工業進化 4.0 正在將重點轉移到通過智能自動化系統為工作場所的人類賦能和參與。 使用機器人是製造商提高靈活性和敏捷性的一種策略。 然而，由於任務的性質和目前的條件，不可能完全用機器人代替人類。 開發的協作場景應該適應人類的能力，同時最大限度地減少他們的工作量和運動無效性，以最大限度地減少人類疲勞。 此外，人與機器人之間的早期快速溝通對於了解人類在協作中的狀態並做出調整以獲得最佳性能至關重要。 這意味著了解人類如何看待協作工作中的變化至關重要。 捕獲人體生理數據以支持設備開發在可穿戴設備系統的靈活性和更大範圍的數據方面顯示出前景。 在這項研究中，開發了兩個場景，以使用生理反饋作為合作評估人類狀態的指標。 第一個場景是評估在人機協作中使用肌肉活動作為運動意圖的有效性。 第二種情況是使用人類心血管和外皮活動來評估人機協作中的腦力負荷。 本研究使用深度學習算法對人體運動意圖和感知心理負荷的生理反饋進行分類，平均準確率高達 90%。 結果表明，肌肉活動、心血管活動和外皮系統活動可有效評估人在意向和腦力負荷方面的狀態。 因此，操作員感知到的運動意圖和腦力負荷可以用來支持操作員4.0的發展。

Industrial evolution 4.0 is shifting the focus to empower and engage human in the workplace with smart automation systems. Utilizing robots is one strategy for manufacturers to improve their flexibility and agility. However, due to the character of the task and present conditions, it is not possible to completely replace human with robots. A collaboration scenario that is developed should accommodate human abilities while minimizing their workloads and motion ineffectiveness. Furthermore, early and quick communication between humans and robots is essential to understand the human's status in collaboration and making adjustments for optimum performance. This implies that it is the importance crucial to of understanding how humans perceive changes in collaborative work. Capturing human physiological data in support of device development has shown promise in terms of flexibility and a greater scope of data in wearable device systems. In this study, two scenarios were developed to use physiological feedback as an indicator to evaluate human status in collaboration. The first scenario is to evaluate the effectiveness of using muscular activities for the motion intention in the Human-robot collaboration. The second scenario is using human cardiovascular and integumentary activities in evaluating the mental workload in Human-robot collaboration. To evaluate the effectiveness of the scenario, this study used a deep learning algorithm to classify the physiological feedback for human motion intention and perceived mental workload accuracy up to 90% on average. The result shows that muscular activities, cardiovascular activities, and integumentary activities are effective at evaluating human status in terms of intention and mental workload. Thus, the motion intention and mental workload perceived by the operator can be used to support the development of operator 4.0.

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