image/svg+xml Miquel Perello-Nieto, Raul Santos-Rodriguez, Peter Flach Abstract Real vs Synthetic (rooms) (rooms) Real Walkaround Synthetic Walkaround Real houses Synthetic houses Leveraging synthetic data for indoor localisation with missing sensors Indoor Localisation -70dB -90dB -34dB Annotatedlocations Signal strenghtto all sensors -Obstacles interfer inthe signal propagation -35dB air -100dB furniture missing wall Missing Sensors - In any real project with lots of sensors it is expected for someof them to fail. - It is necessary to design algorithms that are robust againsunexpected sensor failures. - Example of predictions in a real house in which some sensors failed during last month.(The figure aesthetics and legends are different for non related reasons). - Side by side slice - An algorithm that does notexpect sensor failures onlypredicts bedroom during lastmonth (all purple). - An algorithm expecting sensorfailures indicates that the bedroom isused mostly during the night periods, andother rooms are used during the day. Leveraging Synthetic Data 1. room locations 2. Perfect path 3. Add movement 1st floor 2nd floor 3rd floor 1st floor 2nd floor 3rd floor - Its graph representation - For each room we can perform random walkarounds - In a matrix form - We need large amounts of data to statistically validate if one method is more robust againssensor failure than another. - This is a real house layout - We can generate infinite house layouts withdifferent room sizes, locations,sensors, signal quality and sensor failures. - We can generate synthetic houses with realistic number of sensors and rooms. Results (work in progress) Real Synthetic Synthetic (sensors broken) - Example performance in one house Real Synthetic Synthetic(sensors broken) Real Synthetic Synthetic(sensors broken) - Performance of one method in all real houses and50 synthetic houses. - Statistical comparison of different methodsin all real and 50 synthetic examples. Technologies for this presentation inkscape.org sozi.baierouge.fr homestyler.com - Free- Open Source- Vector Graphics Editor - Free- Open Source- Slides from a Scalable Vector Graphics image - Create house designs and furniture distribution in the web browser Tracking activities of daily living and daily routine provides clear indicators ofwell-being. However, most research in Ambient Assisted Living is performed incontrolled environments. While in real-world scenarios sensors may fail, andsensor signals may change due to external reasons (eg. redistribution of furniture).We are investigating good Machine Learning algorithms that can predict indoorlocations even with sensor failures. In order to draw statistical conclusionsfrom different methods we generate synthetic data as close as possible to ourreal deployments in which we can generate different types of failure. Finally,the best methods are applied into the full set of real deployments. - It is possible to estimate the participants' location based on the "distance" (signal strength) from the wearable to all the receivers. - We use machine learning algorithms to learn the correspondence between "distance" values and room locations. - In order to get these pairs the technicians walk to every room and annotate their location during the installation day.
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  1. All
  2. abstract
  3. indoor
  4. indoor 1
  5. indoor 2
  6. missing
  7. missing 1
  8. missing 2
  9. leveraging
  10. leveraging 1
  11. leveraging 2
  12. real
  13. real 1
  14. real 2
  15. results
  16. results 1
  17. results 2
  18. results 3
  19. technologies
  20. technologies