Work packages

The project is divided into eleven work packages (WP), each of which refers to a set of main objectives to be finally achieved for the realization of the project. The achievement of the objectives of each work package is ensured by the definition of the corresponding tasks to be carried out in the context of the work package. 

 

Work packages

The project is divided into eleven work packages (WP), each of which refers to a set of main objectives to be finally achieved for the realization of the project. The achievement of the objectives of each work package is ensured by the definition of the corresponding tasks to be carried out in the context of the work package. 

 

Package 01

PROJECT COORDINATION & MANAGEMENT

  • Effective monitoring and control of costs and schedule;
  • Establish efficient coordination and communication mechanisms with all project participants;
  • Updating the plans every year according to new needs and knowledge;
  • Defining and monitoring the quality standards to be met by the final results and products of the project.

1.1 Administrative and financial control

1.2 WP coordination

1.3 Internal communication management

1.4 Risk management

1.5 Quality management

Package 02

PROJECT REQUIREMENTS & PROTOCOLS

  • Classification and definition of functional and technical specifications of the BALLADEER platform, requirements in terms of clinical, software and hardware, security and privacy, ADHD screening protocols, etc; 
  • Design of evaluation protocols in a goal-oriented manner and plans will be created for their successful deployment.

2.1 Analysis of project requirements

2.2 Analysis and design of ADHD detection protocols

2.3 Design of ADHD detection experiments

2.4 Selección del grupo de edad

Package 03

SMART XR FOR THE DETECTION OF ADHD

  • ADHD diagnosis using extended reality environments;
  • Personalization of ADHD diagnosis by processing brain/physiological activity data during extended reality testing;
  • Design and implementation of extended reality environments covering a new integrated health and care pathway for ADHD diagnosis.

3.1 XR video game design and implementation

3.2 Design and implementation of electroencephalography (EEG) system.

3.3 Design and implementation of RESTful services API for Smart XR for ADHD

Package 04

SIGNAL PROCESSING AND BRAIN ACTIVITY

  • Development of new signal processing algorithms for the analysis of all modalities: video cameras, gestures and facial expressions, inertial measurement (IMU) wearables such as accelerometers, EEG device, etc.
  • Detect critical signal parameters. This can affect sampling rates, geometric configuration, amplification and pre-filtering optimization. 

4.1 EEG Signal Processing

4.2 Design and implementation of the RESTful service API for signal processing

Package 05

BIG DATA ANALYSIS FOR ADHD

  • Definition of the storage of data collected in Data Lakes;
  • Development of REST API for devices to store data in Data Lakes;
  • Real-time data processing;
    Design of data warehouse to store and analyze the collected data;
  • ETL process design to populate the data warehouse.

5.1 Definition of data lakes

5.2 Design and implementation of the RESTful services API for Big Data Analytics

5.3 Creation of a data warehouse

5.4 Definition and implementation of Extract-Transform-Load process

Package 06

DECISION SUPPORT AND VISUALIZATION FOR ADHD STAKEHOLDERS

  • Identification of dependencies and cross concerns between stakeholder objectives;
  • Definition of the system’s key performance indicators (KPIs).

6.1 Definition of key performance indicators for the diagnosis of ADHD

6.2 Design and implementation of scorecards to increase health literacy

6.3 Design and implementation of scorecards for advancing competency in data-driven health services

Package 07

Artificial intelligence for ADHD

  • Analysis of predictive machine learning (ML) algorithms to deal with collected data (e.g., EEG, eye tracking);
  • Implementation of ML models focused on ADHD diagnosis and prevention;
    Initial performance evaluation of ADHD ML models;
  • Development of cloud-based processes to (re)train customized ML models for ADHD.

7.1 Analysis of machine learning algorithms

7.2 Development and training of machine learning algorithms for ADHD

7.3 Automatic retraining of Machine Learning algorithms for ADHD

7.4 Design and implementation of RESTful services API for Artificial Intelligence

Package 08

Big Data and AI platform integration and deployment.

  • Definition of a novel RESTful integration architecture;
  • Implementation of metrics and indicators to detect quality deterioration in FAIR Service Architecture;
  • Development of tools, data catalogs and specifications to support FAIR data;
  • Definition and implementation of a continuous release process for the deployment of the architecture.

8.1 Specification of RESTful software integration and deployment architecture

8.2 Identification and screening of relevant technical debt in solutions and technologies

8.3 Development of tools, specifications and data catalogs to support FAIR interoperability procedures 8.4 Definition and implementation of a continuous release process for the deployment of the FAIR interoperability procedures

8.4 Definition and implementation of a continuous release process for the deployment of the integration architecture and continuous quality assurance.

Package 09

ADHD Screening Assessment in XR

  • Clinical assessment of the new BALLADEER form of ADHD screening by neurology and psychology clinicians;
  • Determine the usability, gratification, motivation and acceptance of the technological devices in the project stakeholders.

9.1 Clinical evaluation of experimental results

9.2 Technological evaluation of the experimental results

9.3 Proposed recommendations for the detection of ADHD on XR

Package 10

Exploitation, communication and dissemination

  • Promote and demonstrate the capabilities of the system;
  • Disseminate capabilities and other notable scientific results at appropriate conferences and journals.

10.1 Communication and dissemination

10.2 Exploitation of results

10.3 Intellectual property management plan

Package 11

Ethical and legal issues and cross-cutting priorities

  • Ensuring that health data are processed in accordance with the law and ethics;
  • Effective enforcement of ethical requirements relating to AI research in the healthcare sector;
  • Providing legal assistance in the commercialization process of the clinical decision support tool;
  • Ensuring a social science-based approach to the project.

11.1 Processing of health data in accordance with the law and ethics

11.2 Gender issues

11.3 Socio-economic science and humanities issues

11.4 Open science issues

Package 01

PROJECT COORDINATION & MANAGEMENT

  • Effective monitoring and control of costs and schedule;
  • Establish efficient coordination and communication mechanisms with all project participants;
  • Updating the plans every year according to new needs and knowledge;
  • Defining and monitoring the quality standards to be met by the final results and products of the project.

1.1 Administrative and financial control

1.2 WP coordination

1.3 Internal communication management

1.4 Risk management

1.5 Quality management

Package 02

PROJECT REQUIREMENTS & PROTOCOLS

  • Classification and definition of functional and technical specifications of the BALLADEER platform, requirements in terms of clinical, software and hardware, security and privacy, ADHD screening protocols, etc; 
  • Design of evaluation protocols in a goal-oriented manner and plans will be created for their successful deployment.

2.1 Analysis of project requirements

2.2 Analysis and design of ADHD detection protocols

2.3 Design of ADHD detection experiments

2.4 Selección del grupo de edad

Package 03

SMART XR FOR THE DETECTION OF ADHD

  • ADHD diagnosis using extended reality environments;
  • Personalization of ADHD diagnosis by processing brain/physiological activity data during extended reality testing;
  • Design and implementation of extended reality environments covering a new integrated health and care pathway for ADHD diagnosis.

3.1 XR video game design and implementation

3.2 Design and implementation of electroencephalography (EEG) system.

3.3 Design and implementation of RESTful services API for Smart XR for ADHD

Package 04

SIGNAL PROCESSING AND BRAIN ACTIVITY

  • Development of new signal processing algorithms for the analysis of all modalities: video cameras, gestures and facial expressions, inertial measurement (IMU) wearables such as accelerometers, EEG device, etc.
  • Detect critical signal parameters. This can affect sampling rates, geometric configuration, amplification and pre-filtering optimization. 

4.1 EEG Signal Processing

4.2 Design and implementation of the RESTful service API for signal processing

Package 05

BIG DATA ANALYSIS FOR ADHD

  • Definition of the storage of data collected in Data Lakes;
  • Development of REST API for devices to store data in Data Lakes;
  • Real-time data processing;
    Design of data warehouse to store and analyze the collected data;
  • ETL process design to populate the data warehouse.

5.1 Definition of data lakes

5.2 Design and implementation of the RESTful services API for Big Data Analytics

5.3 Creation of a data warehouse

5.4 Definition and implementation of Extract-Transform-Load process

Package 06

DECISION SUPPORT AND VISUALIZATION FOR ADHD STAKEHOLDERS

  • Identification of dependencies and cross concerns between stakeholder objectives;
  • Definition of the system’s key performance indicators (KPIs).

6.1 Definition of key performance indicators for the diagnosis of ADHD

6.2 Design and implementation of scorecards to increase health literacy

6.3 Design and implementation of scorecards for advancing competency in data-driven health services

Package 07

Artificial intelligence for ADHD

  • Analysis of predictive machine learning (ML) algorithms to deal with collected data (e.g., EEG, eye tracking);
  • Implementation of ML models focused on ADHD diagnosis and prevention;
    Initial performance evaluation of ADHD ML models;
  • Development of cloud-based processes to (re)train customized ML models for ADHD.

7.1 Analysis of machine learning algorithms

7.2 Development and training of machine learning algorithms for ADHD

7.3 Automatic retraining of Machine Learning algorithms for ADHD

7.4 Design and implementation of RESTful services API for Artificial Intelligence

Package 08

Big Data and AI platform integration and deployment.

  • Definition of a novel RESTful integration architecture;
  • Implementation of metrics and indicators to detect quality deterioration in FAIR Service Architecture;
  • Development of tools, data catalogs and specifications to support FAIR data;
  • Definition and implementation of a continuous release process for the deployment of the architecture.

8.1 Specification of RESTful software integration and deployment architecture

8.2 Identification and screening of relevant technical debt in solutions and technologies

8.3 Development of tools, specifications and data catalogs to support FAIR interoperability procedures 8.4 Definition and implementation of a continuous release process for the deployment of the FAIR interoperability procedures

8.4 Definition and implementation of a continuous release process for the deployment of the integration architecture and continuous quality assurance.

Package 09

ADHD Screening Assessment in XR

  • Clinical assessment of the new BALLADEER form of ADHD screening by neurology and psychology clinicians;
  • Determine the usability, gratification, motivation and acceptance of the technological devices in the project stakeholders.

9.1 Clinical evaluation of experimental results

9.2 Technological evaluation of the experimental results

9.3 Proposed recommendations for the detection of ADHD on XR

Package 10

Exploitation, communication and dissemination

  • Promote and demonstrate the capabilities of the system;
  • Disseminate capabilities and other notable scientific results at appropriate conferences and journals.

10.1 Communication and dissemination

10.2 Exploitation of results

10.3 Intellectual property management plan

Package 11

Ethical and legal issues and cross-cutting priorities

  • Ensuring that health data are processed in accordance with the law and ethics;
  • Effective enforcement of ethical requirements relating to AI research in the healthcare sector;
  • Providing legal assistance in the commercialization process of the clinical decision support tool;
  • Ensuring a social science-based approach to the project.

11.1 Processing of health data in accordance with the law and ethics

11.2 Gender issues

11.3 Socio-economic science and humanities issues

11.4 Open science issues

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