Suitability of Inexpensive Eye-Tracking Device for User Experience Evaluations

Research hotspots and trends analysis of user experience: Knowledge maps visualization and theoretical framework construction

This study focuses on user experience from the perspective of big data to complete the aggregation, clustering, and visual presentation of knowledge. Using a combination of sample literature review, visualization technologies, knowledge map analysis, Carrot2 clustering, and other methodologies, this study intends to examine user experience from three perspectives: research state, hotspots, and trends. First, based on the double-map overlay, core institutions, core countries, core authors, core journals, and core references distribution research, the knowledge flow, research power, and research subjects of user experience are analyzed. Secondly, through keyword clustering analysis, this research intuitively presents the research topics of user experience and reveals the research hotspots and the evolution path of research methods. Finally, with the help of the subject clustering algorithm, the emerging trends of user experience research are predicted: the immersive experience upgrade of multi-scenario integration, the innovative design of multi-role collaboration, and the cross-disciplinary interactive exploration of multi-discipline. Following this, the user experience knowledge map is constructed, providing a global view and macro-cognition for subsequent research.

Are UX Evaluation Methods Providing the Same Big Picture?

The success of a software application is related to users’ willingness to keep using it. In this sense, evaluating User eXperience (UX) became an important part of the software development process. Researchers have been carrying out studies by employing various methods to evaluate the UX of software products. Some studies reported varied and even contradictory results when applying different UX evaluation methods, making it difficult for practitioners to identify which results to rely upon. However, these works did not evaluate the developers’ perspectives and their impacts on the decision process. Moreover, such studies focused on one-shot evaluations, which cannot assess whether the methods provide the same big picture of the experience (i.e., deteriorating, improving, or stable). This paper presents a longitudinal study in which 68 students evaluated the UX of an online judge system by employing AttrakDiff, UEQ, and Sentence Completion methods at three moments along a semester. This study reveals contrasting results between the methods, which affected developers’ decisions and interpretations. With this work, we intend to draw the HCI community’s attention to the contrast between different UX evaluation methods and the impact of their outcomes in the software development process.

Eye tracking metrics to screen and assess cognitive impairment in patients with neurological disorders

PURPOSE OF REVIEW

Eye tracking is a powerful method to investigate the relationship between behavior and neural mechanisms. In recent years, eye movement analysis has been used in patients with neurological disorders to assess cognitive function. In this review, we explore the latest eye tracking researches in neurological disorders that are commonly associated with cognitive deficits, specifically, amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and epilepsy. We focus on the application of ocular measures in these disorders, with the goal of understanding how eye tracking technology can be used in the clinical setting.

FINDINGS

Eye tracking tasks (especially saccadic tasks) are often used as an adjunct to traditional scales for cognitive assessment. Eye tracking data confirmed that executive dysfunction is common in PD and ALS, whereas AD and MS are characterized by attention deficits. Research in evaluating cognitive function in epilepsy using eye tracking is still in its early stages, but this approach has shown advantages as a sensitive quantitative method with high temporal and spatial resolution. Eye tracking technology can facilitate the assessment of cognitive impairment with higher temporal resolution and finer granularity than traditional cognitive assessment. Oculomotor data collected during cognitive tasks can provide insight into biological processes. Eye tracking provides a nonverbal and less cognitively demanding method of measuring disease progression in cognitively impaired patients.

A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools.