| 1. |
- Hodson, Elise, et al.
(författare)
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Evaluating Social Impact of Smart City Technologies and Services: Methods, Challenges, Future Directions
- 2023
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Ingår i: MULTIMODAL TECHNOLOGIES AND INTERACTION. - : MDPI. - 2414-4088. ; 7:3
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Tidskriftsartikel (refereegranskat)abstract
- This study examines motivations, definitions, methods and challenges of evaluating the social impacts of smart city technologies and services. It outlines concepts of social impact assessment and discusses how social impact has been included in smart city evaluation frameworks. Thematic analysis is used to investigate how social impact is addressed in eight smart city projects that prioritise human-centred design across a variety of contexts and development phases, from design research and prototyping to completed and speculative projects. These projects are notable for their emphasis on human, organisational and natural stakeholders; inclusion, participation and empowerment; new methods of citizen engagement; and relationships between sustainability and social impact. At the same time, there are gaps in the evaluation of social impact in both the smart city indexes and the eight projects. Based on our analysis, we contend that more coherent, consistent and analytical approaches are needed to build narratives of change and to comprehend impacts before, during and after smart city projects. We propose criteria for social impact evaluation in smart cities and identify new directions for research. This is of interest for smart city developers, researchers, funders and policymakers establishing protocols and frameworks for evaluation, particularly as smart city concepts and complex technologies evolve in the context of equitable and sustainable development.
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| 2. |
- Rouchitsas, Alexandros, 1980-, et al.
(författare)
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Smiles and Angry Faces vs. Nods and Head Shakes: Facial Expressions at the Service of Autonomous Vehicles
- 2023
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Ingår i: Multimodal Technologies and Interaction. - : MDPI. - 2414-4088. ; 7:2
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Tidskriftsartikel (refereegranskat)abstract
- When deciding whether to cross the street or not, pedestrians take into consideration information provided by both vehicle kinematics and the driver of an approaching vehicle. It will not be long, however, before drivers of autonomous vehicles (AVs) will be unable to communicate their intention to pedestrians, as they will be engaged in activities unrelated to driving. External human–machine interfaces (eHMIs) have been developed to fill the communication gap that will result by offering information to pedestrians about the situational awareness and intention of an AV. Several anthropomorphic eHMI concepts have employed facial expressions to communicate vehicle intention. The aim of the present study was to evaluate the efficiency of emotional (smile; angry expression) and conversational (nod; head shake) facial expressions in communicating vehicle intention (yielding; non-yielding). Participants completed a crossing intention task where they were tasked with deciding appropriately whether to cross the street or not. Emotional expressions communicated vehicle intention more efficiently than conversational expressions, as evidenced by the lower latency in the emotional expression condition compared to the conversational expression condition. The implications of our findings for the development of anthropomorphic eHMIs that employ facial expressions to communicate vehicle intention are discussed.
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| 3. |
- Zechner, Olivia, et al.
(författare)
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NextGen training for medical first responders : advancing mass-casualty incident preparedness through mixed reality technology
- 2023
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Ingår i: Multimodal Technologies and Interaction. - Basel : MDPI. - 2414-4088. ; 7:12
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Tidskriftsartikel (refereegranskat)abstract
- Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.
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