The Influence of Gender on Performance in Gaming Environment with Different Typographic Factors

Sohail, Shanzeh; Syed, Adeel M.; Jamil, Abid

doi:10.2139/ssrn.3680706

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Another study investigates the relationship of gender with various typographic attributes while interacting with a gaming prototype. The results revealed major disparities between men and women [17]. More specifically, Jiang Shao et al have investigated whether personal protective equipment (PPE) impacts legibility.…”

Section: Literature Reviewmentioning

confidence: 99%

The Influence of Typeface and Case on Critical Care Systems Performance (Medical Ventilator)

Abrar

Faisal

Ahmad

et al. 2023

ACE

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Usability evaluation is a basic tool for investigating a system's performance in different environments. Many studies found a positive influence of different user interface aesthetics and their organization on usability ratings. These design aspects are one of the most important characteristics of systems usability, and few empirical studies have been examining the importance of typography specifically for safety-critical systems. This study aimed to investigate how different typographic factors, i.e., typeface and fontcase, influence a user's performance in health care safety-critical systems. Different study variables were considered, including task completion time, reflection time, and the total number of touches. To test the study objectives, the Hamilton-c6 ventilator prototype was developed. The experiments were conducted on 15 participants, including male and female physicians, in a controlled environment from different healthcare venues. A well-known technique, one-way ANOVA, was used to assess the data. The results suggest a significant difference between serif (times new roman) and sans serif (Calibri) groups for task completion time; in addition to that, the difference between fontcase groups (sentence case, lowercase, and uppercase) was not statistically significant. Moreover, this study investigated the pairwise influence of different typeface and fontcase groups. The results show a significant difference in task completion time. The descriptive analysis of different groups shows that the times new roman with the upper case group has the lowest task completion time while the Calibri with the uppercase groups have the highest task completion time. This study may pave a good research direction to explore the impact of these and other aesthetic factors in multiple contexts.

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Section: Literature Reviewmentioning

confidence: 99%

The Influence of Typeface and Case on Critical Care Systems Performance (Medical Ventilator)

Abrar

Faisal

Ahmad

et al. 2023

ACE

View full text Add to dashboard Cite

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Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices

Guarino

Lettieri²,

Malandrino

et al. 2022

Neural Comput & Applic

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Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies: single-view learning, analyzing, one at a time, datasets relating to a single type of gesture, and multi-view learning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show that scroll down is the most useful gesture and random forest is the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification.

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The Influence of Gender on Performance in Gaming Environment with Different Typographic Factors

Cited by 2 publications

References 14 publications

The Influence of Typeface and Case on Critical Care Systems Performance (Medical Ventilator)

The Influence of Typeface and Case on Critical Care Systems Performance (Medical Ventilator)

Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices

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