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Collaborative Context Recognition for Mobile Devices

This software focuses on collaborative context recognition for mobile devices, integrating sensors for a real contextual experience. It explores the transfer of human context-driven behavior to machines, merging interaction design with human-computer integration. The system utilizes various sensors and applications, aiming to become part of a global computing paradigm shift. The implemented models and interpretations enable context awareness and signal processing, facilitating group awareness and system flexibility. The architecture suggests stronger frameworks and platforms, emphasizing business layer improvements and potential ontologies for future implementations.

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Collaborative Context Recognition for Mobile Devices

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  1. Software for Context-Aware Multi-User Systems Professor Joao Sousa David Gonzalez Collaborative Context Recognition for Mobile Devices

  2. Overview Summary of Huuskonen CCR Theory Abstract. Model Interpretation. Implementation Close look. Long-term context Related works. Recommendations.

  3. Theory Abstract • Once upon a time.... Mobile Devices(MD) were too limited(e.g. Power computing, Energy dependent, not common). • Well, still is like that but they are “ubiquitous”. • PCs are not “wearable”, but MDs are. • MD User Interface are limited, but they are Communication Hubs.

  4. Theory Abstract • Human Computer Interaction(HCI) must integrate Sensors to engage a real Context experience. • Sense of: • Location • Social Situation • Tasks • Activities • Must be easy to the user, but the implementation is not trivial.

  5. Theory Abstract • Context Awareness (CA): • Humans are a “Rank-A” CA animals, because: • We use CA for primitive functions like Survival, Reproduction and Subsistence. • Imitate and Learn is a common behavior, so We are Context-driven individuals. • The issue is how transfer this to Machines.

  6. Simple Model for Human Behavior Doubt Imitate Do CA Lost Ask

  7. Mobile Context Awareness • This is the first step to allow CCR. • It merges IA and HCI. • Examples: • Location • Environmental Sensors • Biometrics • Acceleration sensors • Multimedia

  8. Application Area • Geomarketing • Jaiku • Clarity Brickstream • Nintendo 3DS • Latitude by Google

  9. Long-term goal • State CCR as part of global Initiative. This is not isolated research, but a common effect of Computing Paradigm Shift. • Establish improvements to the current architecture. Till now the architectures work, but lack of new frameworks to ease the inherent flexibility of this kind of systems.

  10. Model Interpretation • A CCR Looks like: Process Method Context Reasoning CCR Server Context Recognition Weighted Voting Protocol Context Awareness Signal Processing Sensors signals

  11. Model Interpretation • A CCR System Looks like: Process Actor Context Reasoning CCR Server Context Recognition Mobile Device Group Context Awareness Mobile Device Sensors signals

  12. Implementation Close look Actor Apache Tomcat Windows, Linux CCR Server Symbian S60, IOS Mobile Devices

  13. Development up to present • State CCR as part of global Initiative: • 2008, Bannach – Context Recognition Network • 2005, Sung & Blum – Wearable computing • 2003, Huuskonen – CCR for MD

  14. Recommendations • New SW Platforms are requires, in this particular case: Android. • Stronger Architecture are required in the Business layer, specifically Web Services. • Ontologies are proposed, not yet implemented.

  15. Architectureideas Presentation Rich User Interfaces, Context Aware like DK Business More Flexibility and spreadable with Web Services Data Access Data Mining for new Contexts rules

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