Personalization for unobtrusive service interaction

Increasingly, mobile devices play a key role in the communication between users and the services embedded in their environment. With ever greater number of services added to our surroundings, there is a need to personalize services according to the user needs and environmental context avoiding service behavior from becoming overwhelming. In order to prevent this information overload, we present a method for the development of mobile services that can be personalized in terms of obtrusiveness (the degree in which each service intrudes the user's mind) according to the user needs and preferences. That is, services can be developed to provide their functionality at different obtrusiveness levels depending on the user by minimizing the duplication of efforts. On the one hand, we provide mechanisms for describing the obtrusiveness degree required for a service. On the other hand, we make use of Feature Modeling techniques in order to define the obtrusiveness level adaptation in a declarative manner. An experiment was conducted in order to put in practice the proposal and evaluate the user acceptance for the personalization capabilities provided by our approach. © Springer-Verlag London Limited 2011.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011 and co-financed with ERDF, in the grants program FPU.Gil Pascual, M.; Giner Blasco, P.; Pelechano Ferragud, V. (2012). Personalization for unobtrusive service interaction. Personal and Ubiquitous Computing. 16(5):543-561. https://doi.org/10.1007/s00779-011-0414-0S543561165Abrams M, Phanouriou C, Batongbacal AL, Williams SM, Shuster JE (1999) Uiml: an appliance-independent xml user interface language. In: WWW ’99. Elsevier, North-Holland, pp 1695–1708Ballagas R, Borchers J, Rohs M, Sheridan JG (2006) The smart phone: a ubiquitous input device. 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Supporting dynamic change detection: using the right tool for the task

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition

A quiet NICU for improved infants' health, development and well-being:a systems approach to reducing noise and auditory alarms

<p>Noise is a direct cause of health problems, long-lasting auditory problems and development problems. Preterm infants are, especially, at risk for auditory and neurocognitive development. Sound levels are very high at the neonatal intensive care unit (NICU) and may contribute to the frequently observed detrimental outcomes of prematurely born infants. Despite efforts to reduce noise level at the NICU, these have not changed over the past years. Although many authors indicate that a systems approach could solve such interrelated problems, methods to do so are generally lacking for the complicated situation in a critical care setting. A new approach was developed, that is, combining Fuzzy Front End earliest stage product development and human factors methods, with a focus on all Human-tech levels and on their interaction. A concept built up from several emerging technologies was developed, including tactile alarms, artificial intelligence for medicine, multimodal alarm system and mobile communication in critical care. Current and envisioned nursing work was modelled. Outcome of the study is an overview of investigations to develop the measures.</p>