This paper systemically reviews and clarifies the state-of-the-art HRI evaluation studies especially for the usability of social robots. A total of 36 articles were collected through a keyword, abstract, title search from various online search engines. Afterwards, 163 measures were selected and reviewed carefully. This research was classified into two parts. In the first part, evaluation methodologies were investigated according to (1) type of stimuli on evaluation, (2) evaluation technique, and (3) criteria of participants. In the second part, assessment measures were collected and the model of attitude towards a social robot is proposed. As a result, this study suggests practical strategies for selecting appropriate methods and measures that meet specific requirements of research. The proposed hierarchical structure of assessment measures is expected to contribute to both practical use and academic use.
Objective The study aims to develop a mHealth application for seizure management based on the human system integration (HSI) approach. Background Unmet healthcare needs among people with epilepsy continue to exist despite the advancement in healthcare technology. Current seizure management methods are found to be ineffective. Therefore, a more efficient strategy such as mHealth technology is necessary to aid seizure management. Method The needs identification phase involved identifying the user requirements by interviewing 10 stakeholders and conducting thematic analysis and needs interpretation technique. In the solution identification phase, the system requirements were derived using various human-centered design and systems engineering approaches and were evaluated through quality function deployment to determine design targets. For the design and evaluation phase, the design targets were reflected in the app through the iterative prototyping process, and the interface and functional design were evaluated by seven human factors and ergonomics experts and four stakeholders, respectively. Results Three primary needs and ten user requirements were derived from the needs identification phase. Ten out of fifteen system requirements were selected as design targets to be included in the final prototype. Results of the evaluation showed that the interface design of the proposed app showed superior usability compared to a competitor app and that the app functions were beneficial for the stakeholders. Conclusion The mHealth app designed through the HSI framework showed good potential in addressing the main issues in seizure management. Application The mHealth app design methodology based on the HSI approach can be applied to the design of small-scale systems in various domains.
Vehicle sound design is gaining attention in the automotive industry, especially for Electric Vehicles (EV). For EVs, acceleration sound is critical for both user experience (UX) and safety. Despite the abundance of UX-related studies investigating the external presentation of acceleration sound for EVs, internal presentation of acceleration sound seems to be overlooked. Thus, further understanding on what influences the user preferences for internal EV sound is essential for better EV sound design. This study aims to explore and develop a simple theoretical path model to help understand the relationship between pragmatic quality, hedonic quality, novelty, and user preferences for EV internal acceleration sounds. Thirty-two participants evaluated twenty-seven EV acceleration sound samples using a 12-item semantic differential scale with bipolar adjective pairs that describe the measured variables in a controlled experimental setting. The relationship between the modeled variables was analyzed using bias-corrected factor score path analysis (BCFSPA). Results showed that the modified model yielded good model fit indices and partially confirmed the initial hypotheses. It was found that pragmatic and hedonic quality had a positive relationship with user preference, whereas, novelty had a negative relationship with hedonic quality and user preference for EV sounds. This study contributes to the understanding of factors that affects user preference for EV sound and provides initial accounts to different approaches and methods for model testing.
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