Context awareness

Context awareness makes your smartphone smarter.  For example, a context aware phone would know that you are taking a flight out of San Francisco airport at 11am, because it has access to your calendar. It knows that you were driving from your speed and location, and it also knows that you’re now looking for a parking spot. It’ll help you find one and remember where it is. Now you’re in the terminal, but you’re late, so it’ll guide you to the shortest security line and tell you which gate you need to be at. Finally, it’ll advise you to run to make the flight and it’ll bring up your electronic boarding pass for you, just in time to show it to the flight attendant.

Sensor data provides real-time confirmation of user activities that other information sources, like calendars or emails can only infer. The MotionQ Context Aware Library is a key enabler to new generations of smart mobile devices, because it is designed to:

  • Utilize sensors with low power consumption
  • Minimize computation when contexts are not changing
  • Use other sensors and system data as needed to assure context detection accuracy

 

 

Navigation systems that rely on GPS cannot function where GPS satellites are not accessible, for example in a mall or supermarket. In these environments, accurate internal navigation could not only benefit a consumer looking to find a specific item, but it could also provide marketers with a powerful, targeted way to reach out to that consumer with promotions. Traditional inertial navigation systems often cannot distinguish between the movements of a handheld device and the motion of the user, resulting in the rapid accumulation of large errors in a short period of time from incidental hand movements. Pedestrian navigation overcomes these limitations by:

  • Providing position and orientation information to phone/tablet apps
  • Using heuristics to permit incidental hand movements
  • Adapting and overcoming magnetic distortions
  • Working with smart sensors to offload app processor

Audience applies its deep understanding of human bio-mechanics and motion heuristics to distinguish between the user’s trajectory and extraneous movements. Its algorithm continuously adapts to changing real world environments.