D7.3 Stakeholder management plan

D7.3 provides an in-depth analysis of the stakeholders who are influenced by the SmartLife solutions and those who can create effect on the decisions made throughout the development and consecutive exploitation. It outlines the continuous engagement with the stakeholders and sets a plan for future communication during the exploitation stages.

D7.1. Market Watch and Analysis

The outcomes of the T7.1 are related to the activities of the business ecosystem analysis the first step of which lies in investigation and analysis of the current state of the market including the overview of current solutions, potential competitors, the analysis of the most common business models, which are…

D6.1. Reports Iterative prototyping

This document provides the results of iterative prototyping with Flemish adolescents about game elements that are preferable to include. The document further recommends the game design.

D3.3. Design recommendations from target users

This document involves a thematic analysis, dealing with the needs and preferences from the target users to assess what they find important in an exergame linked to a smart textile and how an exergame can be successfully implemented into their daily lives.    

D8.1. SmartLife Website

This deliverable includes the design of the project website, which functionalities and characteristics must have. It also includes a basic graphic design of the different sections, layouts and menus, as well as the contents which must contain each section. The document also includes screenshots of…

D5.1. Detailed game design document

This deliverable is the result of the game definition and design process that outlines the design decisions of the SmartLife game that takes into consideration requirement gathering and analysis of the potential end-users at the same time focusing on the engagement, tailoring and usability requirements.

D4.2. Offline data analytics methods

This Deliverable describe the deployment of a set of components capable to extract relevant patterns out of the individual collected data. This data will be analysed by machine learning methods in order to develop a physical activity model.