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首页> 外文会议>International Congress on Human-Computer Interaction, Optimization and Robotic Applications >A Data-Driven Fatigue Prediction using Recurrent Neural Networks
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A Data-Driven Fatigue Prediction using Recurrent Neural Networks

机译：使用经常性神经网络的数据驱动疲劳预测

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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).
机译：工业革命4.0标志着机器和人类之间互动的互动时代，培养自动化。然而，制造业仍然具有对人类的劳动密集，具有许多重复行为的任务。这些行动以及其他因素可能导致工人疲劳或疲惫不堪。这些长期可以发展成与工作相关的肌肉骨骼疾病（WMSD）。然而，由于涉及数据收集所涉及的受试者的异质性，以可量化和客观的方式理解疲劳是一个公开的问题。在这研究包括物理疲劳属性的基准数据集。它们用于对手动材料处理任务进行疲劳预测。它包括从惯性测量单元（IMU）收集的数据和心率（HR）传感器，然后预处理以提取以用于运行模型。数据用作调用作为经常性神经网络（RNN）的时间序列预测模型的输入。

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