Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 28th International Symposium on Computer-Based Medical Systems, CBMS 2015 |
| Editors | Caetano Traina, Pedro Pereira Rodrigues, Bridget Kane, Paulo Mazzoncini de Azevedo-Marques, Agma Juci Machado Traina |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 364-365 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781467367752 |
| DOIs | |
| Publication status | Published - Jun 2015 |
| Event | 28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 - Sao Carlos and Ribeirao Preto, Brazil Duration: 22 Jun 2015 → 25 Jun 2015 |
Publication series
| Name | Proceedings - IEEE Symposium on Computer-Based Medical Systems |
|---|---|
| Volume | 2015-July |
| ISSN (Print) | 1063-7125 |
Conference
| Conference | 28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 |
|---|---|
| Country/Territory | Brazil |
| City | Sao Carlos and Ribeirao Preto |
| Period | 22/06/15 → 25/06/15 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Human Mental Workload
- Human-Computer Interaction
- Interactive Systems
- Machine Learning
- Medical applications
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