Field Study on the Application of a Simulation-Based Software Tool for the Strain-Based Staffing in Industrial Manufacturing

Gust, Peter; Müller, Ulf; Feller, Nico; Schiffmann, Michael

doi:10.1007/978-3-319-41627-4_1

Cited by 2 publications

(1 citation statement)

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“…These effects are due to the nerve, ligament, muscle, joints and spinal injuries in the long term work. Under I4.0 workplace automation has increased but interestingly the workload and the fatigue induced has also increased in the workers [7]. Many studies show that tasks with labor instensive physical exertion cause WMSD.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Fatigue Prediction using Recurrent Neural Networks

Lambay

Liu

Morgan

et al. 2021

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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Industrial revolution 4.0 has marked the era of advances in interaction among machines and humans and cultivate automation. However, manufacturing industries still have tasks which are labor intensive for humans with lots of repetitive actions. These actions along with other factors can cause the worker to be fatigued or exhausted. These in the long term can develop into work-related musculoskeletal disorders (WMSD). Nevertheless, comprehending fatigue in a quantifiable and objective manner is yet an open problem due to the heterogeneity of subjects involved for data collection. In this study a benchmarking dataset comprising of physical fatigue attributes. They are used to perform fatigue prediction for manual material handling task. It includes data collected from Inertial Measurement unit (IMU) and Heart Rate (HR) sensor which is then pre-processed to extract to be used to run the model. The data serves as an input to a time-series prediction model called as Recurrent Neural Network (RNN).

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Section: Introductionmentioning

confidence: 99%