Examinando por Autor "Barco, Alex"

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    How does children’s anthropomorphism of a social robot develop over time?: a six-wave panel study
    (Springer Science and Business Media B.V., 2024-07) Kühne, Rinaldo; Peter, Jochen; Jong, Chiara de; Barco, Alex
    Research on children’s anthropomorphism of social robots is mostly cross-sectional and based on a single measurement. However, because social robots are new type of technology with which children have little experience, children’s initial responses to social robots may be biased by a novelty effect. Accordingly, a single measurement of anthropomorphism may not accurately reflect how children anthropomorphize social robots over time. Thus, we used data from a six-wave panel study to investigate longitudinal changes in 8- to 9-year-old children’s anthropomorphism of a social robot. Latent class growth analyses revealed that anthropomorphism peaked after the first interaction with the social robot, remained stable for a brief period of time, and then decreased. Moreover, two distinct longitudinal trajectories of anthropomorphism could be identified: one with moderate to high anthropomorphism and one with low to moderate anthropomorphism. Previous media exposure to non-fictional robots increased the probability that children experienced higher levels of anthropomorphism.
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    Ítem
    Robots as social companions?: investigating longitudinal companionship and social presence in child-robot interaction
    (Taylor and Francis Ltd., 2025-11-13) Sharan, Navya N.; Peter, Jochen; Lemmens, Jeroen S.; Kühne, Rinaldo; Jong, Chiara de; Barco, Alex
    In research on human-robot interaction (HRI), the Media are Social Actors (MASA) paradigm has received increasing attention. However, MASA is less explicit about longitudinal developments in social responses to media agents. MASA posits that social signals predict social responses to media agents. We examined specifically the longitudinal relationship between perceived companionship, as a social signal, and social presence toward a social robot, as a social response. Data from five waves of a panel study among 400 children aged 8 to 9 years were used. A consistent longitudinal relationship was not found. A cross-lagged panel model showed that, over time, perceived companionship positively predicted social presence about half of the time. Social presence, generally, did not predict greater perceived companionship over time. A random-intercept cross-lagged panel model with its separation of within- and between-subject processes demonstrated no consistent over-time relations. Over-time consistency of predictions of MASA may need further research.
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    A wearable sensor node for measuring air quality through citizen science approach: insights from the SOCIO-BEE project
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-06-15) Morresi, Nicole; Puerta Beldarrain, Maite; López de Ipiña González de Artaza, Diego; Barco, Alex; Gómez Carmona, Oihane; López Gomollón, Carlos; Casado Mansilla, Diego; Kotzagianni, Maria; Casaccia, Sara; Udina Oliva, Sergi; Revel, Gian Marco
    Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO2, O3, and PM2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM2.5 data (R2 = 0.75), with an average absolute error of 0.54 µg/m3 and a statistical confidence interval of ±3.3 µg/m3. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy.