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• W4309199389 abstract "The development of the industry phase from 4.0 into 5.0 is shifting the focus from technology-driven to value-driven to empower and engage humans in the work environment with greater influence and responsibility. This means that it is necessary to know how humans perceive collaborative work, which resulted from a shift from traditional work. Capturing the physiological conditions of humans in support of device development has shown promise in terms of the flexibility and broader scope of data in the wearable device system. In this study, the human mental workload is evaluated via physiological feedback using wearable sensors in a quantitative approach to recognize the human workload perception in the Human–robot collaboration (HRC). The physiological feedback is used to evaluate the mental workload with a classification approach utilizing supervised machine learning algorithms. The results show that, by using a random forest algorithm which selects features according to information gain, the accuracy of classifying the mental workload can be as high as 94% in the testing phase. Thus, the mental workload perceived by the operator can be estimated from physiological parameters. This finding can support Industry 5.0 applications in human–robot collaboration with a human-centric approach." @default.
• W4309199389 created "2022-11-24" @default.
• W4309199389 creator A5044722744 @default.
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• W4309199389 date "2022-10-01" @default.
• W4309199389 modified "2023-10-16" @default.
• W4309199389 title "Classification of mental workload in Human-robot collaboration using machine learning based on physiological feedback" @default.
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• W4309199389 doi "https://doi.org/10.1016/j.jmsy.2022.10.017" @default.
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