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“This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant ag...
What is 3D personal data?
What is 3D personal data?
Two silos Health sector and Consumer goods. Why to share data? • The 3D data of the health sector contains the body shape ...
Why is big data? • Hospitals: About 2.7 petabytes a year stored in the EU26 • Consumer goods: it is estimated that current...
BodyPass objectives • Generate tools for extraction of 3D model data from raw 3D scans, and medical imaging data. • Genera...
Data curation Quality Subject attire Accuracy Resolution Acquisition time
Interoperability problems. Resolution, formats and metadata
Interoperability problems. 3D is not a table 1 𝑛 ෍ 𝑖=1 𝑛 𝑋𝑖
Solution: The Standard Template Point-to-point correspondence Body pats (ISO 8559-1)
Same pose Goal: Adapt the pose for aggregated data
Goal: obtain reliable/usable metrics for end-users Digital Measuring Step 1: landmarks location
Goal: obtain reliable/usable metrics Digital Measuring Step 2: measuring
The anonymization problem
Synthetic avatars RAW DEFACED SYNTHETIC FACE
The BODYPASS ecosystem
Transactions
Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B:...
Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B:...
BODYPASS ecosystem
Off-chain components: Data Management Plane
Consortium
http://bodypass.eu/ This project has received funding from the European Union’s Horizon 2020 research and innovation progr...
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The challenges of 3D Personal Data

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How to exchange and grant access to health data in a secure way approaches and recommendations
The health crisis due to COVID-19 is shaping a new reality in which the exchange and access to health data in a secure way will be more and more necessary. In this complex challenge converge both the respect for the individual rights as well as the interests of the patients and the need to promote the research in pursuit of the public interest. To face this challenge, we can find different approaches across Europe. In this webinar, we will present the experiences of three EU-funded projects (BigMedilytics, BodyPass, and DeepHealth), besides an overview of the legal framework and recommendations to enforce both national regulations and GDPR by an expert in data privacy and security.

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The challenges of 3D Personal Data

  1. 1. “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779780”. The challenges of 3D Personal Data Juan V. Durá juan.dura@ibv.org 14th May 2020
  2. 2. What is 3D personal data?
  3. 3. What is 3D personal data?
  4. 4. Two silos Health sector and Consumer goods. Why to share data? • The 3D data of the health sector contains the body shape information, not only internal body information. These data could be used by designers and manufacturers of the consumer goods sector. • The 3D scanners’ in the consumer goods sector are low cost, non-invasive, and ease of use. It makes them appealing for widespread clinical applications and large-scale epidemiological surveys.
  5. 5. Why is big data? • Hospitals: About 2.7 petabytes a year stored in the EU26 • Consumer goods: it is estimated that currently one person is scanned every 15 minutes in the US and Europe.
  6. 6. BodyPass objectives • Generate tools for extraction of 3D model data from raw 3D scans, and medical imaging data. • Generate protocols and data models for privacy preserving and secure exchange of extracted and derived data between different parties enabling data exchange and big data analytics across different silos
  7. 7. Data curation Quality Subject attire Accuracy Resolution Acquisition time
  8. 8. Interoperability problems. Resolution, formats and metadata
  9. 9. Interoperability problems. 3D is not a table 1 𝑛 ෍ 𝑖=1 𝑛 𝑋𝑖
  10. 10. Solution: The Standard Template Point-to-point correspondence Body pats (ISO 8559-1)
  11. 11. Same pose Goal: Adapt the pose for aggregated data
  12. 12. Goal: obtain reliable/usable metrics for end-users Digital Measuring Step 1: landmarks location
  13. 13. Goal: obtain reliable/usable metrics Digital Measuring Step 2: measuring
  14. 14. The anonymization problem
  15. 15. Synthetic avatars RAW DEFACED SYNTHETIC FACE
  16. 16. The BODYPASS ecosystem
  17. 17. Transactions
  18. 18. Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B: Get many anonymous individual 3D data & metrics • Type C: Get aggregated 3D data & metrics Each data provider chooses which queries it responds to
  19. 19. Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B: Get many anonymous individual 3D data & metrics • Type C: Get aggregated 3D data & metrics Hospitals aggregated data only
  20. 20. BODYPASS ecosystem
  21. 21. Off-chain components: Data Management Plane
  22. 22. Consortium
  23. 23. http://bodypass.eu/ This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779780 Part of the Big Data Value Public-Private Partnership juan.dura@ibv.org

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