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Exploring Science Fiction Prototypes in Persuasive Tele-Health 6 th International Conference on Intelligent Environments (IE2010) Science Fiction Prototyping Workshop Invited Talk Sumi Helal July 19, 2010 Outlines Introduction


  1. Exploring Science Fiction Prototypes in Persuasive Tele-Health 6 th International Conference on Intelligent Environments (IE2010) Science Fiction Prototyping Workshop Invited Talk Sumi Helal July 19, 2010

  2. Outlines � � Introduction � � Problem: � � Present Telehealth Systems and Limitations � � Solution (WIP): � � Framework for Persuasive Telehealth � � Action-based Behavior Model � � Promising Technologies for Persuasive Telehealth � � SFP Validation (WIP) � � Conclusion

  3. Present Telehealth Systems and Limitations � � There have been considerable academic, industrial, standard organizational and multi-national efforts to improve telehealth systems � � Continua Health Alliance, STEPSTONE, Center for Connected Health, Microsoft Vault, Google Health, Ambient Assistive Living (AAL) � � However, many telehealth systems are limited in that they mostly emphasize sensing and monitoring � � Intervention and behavior alteration for affecting changes are equally important and need to be further developed and researched.

  4. What is Missing? What is Needed?

  5. Persuasive Technology for Affecting Behavioral Change � � Interactive computing systems designed to change people’s attitudes or behaviors � � Pitfalls � � Potential misuse of persuasive technology � � Could be immoral � � Potential � � Applying persuasive technology to promote positive social goals such as promoting healthy lifestyle

  6. Why Behavior Alteration is not adequately addressed? � � We don’t have powerful tools for persuasion and affecting change. � � It is difficult for health telematics researchers and technologists to understand the necessary behavioral and social theory � � It is not easy to collaborate closely with social and behavioral scientists

  7. A Framework for Persuasive Telehealth Profiling: User+Domain-specific Knwoledge � � Monitoring: Monitors physical and mental status through sensory � � subsystem Analysis: Domain-specific analysis with personalized information � � Observation: Diagnoses the health status and indentify problematic � � behaviors Intervention: Increases motivation, ability and trigger with goal-setting � � and rewarding Convergence: Performs outcome assessment of behavior change and � � adaptation Profiling Monitoring Analysis Observation Intervention Convergence

  8. Action-based Behavior Model � � Goal: Bridge behavior change theories into models easily utilizable by health telematics researchers � � ABM is a simple finite state machines with generic actions and a states, which can be easily understood by technologists, and which can be implemented by existing technological channels.

  9. Action-based Behavior Model (cont.) � � The ABM consists of a cyber system and a set of actions � � Two types of actions: human and cyber actions � � Cyber Sense initially learns about the user’s profile and preferences and then it learns about the user’s actions and in general senses any relevant vitals and status information � � Cyber influence is where technological channels are used to manufacture controlled persuasion within ethical guidelines � � Cyber influence will affect and help the user to make each human action � � Each human action increases persuasion through one or more of its elements (motivation, ability and trigger) � � After the initial rounds of acting, the Assess cyber action will evaluate the achievements of the goals and advise the user to either continue or roll the user back to the appropriate action

  10. Action-based Behavior Model ������ Aware Profile �������������������������������������� Plan �������������������������� Cyber Learn Influences � � � � � & Cyber Sense � � � � � � � � � � Recall �������������������������� �������� ��������� ��������� Act ������ Reward ������������ ������������ Assess ��������� Restart Loops �������� �������� Convergence Loops

  11. Persuasive Healthcare System ������������� �������������� ���������������� ������������������� �������� �������� ��������� ���������� Goal Informer Educator Setter �������� ������� Simulator ��������� �������� Dynamic Reminder Assesser Adapter �������� �������� ����������� ��������� ���������������� ������������������ ���������������� ��������������� �������� ������ � �������� �������� ��������

  12. Obesity & Diabetes Self-Management

  13. Persuasive Telehealth: Technology Channels � � Existing Technologies � � House calls and AVR (Automated Voice Response) � � Web portals � � Mobile Messaging � � Promising Technologies � � Domestic Robots � � Social Networks Physician Social Network Family Psychologist Nutritionist Neighbors Friends Nurse

  14. Domestic Robots

  15. Domestic Robots

  16. Domestic Robots NAO

  17. Comparative Analysis D. Lee, A. Helal and T. Yamazaki, “Robotic Companions for Smart Space Interactions”, IEEE Pervasive Computing Magazine, April-June 2009

  18. NICT Study on Domestic Robots Usability

  19. NICT Preliminary Studies

  20. Validation

  21. The Girl in the Window by Brian David Johnson

  22. Story Characters � � Corina Poole, 15 years depressed teenager. � � Kevin – a domestic robot � � Frank Poole, Corina’s father � � Gina Poole, Cornia’s mother � � Dr. Sinclair, Corina’s rehab specialist

  23. Story Lines � � Kevin takes care of the Poole’s house � � Little Corina is depressed � � Kevin gets the rifts of Corina’s depression � � Kevin is attached to the family esp. Corina’s mother � � Emotions and Feelings around Kevin: � � Anger, hate, trust, distrust, love, secure comfort

  24. Conclusions � � Persuasive Telehealth could means: � � Robots at Home � � How do we program the robots? and how much we can trust them? � � Will they work effectively? � � If they work and they are adopted, are there any down side to using domestic robots? � � What if these connected robots form a robotic social network amongst themselves? Should we encourage such social networks? � � What could go wrong?

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