Hasanain Bassam, Boyd Andrew D, Edworthy Judy, Bolton Matthew L
University of Illinois at Chicago, Department of Mechanical and Industrial Engineering, Chicago, IL, USA.
University of Illinois at Chicago, Department of Biomedical and Health Information Sciences, Chicago, IL, USA.
Appl Ergon. 2017 Jan;58:500-514. doi: 10.1016/j.apergo.2016.07.008. Epub 2016 Aug 29.
The failure of humans to respond to auditory medical alarms has resulted in numerous patient injuries and deaths and is thus a major safety concern. A relatively understudied source of response failures has to do with simultaneous masking, a condition where concurrent sounds interact in ways that make one or more of them imperceptible due to physical limitations of human perception. This paper presents a method, which builds on a previous implementation, that uses a novel combination of psychophysical modeling and formal verification with model checking to detect masking in a modeled configuration of medical alarms. Specifically, the new method discussed here improves the original method by adding the ability to detect additive masking while concurrently improving method usability and scalability. This paper describes how these additions to our method were realized. It then demonstrates the scalability and detection improvements via three different case studies. Results and future research are discussed.
人类对听觉医疗警报无反应已导致众多患者受伤和死亡，因此成为一个重大安全问题。一个相对较少被研究的反应失败源与同时掩蔽有关，即并发声音以某种方式相互作用，由于人类感知的生理限制使得其中一个或多个声音无法被察觉。本文提出了一种基于先前实现的方法，该方法使用心理物理建模和形式验证与模型检查的新颖组合，以在医疗警报的建模配置中检测掩蔽。具体而言，这里讨论的新方法通过增加检测相加掩蔽的能力，同时提高方法的可用性和可扩展性，对原始方法进行了改进。本文描述了这些对我们方法的添加是如何实现的。然后通过三个不同的案例研究展示了可扩展性和检测改进。讨论了结果和未来研究。
