A Method of Learner's Sitting Posture Recognition Based on Depth Image

Zeng, Xing; Bei, Sun; Wang, Enlong; Luo, Wenjian; Liu, Taocheng

doi:10.2991/caai-17.2017.125

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…There are several approaches to monitor sitting posture, and some use a categorization method. The authors of [11] collect 3D footage of persons sitting at a desk and classify the images into 14 different categories after applying feature enhancement. In [12] a quantitative assessment of posture with a wear-able monitoring system has been conducted to detect poor sitting postures causing neck or back pain.…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of Occupant Posture and Shoulder Belt Fit Using Safety Specific Key Points

Hartleitner

Koppisetty

Bohman

2022

IEEE Open J. Intell. Transp. Syst.

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Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorisation of the respective tracked occupant by analysing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.

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Section: Introductionmentioning

confidence: 99%