Comparison Semiautomatic NIOSH Lifting Equation: AzKNIOSH versus RGB-based Machine Vision Algorithm
Contributo in Atti di convegno
Data di Pubblicazione:
2024
Citazione:
Comparison Semiautomatic NIOSH
Lifting Equation: AzKNIOSH versus RGB-based Machine Vision Algorithm / Forgione, Chiara; Coruzzolo, Antonio Maria; Lolli, Francesco; Balugani, Elia. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - (2024). ( XXIX Summer School “Francesco Turco” – «Sustainability and resilience in industrial systems across the era of digitalization» Otranto, Italia 11/09/2024 - 13/09/2024).
Abstract:
Work related musculoskeletal disorders (WMDs) have a significant impact on industrial productivity and society. With the advent of Industry 5.0, the safety and well-being of human operators are back to being crucial for each modern production system. In this context, many innovative technologies have been developed for ergonomic purposes. Motion Capture (MOCAP) technologies are applied to semi automatically calculate the ergonomic risk in a faster and less expensive way. In the other hand, the usage of MOCAP is not always recommended and data collection with common devices is preferred in industrial environment. For this scope, we compared the effectiveness of a commercial machine vision algorithm (ErgoEdge) based on RGB camera against a developed application based on the depth camera Microsoft Azure Kinect (AzKNIOSH) for NIOSH Lifting Equation computation. Fifty-two tasks in which volunteers performed manual handling of loads were evaluated with both systems, showing a good agreement.
Tipologia CRIS:
Relazione in Atti di Convegno
Keywords:
Depth Camera; Ergonomics; Kinect; Machine Vision; Picking;
Elenco autori:
Forgione, Chiara; Coruzzolo, Antonio Maria; Lolli, Francesco; Balugani, Elia
Link alla scheda completa:
Titolo del libro:
Proceedings of the 29th Summer School Francesco Turco
Pubblicato in: