The WP1 marks the beginning of the project. We aim to adopt a user-centered approach and through iterative development steps involving users in all phases to test alternative usage scenarios. In WP1 a thorough and in-depth analysis of the existing state-of-the-art in all areas of interest of the project will be conducted during the initial period of project implementation.

The WP2 consists of the following tasks:
Task 2.1 – Smart insole development. This activity is the most critical activity. It starts with the end of WP1 and lasts until M24. It is implemented in iterative cycles of implementation - confirmation - optimization.
Task 2.2 – Design and implementation of the communication system of the smart insole with appropriate computing platforms. Its successful implementation will increase the usability, the clinical value and the innovation of the solutions. It is implemented in iterative cycles of implementation - confirmation - optimization until the development of the final solution.
Task 2.3 – Design and implementation of the energy harvesting methodology. This activity will explore energy harvesting from the human body - a combination of mechanical and thermal energy - to power the wearable sensor. Its success will constitute a radical innovation with important perspectives.

The implementation of new, improved algorithms for analyzing and visualizing the data, i.e. the signals received by the Smart Insole, is another critical element regarding the overall success of the project.
The WP3 consists of the following tasks:
Task 3.1 – Development of algorithms for gait phase analysis. The complexity of human gait implies different motor and kinematic events withing the gait phases. This task will develop the algorithms for extracting appropriate biomarkers for the gait phase recognition.
Task 3.2 – Development of algorithms for gait pattern recognition (in correspondence with Task 3.1).

The WP4 consists of the following tasks:
Task 4.1 – Development of the reasoning engine. The gait phases and other estimated gait parameters that will be produced by WP3 will be uploaded to a remote server, where inference techniques will be applied to accurately predict critical events.
Task 4.2 – Development of machine learning algorithms. Machine learning will be developed for identifying critical signal features and classifiers for gait feature recognintion. Supervised algorithms such as supervised feature selection, greedy forward feature selection, and mutual information maximization (MIM) as well as deep learing techniques will be evaluated in terms of suitability accuracy.

The WP5 consists of the following tasks:
Task 5.1 - System integration and testing. It concerns the functional integration of subsystems into a single system and the implementation of appropriate, meticulously designed tests to verify its functional efficiency. It is implemented in two cycles - alfa tests και beta tests.
Task 5.2 – Data visualization and app development. This tasks deals with the development of indicative applications in smart phone environments, for the two population groups targeted by the project.
Task 5.3 – Exploitation plan

The WP6 will focus on the elaboration of a detailed feasibility study. It will be assigned to an appropriate external partner that will undertake to implement the envisaged feasibility study. In particular, the dynamics of the project will be evaluated and analyzed, aiming at the objective disclosure of its strengths and weaknesses, the opportunities arising from it, as well as its prospects for success. Particular emphasis will be placed on the analysis of the characteristics of the target market, and the elaboration of a suitable business model proposal for the exploitation of the results and a sustainability plan.

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