This study was conducted in a warehouse at a vaccine production centre where improvement to existing storage and working conditions were sought through the construction of a new refrigerated store section (2-8C°). Warehousing tasks were videotaped and ergonomics analysis tools were used to assess the risk of developing MSDs. Specifically, these tools were the Rapid Entire Body Assessment (REBA) and the NIOSH equation. The current plant layout was sketched and analyzed to find possible targets for improvement trough the application of general work space design and ergonomics principles. Seven of the eight postures evaluated with REBA had a total score between 8 and 10, meaning a high risk, and only one was at a medium risk level. Nine of the eleven manual material handling tasks analyzed with the NIOSH equation had a Lifting Index between 1.14 and 1.80 and two had a recommended weight limit of 0 kg, indicating a need for job redesign. Solutions included the redesign of shelves, the design of a two-step stair and a trolley with adjustable height; also, changes in work methods were proposed by introducing a two-workers lifting strategy and job rotation, and, finally, a restructuring of plant layout was completed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/WOR-2012-0486-2487 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated vs manual cardiac MRI planning: a single-center prospective evaluation of reliability and scan times.
Eur Radiol
January 2025
Department of Radiology, Geneva University Hospitals, Geneva, Switzerland.
Objectives: Evaluating the impact of an AI-based automated cardiac MRI (CMR) planning software on procedure errors and scan times compared to manual planning alone.
Material And Methods: Consecutive patients undergoing non-stress CMR were prospectively enrolled at a single center (August 2023-February 2024) and randomized into manual, or automated scan execution using prototype software. Patients with pacemakers, targeted indications, or inability to consent were excluded.
An open-source, user-friendly, machine-learning method for automated segmentation and analysis of peripheral nerve cross-sections.
Plast Reconstr Surg
January 2025
Division of Plastic Surgery, Mayo Clinic; Rochester, MN.
Introduction: Quantitative neuromorphometry analysis of the peripheral nerve is paramount to nerve regeneration research. However, this technique relies upon accurate segmentation and determination of myelin and axonal area. Manual histological analysis methods are time- consuming, and subject to error and bias.
View Article and Find Full Text PDF[Comparison of facial asymmetry assessment by frontal teleradiography and 3D imaging].
Orthod Fr
January 2025
Service de Médecine bucco-dentaire, AP-HP, Hôpital Pitié Salpêtrière, 83 boulevard de l’Hôpital, 75013 Paris, France
Introduction: The diagnostic approach to facial asymmetries is classically based, in addition to the clinical examination, on a cephalometric analysis carried out on a frontal radiography. Planning of orthognathic surgery requires a three-dimensional (3D) examination to visualize deformities. Nevertheless, the use of cephalometric analysis on 3D imaging remains clinically modest.
View Article and Find Full Text PDFUtilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography.
Acta Radiol
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
Background: Segmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.
Purpose: To assess the utility of deep learning analysis in automatic segmentation of the cochleae in temporal bone CT to differentiate abnormal images from normal images.
Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.
Cancer Imaging
January 2025
Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!