1. Slide 1 Towards a Cost-Effective Homecare for a Caregiver Assistance System in Brazil MAURO OLIVEIRA LAR-A Computer Network Laboratory of Aracati

2. Slide 2 16th Healthcom October 16th, 2014 Natal - RN Slide 2 Mauro Oliveira Federal Institute of Ceará (IFCE) Aracati, Brazil amauroboliveira@gmail.com Odorico Andrade Federal University of Ceara (UFC) Fortaleza, Brazil odorico0811@gmail.com Marcos Santos State University of Ceara (UECE) Fortaleza, Brazil marcos.eduardo@uece.br Roberto Alcântara Federal Institute of Ceará (IFCE) Aracati, Brazil robertoalcantara@gmail.com Germanno Teles Northeast Bank of Brazil(BNB) Fortaleza, Brazil germanno@bnb.gov.br Nazim Agoulmine Joseph Fourier University (UJF) Université d´Evry Val dÉssone Nazim.Agoulmine@ufrst.univ- evry.fr Towards a Cost-Effective Homecare for a Caregiver Assistance System In Brazil Towards a Cost-Effective Homecare for a Caregiver Assistance System In Brazil Team working on this project

3. Slide 3 1.Contextualization 2.Application Scenario 3.Brazilian Digital TV 4. LARIISA Project 5. Prototype Conclusion Summary

4. Slide 4 1. Contextualization

5. Slide 5 Health System - Information Era Based on Disease PREVENTION Costs ¢ Encouraged Discouraged Fonte: K. Jennings, K. Miller, S. Materna (1997) Hospital (specialits) Health Agent Primary Health Care $ CONTEXTUALIZATION Descentralization of Public Health System

6. Slide 6 PROBLEM Primary Health Care Primary Health Care Hospital (specialits) Hospital (specialits) Health Agent Health Agent Management Information Message Data Acquisition CONTEXTUALIZATION  Increasing Complexity of Health Management for Decision-making Increasing Complexity of Health Management for Decision-making

7. Slide 7 CONTEXT-AWARE FRAMEWORK SOLUTION ONTOLOGY BAYESIAN NETWORK Metadata Geolocation Decision- Making Information Health Knowledge Inference Mechanism PROBLEM  CONTEXTUALIZATION  LARIISA: an Intelligent System to support decision-making process DATA MINING

8. Slide 8 Data Acquisition (Patient CONTEXT) DECISION-MAKING LARIISA’s Scenario: a context-aware framework METADATA Option 1 Option 3 Option 2 LARIISA: A Context-Aware Framework LARIISA: A Context-Aware Framework ONTOLOGY BAYESIAN NETWORKS DATA MINING

9. Slide 9 2. Application Scenario

10. Slide 10 Scenario for LARIISA Application CAREGIVER - an unpaid or paid person who helps another individual with an impairment with his or her activities of daily living. 2. Motivation: The Brazilian Digital TV LARIISA can help Specialized person Or NON specialized person

11. Slide 11 Prague, Czech Republic July/2013 Data acquisition - CONTEXT

12. Slide 12 LARIISA Data Acquisition

13. Slide 13 ONTOLOGY BAYESIAN NETWORKS DARA MINING Data Acquisition (CONTEXT) METADATA DECISION-MAKING LARIISA: Next Generation

14. Slide 14 3. The Brazilian Digital TV

15. Slide 15 Analógico Today Interactive Digital TV Digital The Brazilian Digital TV

16. Slide 16 Network Audio Video Data Data Carrossel 1. MOTIVATION Interactive Digital TV

17. Slide 17 Interactive Digital TV The Brazilian Digital TV

18. Slide 18 Prague, Czech Republic July/2013 Architecture of the Brazilian Digital TV GINGA, a recommendation H.761 of the International Telecommunications Union (ITU-T).

19. Slide 19 4. LARIISA Project

20. Slide 20 Medical Sensors Humidity Sensor Atmospheric Pressure Sensor Temperature Sensor Digital Camera Global Positioning System (GPS) Accelerometer Internet Connection Light Sensor Proximity Sensor Compass Gyroscope Geographical Information System (GIS) LARIISA: a Context-Aware Framework

21. Slide 21 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 21 LARIISA: a Context-Aware Framework The photo IMG001 was taken at lat=S 3° 45' 48.6532“, lon=W 38° 36' 28.7332“ on February 2nd, 2013 at 8:00AM. Address =Av. G, 162-229-Conj. Ceará, Fortaleza – CE. Local Temperatura=29°C. Comment= Dengue Habitat. 80%-90% likelihood of being with Dengue (Local Context) Information: lat=S 3° 45' 48.6429“, lon=W 38° 36' 28.7434“ on March 3rd, 2013 at 5:00PM. Body temperature=40°C, Heart rate=110 bpm, Blood pressure=140/90. Address=Av. F, 126-298-Conj. Ceará, Fortaleza – CE. Local Temperature=25°C. Symptoms=Headache, Vomiting, Body aches. Patient B Patient A Health Agent Relocating a health agent for the Patient B’s house Information: lat=S 2° 22' 32.9483“, lon=W 34° 33' 21.4657“ on March 24th, 2013 at 3:00PM. Body temperature=37°C, Heart rate=90 bpm, Blood pressure=120/80. Address= Av. da Sé, n° 227, Conj. Palmeiras, Fortaleza – CE. Local Temperature=32°C. Symptoms=Chills, Diarrhea. Who is the patient? Are Data Structured? Who is the patient? Are Data Structured?

22. Slide 22 3° 45' 48.6429" 38° 36' 28.7434" 17:00 03/02/2013 Av. F, 126-298-Conj. Ceará, Fortaleza - CE 209968974640021 A, B, C 40°C 110bpm 140/90,,,,,,,,,, metadata file Building a metadata file Geolocation Patient Identification via Web Service Health Information

23. Slide 23 Context Providers Data Processing Data Acquisition Publishing User Device Internet Health Agent Device Symptoms + sus_id Global Context Local Context Inference Rules Context Aggregator (CA) Health Managers System Security Protocol Metadata LARIISA’s Architecture: a context-aware framework

24. Slide 24 IN LARIISA_BAY Inference Module Patient Health Agent Specialist Decision Module Interface Specialist Decision Module 1 1 Specialist Decision: f(%) 3 3 Pass Through A  f(%) A = A’ 2 2 Specialist Validation: A  f(%) A ≠ A’ RB % SITUATION ROOM Health Agent OUT A’ B’ C’ A’ B’ C’ C B A A C B OTHER CONTEXT PROVIDERS A’ B’ C’ LARIISA INFERENCE MODULE Sensors Health Center Ambulance METADATA User Interface Inference Rules Global Context Repository Local Context Repository Patient Specialist Epidemic Graph Manager LARIISA: Functional Diagram

25. Slide 25 5. Prototype

26. Slide 26 Data Acquisition (Patient CONTEXT) DECISION-MAKING LARIISA’s Prototype METADATA Option 1 Option 3 Option 2 LARIISA: A Context-Aware Framework LARIISA: A Context-Aware Framework ONTOLOGY BAYESIAN NETWORKS

27. Slide 27 Local health context model Global health context model Prototype: Dengue Fever Case Study Metadata

28. Slide 28 ENTRADA DO SISTEMA Módulo de Inferência do LARIISA_Bay Paciente Agente de Saúde Especialista Interface Módulo de Decisão Módulo de Decisão 1 1 Decisão do Especialista: f(%) 3 3 Pass Through A  f(%) A = A’ 2 2 Validação do Especialista: A  f(%) A ≠ A’ RB % SALA DE SITUAÇÃO Agente de Saúde SAÍDA DO SISTEMA A’ B’ C’ A’ B’ C’ C B A A C B OUTROS PROVEDORES DE CONTEXTO A’ B’ C’ LARIISA LARIISA_Bay Sensores Posto de Saúde Ambulância METADADO Interface do Usuário Regras de Inferência Repositório de Contexto Global Repositório de Contexto Local Paciente Especialista Gráfico de Epidemias Gestor Screens of the proposed System Patient Health Agent Specialist

29. Slide 29

30. Slide 30 Conclusion

31. Slide 31 Conclusão Diga Saude (FUNCAP) SISA (FIOCRUZ) LARIISA (IFCE) GISSA (FINEP) Next Saude (DATASUS / Min Saúde) Sponsors LARIISA project is being sponsored since 2004 by the Science and Technology Ministry of Brazil and others Brazilian Research Agencies It will be applied to the brazilian public health system LARIISA project is being sponsored since 2004 by the Science and Technology Ministry of Brazil and others Brazilian Research Agencies It will be applied to the brazilian public health system

32. Slide 32 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 32 Mauro Oliveira mauro.oliveira@fortalnet.com.br +55 85 9705 4321 www.maurooliveira.com.br

33. Slide 33 MUITO OBRIGADO