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090223 Pervasive Behavior Tracking For Cognitive Assistance


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090223 Pervasive Behavior Tracking For Cognitive Assistance

  1. 1. Pervasive behavior tracking for cognitive assistance
  2. 2. Reference  Sylvain Giroux , Jérémy Bauchet, Hélène Pigot, Dany Lussier-Desrochers, and Yves Lachappelle, “Pervasive behavior tracking for cognitive assistance” PETRA'08(PErvasive Technologies Related to Assistive Environments), July 15-19, 2008, Athens, Greece
  3. 3. Outline  Introduction  Autonomy and Cognitive Deficits  Smart Homes  Sensors network  Middleware  Applications  Behavior Tracking  Cognitive Assistance to ADLs  Prototypes  Experimentation  Future Works  Conclusion
  4. 4. Introduction  In many cases, people with cognitive impairments would can stay at home if a light assistance was provided, but resources are scarce  The current demographic trends bring to forecast a dramatic increase of demand for care resources from people with cognitive impairments  Smart homes are environments augmented with networked sensors, communicating objects, embedded computers, and information appliances
  5. 5. Autonomy and Cognitive Deficits  In a study made both in Quebec and France [10], four categories of cognitive deficits [11] were identified as primary responsible of autonomy loss in the daily life of cognitively impaired people: Initiation deficits Planning deficits Attention deficits Memory deficits
  6. 6. Smart Homes
  7. 7. Smart Homes
  8. 8. Sensors network  electromagnetic contacts  electronic tags by the mean of Ultra Wide Band (UWB) enabling their localization in 3D.  RFID tags  Flow meters  Powerline communication devices  infrared sensors movement detectors(big) or sensitive rugs(small)
  9. 9. Middleware
  10. 10. Applications  Medical assessment  Cognitive assistance  Tele-monitoring  This paper focus on services dedicated to cognitive assistance for activities of daily living (ADL)
  11. 11. Behavior Tracking  Carberry identifies three main issues: system robustness in the face of noise in the input effective discrimination among competing hypothesis recognition algorithms that scale up to large domains  More theoretical and long term approach are based on lattice-based models enhanced with probabilities to recognize ADLs and to anticipate erroneous plans classified according to cognitive errors Bayesian networks Petri nets combined to a tangible user interfaces approach based on the “Token and constraints” model  rule-based approaches and ad hoc modeling
  12. 12. Cognitive Assistance to ADLs
  13. 13. Cognitive Assistance to ADLs  The interaction modality (visual, vocal, video, for instance highlighting an object with a LED) also have to be chosen, according to the user profile and the assistance strategy  need to set some principles to guides the development of cognitive assistants for cognitively impaired people The assistant has to foster the autonomy of the person The system is not intended to replace caregivers so there is always a person at the end of the system The user should have control, so it is a mixed control of the interaction The hardware and software setting should be as unobtrusive; so as least sensors as possible will be involved.
  14. 14. Prototypes  Two prototypes were implemented for assisting cognitively impaired people at home.  The first one is monitoring ADLs related to the morning routine  The second one is focusing a specific ADL, namely meal preparation
  15. 15. A general pervasive cognitive assistant
  16. 16. A general pervasive cognitive assistant
  17. 17. Archipel  simplified illustrated recipes books; the Visual Assistant provided by AbleLink Contextual information is neither used nor available  Archipel is a cognitive assistant relying on the hierarchical structure described previously and interacting with the environment in a manner  through the IO Event server → advance automatically to the next step
  18. 18. Archipel
  19. 19. Archipel
  20. 20. Experimentation  12 people with mild intellectual disability has been performed in the smart apartment of DOMUS  Some of them were not able to read  For each participant, the experimentation was performed over a three-day period  Help needed has been compared for both experimental conditions.  Preliminary data analysis suggests that Archipel reduced human assistance by half
  21. 21. Future Works  Developing deep cognitive modeling to better anticipate errors and analyze their causes to provide for more subtle assistance.  Developing multi person localization services because currently our localization systems works best when there is just one person in the apartment which is not a realistic setting.  Extending Archipel towards a Virtual Community Kitchen
  22. 22. Conclusion  Smart textiles, sensor networks, ubiquitous input and output devices could combine to provide what can be considered a computer-based cognitive prosthetics  proposed a cognitive prosthetic which interacts with the person and assist him through the environment  The cognitive assistant supports the person during the completion of ADLs in a non-intrusive way