1. 11th International Conference on Mobile and Ubiquitous Systems:
Computing, Network and Services – Dec 2nd-5th London
F. Veronese, S. Comai, M. Matteucci, F. Salice Method, Design and Implementation of a Multiuser Indoor Localization System with Concurrent Fault Detection
Fabio Veronese –
Biomedical Engineer, IT PhD Fellow
ATG – Assistive Technology Group
Dept. of Electronics Information and Bioengineering

2. AAL – Ambient Assisted Living
Introduction
Indoor Humans Localization
LBS – Location Based Services ↓
Advertising
Notification – Services… any?
AAL – Ambient Assisted Living

3. Methods, Design and Implementation
Outline
Motivation
Methods, Design and Implementation
Experimental Results
Simulations
Conclusions
Future Work

4. What are users asked to do?
Usually IT approaches refer to:
INVASIVENESS
WEARABILITY
MOBILITY
BUT…
… is the USER where we locate the device?

5. Dependability as a Target
Indoor Humans Localization systems rarely provide a Dependable Service
AAL Case Study implies: - User’s Needs Centered System Design -
- Instrumented Environment -

6. Errors, Faults and Failures
The result of localization can be not reliable
WHY? → FAULTS!
Natural Hardware Human Made
HOW can we DETECT them?
Designing a Model of the systems
Gathering extra data from another system

7. IHL systems provide an area where the person is with a certain error
Modeling IHL Systems
IHL systems provide an area where the person is
with a certain error
Let us consider the area
centered in the estimation
with a range based on the
localization performances
(3m ~ 84h percentile)

8. Gathering Information
In AAL settings humans interact continuously, repeatedly and inevitably with Home Automation (HA) systems. →

9. Anonymous Interaction Detection
Let us build a MODEL of interaction with DWS*
What about More informative sensors? rs
( x,y )
Inactive No Information
Toggled Person Interaction
* Door Window Sensor

10. PIR* sensors have a more complicated behavior
Presence Detection
PIR* sensors have a more complicated behavior
Inactive
Active
Inactive
( x,y ) rs
No Movement
Inactive
Active
Delay
Active
Inactive
Persistence
* Passive InfreRed

11. Presence Detection Model
Let us define the behavior by cases:
Entering and exiting a Sensor Area:
Stopping in a Sensor Area
Ideal
Real
Ideal
Real

12. Concurrent Fault Detection
Both systems have outputs based on the user position
Indoor Humans Localization
Human’s Real Position
Fault Detection
Apparatus
Home Automation
Sensor-detected Error
Localization-detected Error

13. Fault Detection Policies
The sets of possible locations from
IHL and HA must allow a valid position
All the HA activity must be motivated by
a valid IHL position ? ?

14. Going MultiUser The handle multiuser we have to keep memory of the
last IHL and HA valid position for each localized user ↓

15. Employed system were implemented based on AAL scenario settings:
Who’s who?
Employed system were implemented based on
AAL scenario settings:
Indoor Humans Localization
Fault Detection
Apparatus
Home Automation

16. CAVEAT - Fault Observability
“If a tree falls in the forest and
no one is around to hear it…
did it really fall?”
If the user does not create a fault condition to produce an error which is visible then is the fault really happening?

17. Person Experimental Path
Real World Tests
TESTS
No-fault Condition Natural Fault (Blinded Sensor) Human Fault (Forgotten Device)
Sensor Positioning
Person Experimental Path

18. Real World Results
NO FAULT
FORGOTTEN
DEVICE
SENSOR
BLINDED

19. Making Real World Grow
Limiting factors were making this not affordable:
Few Devices Available
Impossibility to test with several users
Purchasing and Acquisition Times ↓
Large Environment Simulations:
Unlimited Ideal Sensors
Large Datasets Fast Generation

20. Simulated Tests
TESTS
No-fault Condition Natural Fault (Blinded Sensor) Human Fault (Forgotten Device)

21. Both Sensitivity and Specificity over 90%
Simulations Results
FORGOTTEN DEVICE
NO FAULT
BLINDED SENSOR
Both Sensitivity and Specificity over 90%

22. Wrappin’ up
The proposed method is based on the definition of a model representing a IHL and an HA systems, defining joint consistency conditions.
The validity of the approach is proved applying it to a case study. The chosen case study subsystems are: LAURA localization system and a Z-wave based HA.
The obtained experimental results showed the validity of our approach, correctly reporting errors in fault-free and fault injected conditions.
Furthermore, we generated multiuser data, creating them based on the knowledge of the environment and the systems.

23. Conclusions
Results of multiuser simulations show the system correctly detecting faults also in case of several targets. Both specificity and sensitivity above 90% represent a satisfying performance.
Nonetheless, if the application requires it, system model parameters can be tuned to benefit selectively sensitivity or specificity.
Concluding, our approach, even under some limitations in terms of fault observability, enables the dependable localization of a set of persons inside an instrumented house, detecting both natural and human-made faults.

24. Future Work GROWTH Real World installation Pattern Identification
Fault Identification and Localization

25. Thanks Fabio Veronese – fabio.veronese@polimi.it
Biomedical Engineer, IT PhD Fellow
ATG – Assistive Technology Group
Dept. of Electronics Information and Bioengineering