An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions, and initialized by a priori domain knowledge and preprocessed public datasets. The system defines a causal and temporal mapping of worker and machine states where one side can label the other automatically. A case study in machines with different automation levels is conducted in a multiuser facility by using finite state machine representations for machine energy states, worker activity states, and interaction transition functions. The fully automated adaptive ML system improves its accuracy for human machine interaction detection by up to 12.5% and shows potential to recognize more fine-grained actions.

中文翻译：

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用于制造的交互式和自适应学习网络物理人体系统以及工人机器交互的案例研究


概念化、设计和实现了基于自适应机器学习 (ML) 的智能制造交互式网络物理人体系统 (ICPHS)。其重要属性之一是机器学习模型，在部署过程中，该模型会以自标记方式随流数据进行自我演化。这种自动化自适应机器学习系统是通过利用人机交互过程中的潜在因果关系来实现的，并通过先验领域知识和预处理的公共数据集进行初始化。该系统定义了工人和机器状态的因果和时间映射，其中一侧可以自动标记另一侧。通过使用机器能量状态、工人活动状态和交互转换函数的有限状态机表示，在多用户设施中对具有不同自动化级别的机器进行案例研究。全自动自适应机器学习系统将人机交互检测的准确性提高了高达 12.5%，并显示出识别更细粒度动作的潜力。