Multimodal Technologies and Interaction

21 pages, 27048 KB

Open AccessArticle

Evaluating Rich Visual Feedback on Head-Up Displays for In-Vehicle Voice Assistants: A User Study

by Mahmoud Baghdadi, Dilara Samad-Zada and Achim Ebert

Multimodal Technol. Interact. 2025, 9(11), 114; https://doi.org/10.3390/mti9110114 - 16 Nov 2025

Viewed by 319

In-vehicle voice assistants face usability challenges due to limitations in delivering feedback within the constraints of the driving environment. The presented study explores the potential of Rich Visual Feedback (RVF) on Head-Up Displays (HUDs) as a multimodal solution to enhance system usability. A user study with 32 participants evaluated three HUD User Interface (UI) designs: the AR Fusion UI, which integrates augmented reality elements for layered, dynamic information presentation; the Baseline UI, which displays only essential keywords; and the Flat Fusion UI, which uses conventional vertical scrolling. To explore HUD interface principles and inform future HUD design without relying on specific hardware, a simulated near-field overlay was used. Usability was measured using the System Usability Scale (SUS), and distraction was assessed with a penalty point method. Results show that RVF on the HUD significantly influences usability, with both content quantity and presentation style affecting outcomes. The minimal Baseline UI achieved the highest overall usability. However, among the two Fusion designs, the AR-based layered information mechanism outperformed the flat scrolling method. Distraction effects were not statistically significant, indicating the need for further research. These findings suggest RVF-enabled HUDs can enhance in-vehicle voice assistant usability, potentially contributing to safer, more efficient driving. Full article

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18 pages, 3035 KB

Open AccessArticle

A Multi-Institution Mixed Methods Analysis of a Novel Acid-Base Mnemonic Algorithm

by Camille Massaad, Harrison Howe, Meize Guo and Tyler Bland

Multimodal Technol. Interact. 2025, 9(11), 113; https://doi.org/10.3390/mti9110113 - 11 Nov 2025

Viewed by 412

Abstract

Acid-base analysis is a high-load diagnostic skill that many medical students struggle to master when taught using traditional text-based flowcharts. This multi-institution mixed-methods study evaluated a novel visual mnemonic algorithm that integrated Medimon characters, symbolic imagery, and pop-culture references into the standard acid-base diagnostic framework. First-year medical students (n = 273) at six distributed WWAMI campuses attended an identical lecture on acid-base physiology. Students at five control campuses received the original text-based algorithm, while students at one experimental campus received the Medimon algorithm in addition. Achievement was measured with a unit exam (nine focal items, day 7) and a final exam (four focal items, day 11). A Differences-in-Differences approach compared performance on focal items versus baseline items across sites. Students at the experimental campus showed no significant advantage on the unit exam (DiD = +1.2%, g = 0.12) but demonstrated a larger, but still non-significant, medium-to-large effect on the final exam (DiD = +11.0%, g = 0.85). At the experimental site, 39 students completed the Situational Interest Survey for Multimedia (SIS-M), revealing significantly higher triggered, maintained-feeling, maintained-value, and overall situational interest scores for the Medimon algorithm (all p < 0.001). Thematic analysis of open-ended responses identified four themes: enhanced clarity, improved memorability, increased engagement, and barriers to interpretation. Collectively, the findings suggest that embedding visual mnemonics and serious-game characters into diagnostic algorithms can enhance learner interest and may improve long-term retention in preclinical medical education. Full article

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29 pages, 2266 KB

Open AccessReview

A Scoping Review of AI-Driven mHealth Systems for Precision Hydration: Integrating Food and Beverage Water Content for Personalized Recommendations

by Kyriaki Apergi, Georgios D. Styliaras, George Tsirogiannis, Grigorios N. Beligiannis and Olga Malisova

Multimodal Technol. Interact. 2025, 9(11), 112; https://doi.org/10.3390/mti9110112 - 8 Nov 2025

Viewed by 624

Abstract

Background: Precision nutrition increasingly integrates mobile health (mHealth) and artificial intelligence (AI) tools. However, personalized hydration remains underdeveloped, particularly in accounting for both food- and beverage-derived water intake. Objective: This scoping review maps the existing literature on mHealth applications that incorporate machine learning (ML) or AI for personalized hydration. The focus is on systems that combine dietary (food-based) and fluid (beverage-based) water sources to generate individualized hydration assessments and recommendations. Methods: Following the PRISMA-ScR guidelines, we conducted a structured literature search across three databases (PubMed, Scopus, Web of Science) through March 2025. Studies were included if they addressed AI or ML within mHealth platforms for personalized hydration or nutrition, with an emphasis on systems using both beverage and food intake data. Results: Of the 43 included studies, most examined dietary recommender systems or hydration-focused apps. Few studies used hydration assessments focusing on both food and beverages or employed AI for integrated guidance. Emerging trends include wearable sensors, AR tools, and behavioral modeling. Conclusions: While numerous digital health tools address hydration or nutrition separately, there is a lack of comprehensive systems leveraging AI to guide hydration from both food and beverage sources. Bridging this gap is essential for effective, equitable, and precise hydration interventions. In this direction, we propose a hydration diet recommender system that integrates demographic, anthropometric, psychological, and socioeconomic data to create a truly personalized diet and hydration plan with a holistic approach. Full article

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27 pages, 2231 KB

Open AccessArticle

A Digital Model-Based Serious Game for PID-Controller Education: One-Axis Drone Model, Analytics, and Student Study

by Raul Brumar, Stelian Nicola and Horia Ciocârlie

Multimodal Technol. Interact. 2025, 9(11), 111; https://doi.org/10.3390/mti9110111 - 24 Oct 2025

Viewed by 577

Abstract

This paper presents a serious game designed to support the teaching of PID controllers. The game couples a visually clear Unity scene with a physics-accurate digital model of a drone with a single degree of freedom (called a one-axis drone) and helps prepare students to meet the demands of Industry 4.0 and 5.0. An analytics back-end logs system error at 10 Hz and interaction metrics, enabling instructors to diagnose common tuning issues from a plot and to provide actionable hints to students. The design process that led to choosing the one-axis drone and turbulence application via “turbulence balls” is explained, after which the implementation is described. The proposed solution is evaluated in a within-subjects study performed with 21 students from mixed technical backgrounds across two short, unsupervised tinkering sessions of up to 10 min framed by four quizzes of both general and theoretical content. Three questions shaped the analysis: (i) whether error traces can be visualized by instructors to generate actionable hints for students; (ii) whether brief, unsupervised play sessions yield measurable gains in knowledge or stability; and (iii) whether efficiency of tuning improves without measurable changes in tune performance. Results show that analysis of plotted error values exposes recognizable issues with PID tunes that map to concrete hints provided by the instructor. When it comes to unsupervised play sessions, no systematic pre/post improvement in quiz scores or normalized area under absolute error was observed. However, it required significantly less effort from students in the second session to reach the same tune performance, indicating improved tuning efficiency. Overall, the proposed serious game with the digital twin-inspired one-axis drone and custom analytics back-end has emerged as a practical, safe, and low-cost auxiliary tool for teaching PID controllers, helping bridge the gap between theory and practice. Full article

(This article belongs to the Special Issue Video Games: Learning, Emotions, and Motivation)

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Multimodal Technol. Interact., Volume 9, Issue 11 (November 2025) – 4 articles

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