic Time Warping and Euclidian distance algorithms are used for extraction of features. Ada-boost algorithm is used for classification of stoop and squat. The results show that the 3D image data is large and complex to analyze. The application of nonlinear and linear metrics captures the variation in the lifting pattern. Additionally, the features extracted from this metric resulted in a classification accuracy of 85% and 81% respectively. This framework may be put-upon to alert the workers in the industrial ergonomic environments.

Paper Details

Date Published: 19 December 2017
PDF: 6 pages
Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 1061304 (19 December 2017); doi: 10.1117/12.2300745
Show Author Affiliations
Sayli Raut, Indian Institute of Technology Madras (India)
Navaneethakrishna M., Indian Institute of Technology Madras (India)
Ramakrishnan S., Indian Institute of Technology Madras (India)


Published in SPIE Proceedings Vol. 10613:
2017 International Conference on Robotics and Machine Vision
Chiharu Ishii; Genci Capi; Jianhong Zhou, Editor(s)

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