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Article . 2026
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Article . 2026
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License: CC BY
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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
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Ergonomic Posture Monitoring through Pose Estimation and Machine Learning

Authors: Laishram Trinity; S Athisii Kayina; Usham Sanjota Chanu;

Ergonomic Posture Monitoring through Pose Estimation and Machine Learning

Abstract

Posture has a direct impact on health and daily performance, but tracking it in real time is not always easy to achieve. Traditional approaches, such as asking experts to observe, are often uncomfortable and time-consuming. With recent progress in computer vision, it is now possible to monitor posture using body landmarks. In this study, we developed a simple but effective system that uses MediaPipe to detect body landmarks and calculate joint angles from a live video feed. By analysing these angles, the system can recognise poor posture and provide immediate feedback through visual messages and auditory alerts. Tests with standard pose estimation datasets showed that the method works reliably while running efficiently on common hardware. The system can be applied in areas such as workplace ergonomics, sports practice, and rehabilitation, where continuous posture monitoring helps to reduce the risk of strain or injury.

Keywords

Posture Detection, Human Pose Estimation, MediaPipe, Real-Time Monitoring, Ergonomics, machine learning, open computer vision, random forest classifier, accuracy matrix.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green