A Classification Model for Human Error in Collaborative Systems The primary focus of the work reported in this thesis is to investigate and provide a means by which the occurrence of human error in collaborative systems can be better understood. The thesis suggests that much can be gained from looking at human error from a collaborative perspective as opposed to more traditional cognitive and b...

exploiting multimodal information like acceleration and heart rate is a promising method to achieve human action recognition. a semi-supervised action recognition approach aucc (action understanding with combinational classifier) using the diversity of base classifiers to create a high-quality ensemble for multimodal human action recognition is proposed in this paper. furthermore, both labeled ...

Error classification methods are used throughout aviation to help understand and mitigate the causes of human error. Many assumptions underlying error classification, however, remain untested. For example, "error" is taken to mean different things, even within individual methods; and a close mapping is uncritically presumed between the quantity measured (errors) and the quality managed (safety)...

More than 60 years has passed since the installation of first robot in an industrial context. Since then, robotics seen great advancements and, today, robots can collaborate with humans executing a wide range working activities. Nevertheless, impact on human operators not been deeply investigated. To address this problem, we conducted empirical study to measure errors performed by two groups pe...

introduction: medication errors are among the most serious health errors threatening patient safety in all countries, with major impacts on public health. midwives as members of healthcare systems are prone to such errors. therefore, in this study, we aimed to determine medication errors and the contributing factors among midwives working in maternity units of mashhad university of medical scie...