WORKSHOP | Open co-design | June 3rd

The “Open co-design workshop on next generation tools to manage ALS and MS” took plane on June 3rd and follows a series of two previous events opened only to the project’s consortium members aimed at exploring and defining the BRAINTEASER stakeholders’ ecosystem, the user’s needs and profiles and the potential clinical use of the project’s technology. During the open co-design workshop, the initial BRAINTEASER technical solution was presented. The external experts and the audience was involved in a collaborative and participative discussion on clinical unmet needs, disease course, clinical practice and new emerging research on ALS and MS to assess the BRAINTEASER solution and propose possible refinements.

LAUNCH EVENT | eMOTIONAL Cities | April 13th

The eMOTIONAL CITIES Launch Event was held on April 13th with the presence of Manuel Heitor, Minister of Science, Technology and Higher Education. The opening dissemination event was organised in Lisbon (Portugal) to communicate and disseminate the project’s objectives and action plan.


KICK​-OFF ​MEETING | eMOTIONAL Cities | April 8th & 9th

The project kick-off meeting took place on April 8th and 9th, during which the general features of the project were presented, as well as the nine workpackages (WP). On the second day there were also presented the other Urban Health Cluster projects, namely ENLIGHTENme, WELLBASED, HEART, RECETAS and URBANOME.

WORKSHOP | Environmental Sensors | March 12th

The Environmental Sensors Workshop took place on March 12th and aimed:

1) Discuss the optimal solutions to collect environmental data from existing infrastructure, sensors grids, and open data already available in the involved cities (i.e. Lisbon, Madrid, Turin, and Pavia);

2) Find the best solutions to enhance and integrate environmental data infrastructures with novel sensors;

3) Define synergies and collaboration paths with other projects such as eMOTIONAL Cities, 4) Outline the strategies and methods to define the “personal exposure” to pollutant for each patient so to integrate it into our AI models.