Center for Cognitive Interaction Technology (CITEC), Neurocognition and Action Group, Bielefeld University, 33615, Bielefeld, Germany.
Sci Rep. 2021 May 4;11(1):9473. doi: 10.1038/s41598-021-88921-1.
The majority of manufacturing tasks are still performed by human workers, and this will probably continue to be the case in many industry 4.0 settings that aim at highly customized products and small lot sizes. Technical systems could assist on-the-job training and execution of these manual assembly processes, using augmented reality and other means, by properly treating and supporting workers' cognitive resources. Recent algorithmic advancements automatized the assessment of task-related mental representation structures based on SDA-M, which enables technical systems to anticipate mistakes and provide corresponding user-specific assistance. Two studies have empirically investigated the relations between algorithmic assessments of individual memory structures and the occurrences of human errors in different assembly tasks. Hereby theoretical assumptions of the automatized SDA-M assessment approaches were deliberately violated in realistic ways to evaluate the practical applicability of these approaches. Substantial but imperfect correspondences were found between task-related mental representation structures and actual performances with sensitivity and specificity values ranging from 0.63 to 0.72, accompanied by prediction accuracies that were highly significant above chance level.
大多数制造任务仍然由人工完成，在许多旨在生产高度定制化产品和小批量产品的工业 4.0 环境中，这种情况可能仍将继续。技术系统可以通过使用增强现实和其他手段，正确处理和支持工人的认知资源，来协助在职培训和执行这些手动装配过程。最近的算法进步使基于 SDA-M 的任务相关心理表现结构的评估自动化，这使技术系统能够预测错误并提供相应的特定于用户的帮助。两项研究基于 SDA-M 评估方法的理论假设，以现实的方式故意违反，以评估这些方法的实际适用性，对不同装配任务中个体记忆结构的算法评估与人为错误的发生之间的关系进行了实证研究。结果发现，与任务相关的心理表现结构与实际表现之间存在一定程度的对应关系，但并不完全对应，其敏感性和特异性值在 0.63 到 0.72 之间，预测准确率明显高于随机水平。
