Upper-Limb and Low-Back Load Analysis in Workers Performing an Actual Industrial Use-Case with and without a Dual-Arm Collaborative Robot DOI Creative Commons

Alessio Silvetti,

Tiwana Varrecchia, Giorgia Chini

и другие.

Safety, Год журнала: 2024, Номер 10(3), С. 78 - 78

Опубликована: Сен. 11, 2024

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of ISO 11228 series commonly used for biomechanical risk assessments cannot be applied 4.0, as they do not involve interactions between HRC technologies. use wearable sensor networks software could us develop more reliable idea about effectiveness collaborative robots (coBots) reducing load workers. aim present study was investigate some parameters with 3D Static Strength Prediction Program (3DSSPP) v.7.1.3, on executing practical manual material-handling task, by comparing dual-arm coBot-assisted scenario no-coBot scenario. this study, we calculated mean standard deviation (SD) values from eleven participants 3DSSPP parameters. We considered following parameters: percentage maximum voluntary contraction (%MVC), allowed static exertion time (MaxST), low-back spine compression forces at L4/L5 level (L4Ort), strength percent capable value (SPC). advantages introducing coBot, according our statistics, concerned trunk flexion (SPC 85.8% without coBot 95.2%; %MVC 63.5% 43.4%; MaxST 33.9 s 86.2 s), left shoulder abdo-adduction (%MVC 46.1% 32.6%; 32.7 65 right 43.9% 30.0%; 37.2 70.7 s) Phase 1, humeral rotation 68.4% 7.4%; 873.0 125.2 31.0% 18.3%; 60.3 183.6 wrist flexion/extension 50.2% 3.0%; 58.8 1200.0 2. Moreover, 3, which consisted another handling would removed using coBot. summary, industrial workers, particularly trunk, both shoulders, wrist. Finally, an easy, fast, costless tool where applied; it occupational medicine physicians health safety technicians, also employers justify long-term investment.

Язык: Английский

The Effectiveness of Problem‐Based Learning in Reducing Work‐Related Musculoskeletal Problems Among Hospital Nurses: An Interventional Study DOI Creative Commons

Shahrzad Modarresi,

Tayebeh Rakhshani, Zahra Mehri

и другие.

Nursing Forum, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Background: Musculoskeletal disorders (MSDs) are common among nursing professionals and require effective management strategies. Implementing targeted training programs for nurses is a vital approach to reduce these problems. Aim: This study aimed evaluate problem‐based learning (PBL) effectiveness in reducing work‐related musculoskeletal symptoms (WMSs) nurses. Methods: Fifty Iranian participated this interventional study. Some data were collected by demographic/occupational questionnaire Persian version of the Nordic Questionnaire (P‐NMQ). other was gathered Rapid Entire Body Assessment (REBA) PBL method. Results: The prevalence WMSs during last 12 months subjects related lower back (76%), wrists/hands (70%), neck (64%), knee respectively. results showed that decreased significantly only subjects’ elbow region postintervention ( p = 0.031). Although stage regions, decrease not statistically significant > 0.05). Conclusions: implementation could level nurses’ exposure risk factors MSDs.

Язык: Английский

Процитировано

1
NLP-based ergonomics MSD risk root cause analysis and risk controls recommendation DOI Creative Commons
Pulkit Parikh, Julia Penfield, Richard Barker

и другие.

Ergonomics, Год журнала: 2024, Номер unknown, С. 1 - 13

Опубликована: Авг. 27, 2024

An ergonomics assessment of the physical risk factors in workplace is instrumental predicting and preventing musculoskeletal disorders (MSDs). Using Artificial Intelligence (AI) has become increasingly popular for assessments because time savings improved accuracy. However, most effort this area starts ends with producing scores, without providing guidance to reduce risk. This paper proposes a holistic job improvement process that performs automatic root cause analysis control recommendations reducing MSD We apply deep learning-based Natural Language Processing (NLP) techniques such as Part Speech (PoS) tagging dependency parsing on textual descriptions actions performed (e.g. pushing) along object cart) being acted upon. The action-object inferences provide entry point an expert-based Machine Learning (ML) system automatically identifies targeted work-related causes cart movement forces are too high, due caster size small) identified excessive shoulder forces). proposed framework utilises recommend strategies larger diameter casters, minimum 8" or 203 mm) likely mitigate risk, resulting more efficient effective process.

Язык: Английский

Процитировано

0
Investigating the Relationship Between Environmental and Cognitive Ergonomics with Work-Related Musculoskeletal Disorders: A Case Study in an Automobile Industry DOI
Nasrin ASADI, Mohsen Sadeghi‐Yarandi

Work, Год журнала: 2024, Номер unknown, С. 1 - 16

Опубликована: Сен. 3, 2024

BACKGROUND: Cognitive and environmental parameters are among the most important influencing factors in prevalence of WRMSDs, which have been studied less compared to physical ergonomic automobile industry. OBJECTIVE: This study was conducted with aim investigating relationship between cognitive ergonomics WRMSDs an automotive METHODS: 2023 company. The sample size 740 workers. assessed using Cornell Musculoskeletal Discomfort Questionnaire. Occupational stress, mental workload, sleep quality, failure were by Job Content Questionnaire, NASA-TLX Pittsburgh Sleep Quality Index, Failure respectively. Noise measured KIMO-DB300 sound analyzer. intensity lighting a Hanger Screen Master illuminance meter. Heat stress Wet Bulb Globe Temperature (WBGT). RESULTS: 72.58% reported musculoskeletal disorders at least one their body parts during past 12 months. average values occupational workers higher than participants without (p-value < 0.05). There significant difference all harmful two investigated groups, except thermal CONCLUSION: Findings from this highlight critical need for holistic approach that considers both external work environment internal processes effectively prevent manage industry

Язык: Английский

Процитировано

0
Upper-Limb and Low-Back Load Analysis in Workers Performing an Actual Industrial Use-Case with and without a Dual-Arm Collaborative Robot DOI Creative Commons

Alessio Silvetti,

Tiwana Varrecchia, Giorgia Chini

и другие.

Safety, Год журнала: 2024, Номер 10(3), С. 78 - 78

Опубликована: Сен. 11, 2024

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of ISO 11228 series commonly used for biomechanical risk assessments cannot be applied 4.0, as they do not involve interactions between HRC technologies. use wearable sensor networks software could us develop more reliable idea about effectiveness collaborative robots (coBots) reducing load workers. aim present study was investigate some parameters with 3D Static Strength Prediction Program (3DSSPP) v.7.1.3, on executing practical manual material-handling task, by comparing dual-arm coBot-assisted scenario no-coBot scenario. this study, we calculated mean standard deviation (SD) values from eleven participants 3DSSPP parameters. We considered following parameters: percentage maximum voluntary contraction (%MVC), allowed static exertion time (MaxST), low-back spine compression forces at L4/L5 level (L4Ort), strength percent capable value (SPC). advantages introducing coBot, according our statistics, concerned trunk flexion (SPC 85.8% without coBot 95.2%; %MVC 63.5% 43.4%; MaxST 33.9 s 86.2 s), left shoulder abdo-adduction (%MVC 46.1% 32.6%; 32.7 65 right 43.9% 30.0%; 37.2 70.7 s) Phase 1, humeral rotation 68.4% 7.4%; 873.0 125.2 31.0% 18.3%; 60.3 183.6 wrist flexion/extension 50.2% 3.0%; 58.8 1200.0 2. Moreover, 3, which consisted another handling would removed using coBot. summary, industrial workers, particularly trunk, both shoulders, wrist. Finally, an easy, fast, costless tool where applied; it occupational medicine physicians health safety technicians, also employers justify long-term investment.

Язык: Английский

Процитировано

0