Lift It Up Right

A Recommender System for Safer Lifting Postures

Conference Paper (2025)
Author(s)

Gaetano Dibenedetto (Università degli Studi di Bari Aldo Moro)

Pasquale Lops (Università degli Studi di Bari Aldo Moro)

Marco Polignano (Università degli Studi di Bari Aldo Moro)

H. Torkamaan (TU Delft - System Engineering)

Research Group
System Engineering
DOI related publication
https://doi.org/10.1145/3705328.3759314
More Info
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Publication Year
2025
Language
English
Research Group
System Engineering
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
1222-1227
ISBN (electronic)
9798400713644
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety. This paper presents a health recommender system designed to prevent injury by assessing and correcting posture for lifting techniques. Leveraging monocular video input, our method estimates key ergonomic parameters to compute the Lifting Index based on the Revised NIOSH Lifting Equation. When the computed Lifting Index exceeds a predefined safety threshold, the system automatically generates graphical and textual recommendations to guide the worker towards safer postural strategies. This safety-aware recommender system provides interpretable and actionable feedback without requiring wearable sensors or multi-camera setups, making it suitable for deployment in real-world workplace environments. By integrating ergonomics with recommender system design, we contribute to a new class of context-aware, safety-oriented recommendation technologies tailored for occupational health.

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