1. Inspiring Experiences in Defence and Health Care with E-partners: a New Personal Support Concept Joris Janssen TNO Human Factors, Soesterberg

3. ePartners: next generation interfaces 22 Intuitive interaction One interface for N machines Personalized and adaptive ePartner

4. 10 januari 2011 3 M Bouman TNO Nieuwe huisstijl

5. Networked ePartners for man- machine teams UGS Unmanned Vehicle Reach-back Command Post ISR Fighting units Robust network Soldier - Defense Care Space

6. External Systems User

7. Experiences in two domains Military command and control task Healthcare application: ALIZ-E

8. Example of Air Defense Task Object Identification Unknown Friendly Assumed friendly Neutral Suspect Hostile

9. 8 De Greef. T.E., Arciszewski, H.F.R., and Neerincx, M.A. (2010). Adaptive Automation based on an Object- Oriented Task Model: Implementation and Evaluation in a Realistic C2 Environment. Journal of Cognitive Engineering and Decision Making, Vol 4, 152-173

10. Human World View is leading ePartner can inspect both Views

11. 10 ePartner advices “Hostile” ePartner sets “Friendly”

12. Working agreements, per Track per Task

13. Adaptability is making the Track Sets adjustable Working agreements, per Track per Task

14. Participants Eight (LVO, CCO, ALVO, ACCO) Two Observers (LVO, CCO) Four Scenarios ePartner’s (Adaptive) Level of Autonomy Fixed (CONSENT mode) or Adaptive (three configurations) Presented to Participants in Varying Configurations Prototype evaluation

15. Results Summary Measures of Performance better Observed Situation Awareness unchanged Observed Workload, Correctness unchanged Subjective Workload unchanged 14

16. Experiences in two domains Military command and control task Healthcare application: ALIZ-E

17. 10 januari 2011 Social robots Adaptive, Long-Term and Social Human-Robot Interaction Supporting Autonomy and Safety of Elderly

18. Funding EU 7th framework program Partners Vrije Universiteit Brussel, Belgium Imperial College London, England Fondazione Centro San Raffaele, Italy DFKI, Germany University of Plymouth, England University of Hertfordshire, England TNO, The Netherlands Robot NAO robot Goal Long-term interaction for children with diabetes 17

19. Setting Children with diabetes in Italy stay one week in hospital Introducing a robot with three roles: a motivator, educator, buddy for the child Combining fun with education Helping the children with basic skills, such as arithmetic (which is useful to calculate carbohydrate intake for their nutrients)

20. Dance Diary Quiz Dance Child and robot play quiz asking each other questions about topics including health related issues Child and robot play imitation game making a sequence of arm gestures. Each turn a player repeats the sequence and adds a new gesture to it. Child shares personal diabetes diary with the robot. Robot helps child with achieving goals (increasing self- efficacy) Robot and child dance together to stay healthy and create a bond. Quiz Diary Quiz Dance Diary Imitation Quiz

21. Study: Performance and motivation RQ: Does adaptation on performance increase motivation to play in a robot game? Integrated an engaging game (simon game) with learning part (arithmetic) MeasuresPerformance on arithmetic, Questionnaire Free choice

22. Discussion and Challenges Adaptation on personal level increased motivation for robot interaction Challenges Implementing a user model that personalizes behavior of the robot towards child Integration of activities, and personalisation/scheduling over activities Extending the set of (objective) measures for child research

23. Conclusion ePartner concept Intuitive interaction Adaptive and personalised Gateway to multiple machines ePartner helping navy operators ePartner helping children with diabetics coping with their disease

24. Looking forward to new ePartner applications… Contact Joris.Janssen@tno.nl TNO Human Factors Motivating children to learn arithmetic with an adaptive robot game (2011) Joris B. Janssen, Chrissy C. van der Wal, Mark A. Neerincx, and Rosemarijn Looije