Personalisation of intelligent homecare services adapted to children with motor impairments

Ambient Intelligence could support innovative application domains like motor impairments' detection at the home environment. This research aims to prevent neurodevelopmental disorders through the natural interaction of the children with embedded intelligence daily life objects, like home furniture and toys. Designed system uses an interoperable platform to provide two intelligent interrelated home healthcare services: monitoring of children¿s abilities and completion of early stimulation activities. A set of sensors, which are embedded within the rooms, toys and furniture, allows private data gathering about the child's interaction with the environment. This information feeds a reasoning subsystem, which encloses an ontology of neurodevelopment items, and adapts the service to the age and acquisition of expected abilities. Next, the platform proposes customized stimulation services by taking advantage of the existing facilities at the child's environment. The result integrates Embedded Sensor Systems for Health at Mälardalen University with UPM Smart Home, for adapted services delivery.

Exploring the role of social capital influence variables on travel behaviour

This paper explores the potential role of individual trip characteristics and social capital network variables in the choice of transport mode. A sample of around 100 individuals living or working in one suburb of Madrid (i.e. Las Rosas district of Madrid) participated in a smartphone short panel survey, entering travel data for an entire working week. A Mixed Logit model was estimated with this data to analyze shifts to metro as a consequence of the opening of two new stations in the area. Apart from classical explanatory variables, such as travel time and cost, gender, license and car ownership, the model incorporated two “social capital network” variables: participation in voluntary activities and receiving help for various tasks (i.e. child care, housekeeping, etc.). Both variables improved the capacity of the model to explain transport mode shifts. Further, our results confirm that the shift towards metro was higher in the case of people “helped” and lower for those participating in some voluntary activities.