A Study for Human-Machine Interface Design of Spacecraft Display &amp; Control Device Based on Eye-Tracking Experiments

Guo, Qi; Xue, Chengqi; Lin, Yun; Niu, Yafeng; Chen, Mo

doi:10.1007/978-3-319-58475-1_16

Cited by 4 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples are web and print advertisements [ 23 , 24 ] and graphical representations like x-ray images of patients [ 25 ]. More complex subjects of the investigations include graphical UIs such as computer tomography interfaces [ 26 ] or spacecraft displays [ 27 , 28 ]. Further, there are single studies where eye tracking is used to evaluate highly interactive UIs of tangible products like smart TVs [ 29 ], smartwatches [ 12 , 21 ], or medical devices [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

Wegner¹,

Lohmeyer²,

Wahlen³

et al. 2020

JMIR Hum Factors

View full text Add to dashboard Cite

Background In order to give a wide range of people the opportunity to ensure and support home care, one approach is to develop medical devices that are as user-friendly as possible. This allows nonexperts to use medical devices that were originally too complicated to use. For a user-centric development of such medical devices, it is essential to understand which user interface design best supports patients, caregivers, and health care professionals. Objective Using the benefits of mobile eye tracking, this work aims to gain a deeper understanding of the challenges of user cognition. As a consequence, its goal is to identify the obstacles to the usability of the features of two different designs of a single medical device user interface. The medical device is a patient assistance device for home use in peritoneal dialysis therapy. Methods A total of 16 participants, with a subset of seniors (8/16, mean age 73.7 years) and young adults (8/16, mean age 25.0 years), were recruited and participated in this study. The handling cycle consisted of seven main tasks. Data analysis started with the analysis of task effectiveness for searching for error-related tasks. Subsequently, the in-depth gaze data analysis focused on these identified critical tasks. In order to understand the challenges of user cognition in critical tasks, gaze data were analyzed with respect to individual user interface features of the medical device system. Therefore, it focused on the two dimensions of dwell time and fixation duration of the gaze. Results In total, 97% of the handling steps for design 1 and 96% for design 2 were performed correctly, with the main challenges being task 1 insert, task 2 connect, and task 6 disconnect for both designs. In order to understand the two analyzed dimensions of the physiological measurements simultaneously, the authors propose a new graphical representation. It distinguishes four different patterns to compare the eye movements associated with the two designs. The patterns identified for the critical tasks are consistent with the results of the task performance. Conclusions This study showed that mobile eye tracking provides insights into information processing in intensive handling tasks related to individual user interface features. The evaluation of each feature of the user interface promises an optimal design by combining the best found features. In this way, manufacturers are able to develop products that can be used by untrained people without prior knowledge. This would allow home care to be provided not only by highly qualified nurses and caregivers, but also by patients themselves, partners, children, or neighbors.

show abstract

Section: Introductionmentioning

confidence: 99%

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

Wegner¹,

Lohmeyer²,

Wahlen³

et al. 2020

JMIR Hum Factors

View full text Add to dashboard Cite

show abstract

Improvement of Spinning Machine Monitoring Interface Based on Eye Tracking Experiment

Zhou

Zhang

Jia

2021

Advances in Ergonomics in Design

View full text Add to dashboard Cite

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design (Preprint)

Wegner¹,

Lohmeyer²,

Wahlen³

et al. 2019

Preprint

View full text Add to dashboard Cite

BACKGROUND In order to give a wide range of people the opportunity to ensure and support home care, one approach is to develop medical devices that are as user-friendly as possible. This allows nonexperts to use medical devices that were originally too complicated to use. For a user-centric development of such medical devices, it is essential to understand which user interface design best supports patients, caregivers, and health care professionals. OBJECTIVE Using the benefits of mobile eye tracking, this work aims to gain a deeper understanding of the challenges of user cognition. As a consequence, its goal is to identify the obstacles to the usability of the features of two different designs of a single medical device user interface. The medical device is a patient assistance device for home use in peritoneal dialysis therapy. METHODS A total of 16 participants, with a subset of seniors (8/16, mean age 73.7 years) and young adults (8/16, mean age 25.0 years), were recruited and participated in this study. The handling cycle consisted of seven main tasks. Data analysis started with the analysis of task effectiveness for searching for error-related tasks. Subsequently, the in-depth gaze data analysis focused on these identified critical tasks. In order to understand the challenges of user cognition in critical tasks, gaze data were analyzed with respect to individual user interface features of the medical device system. Therefore, it focused on the two dimensions of dwell time and fixation duration of the gaze. RESULTS In total, 97% of the handling steps for design 1 and 96% for design 2 were performed correctly, with the main challenges being task 1 insert, task 2 connect, and task 6 disconnect for both designs. In order to understand the two analyzed dimensions of the physiological measurements simultaneously, the authors propose a new graphical representation. It distinguishes four different patterns to compare the eye movements associated with the two designs. The patterns identified for the critical tasks are consistent with the results of the task performance. CONCLUSIONS This study showed that mobile eye tracking provides insights into information processing in intensive handling tasks related to individual user interface features. The evaluation of each feature of the user interface promises an optimal design by combining the best found features. In this way, manufacturers are able to develop products that can be used by untrained people without prior knowledge. This would allow home care to be provided not only by highly qualified nurses and caregivers, but also by patients themselves, partners, children, or neighbors.

show abstract

A Study for Human-Machine Interface Design of Spacecraft Display & Control Device Based on Eye-Tracking Experiments

Cited by 4 publications

References 2 publications

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

Improvement of Spinning Machine Monitoring Interface Based on Eye Tracking Experiment

Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design (Preprint)

Contact Info

Product

Resources

About