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Identification of heavy lifting using simple wearables (LiftID): development and evaluation

Year: 2025

Type: conference-paper

Abstract: Introduction: Despite increased automation, manual heavy lifting remains a health risk for workers. Current assessment methods rely on subjective reports, lacking precision, or complex EMG laboratory methods unsuitable for large-scale workplace monitoring. However, in order to increase organizational/worker [for full text, please go to the a.m. URL]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25premus075

Authors: Jakobsen, Markus Due, Lohne, Fredrik, Kalvig, Peter, Andersen, Lars

Institution: National Research Centre for the Working Environment

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Decent work and economic growth

Open Access status: green

OpenAlex ID: W7080772320

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