Document Type

Conference Paper


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Computer Sciences

Publication Details

CBMS '15 Proceedings of the 2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pages 364-365 IEEE Computer Society Washington, DC, USA ©2015 table of contents ISBN: 978-1-4673-6775-2 doi>10.1109/CBMS.2015.67


In clinical settings, Human-computer systems need to be designed in a way that medical errors are reduced and patient care is enhanced. Inspection methods are usually employed in HCI to assess usability of interactive systems. However, they do not consider the state of the operator while executing a task, the surrounding environment and the task demands. It is argued that assessing performance of operators is fundamental for designing optimal systems with which healthcare can be effectively delivered. The aim of our solution is to assess performance of operators employing the notion of Mental Workload (MWL) this being a construct believed to strongly correlate with performance. The proposal is to develop a model for MWL assessment using supervised machine learning. This model will be evaluated via user studies involving clinicians and operators interacting with a set of medical systems. Assessments of MWL will be compared and validated with objective indexes of performance such as error rate and task execution time.