This presents some experimental results on the detection of occupants in an office using PIR sensors only.

1. Occupancy level
estimation using
PIR sensors only
BASTIEN PIETROPAOLI,
DAVID ROJAS, PABLO CORBALAN, KIERAN DELANEY, DIRK PESCH

2. What’s occupancy?
 Three dimensions
 Most common:
• Binary/Head counts
• At the room level
• Time resolution app dependent
 Heisenberg’s principle
• Δoccupants × Δtime × Δspace ≥ min. cost
• Costs: $$$ and privacy
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3. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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4. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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5. Sensors used for occupancy detection
 CO2 / VOC
 Pros: Detect people independently of their activity,
• Cons: Expensive, low time resolution, not suitable for open spaces, highly sensitive to ventilation
 PIRs
• Pros: Cheap, already deployed, well-known
• Cons: Binary events, noisy peaks, cannot detect still people
 Sound
• Pros: ?
• Cons: Sensitive to external noises
 Cameras
• Pros: Highly reliable
• Cons: Privacy concerns, sensitive to obstruction and luminosity changes, computationally demanding
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6. Existing approaches (1/3)
 Counters at key places
• Pairs of PIR sensors
• Modified PIR sensors
• Cameras
• Wireless sensing
 Pros
• Simple in principle
• Cost effective
 Cons
• Error prone
• Propagated errors
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Zappi et al. 2010 Lin et al. 2011
Erickson et al. 2013
Hutchins et al. 2007

7. Existing approaches (2/3)
 Costless methods
• DHCP monitoring
• Laptop monitoring
• Calendars monitoring
• Wi-Fi monitoring
 Pros
• No new hardware required
 Cons
• Low time/space resolution
• Very low accuracy
• Potential privacy concerns
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Melfi et al. 2011
Martani et al. 2012

8. Existing approaches (3/3)
 Artificial intelligence
• Neural networks
• Decision trees
• Hidden Markov models
• Classification
• Topic identification
• Graphical models
 Pros
• Reliable
 Cons
• Massive training sets
• Complex modelling
• Meaningless modelling
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Ekwevugbe et al. 2013
Hailemariam et al. 2011

9. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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10. Seeking a simpler solution
Required qualities
• Cheap
• Short training set
• Simple models
• Privacy friendly
• Reliable head counts
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The solution
we need!

11. Our (ridiculously) small deployment
 PIR sensors
• One office
• Two sensors
• Four people
 First to test binary occupancy
• Integration to Wi-Fi sniffer project
• Indoor localisation improvement
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12. Save energy, keep reactivity
 Wireless sensor nodes
• Limited batteries
• Wireless com’ consuming too much
 Needs
• Reactivity
• All the events
 Solution
• Send a sequence start message
• A message every 5s maximum
• Over 11 days: 99727 msgs sent for 198842 events
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13. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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14. Binary occupancy, the classic (1/2)
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= bastard!

15. Binary occupancy, the classic (2/2)
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16. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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17. What features?
 Intuition
• More people = more motion events
• Integration might help
• Take the number of events!
 Enough data?
 Correlated enough?
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Int.
time 15s 30s 60s 90s 300s 900s 1800s
Corr. 0.741 0.803 0.846 0.866 0.909 0.929 0.928

18. Machine learning?
 Supervised, unsupervised?
 Which method?
 Training set?
 Enough features?
 Let’s test the simplest: linear
regression
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My colleagues
when I explain
how to use it.
Ground truth system
made of two buttons.

19. Linear regression, the simplest
 A matrix of features: nb of motion events
at various degrees
 A vector of real measurements: our
occupancy ground truth
 A closed-form solution
 Super fast prediction!
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𝑋 =
1 𝑥1,1 ⋯ 𝑥1,𝑛
⋮ ⋱ ⋮
1 𝑥 𝑚,1 ⋯ 𝑥 𝑚,𝑛
𝑌 =
𝑦1
⋮
𝑦 𝑚
Θ =
𝜃0
⋮
𝜃 𝑛
= (𝑋 𝑇
𝑋)−1
𝑋 𝑇
𝑌
𝑌 = 𝑋Θ 𝑦𝑖 = 𝜃0 +
𝑗=1
𝑛
𝜃𝑗. 𝑥𝑖,𝑗with

20. Some results
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21. Some results (rounded)
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22. Visualising the results (1/4)
 Seeking the best parameter combination
• Integration time
• Degree of the polynomial used
 Various criteria
• RMSE
• Accuracy
• Accuracy with tolerance
 Results averaged over all the days used to learn
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23. Visualising the results (2/4)
 RMSE
• Good estimate mixing both mean error
and standard deviation of the error
 Best
• Degree 2
• 900s integration
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24. Visualising the results (3/4)
 Accuracy (when rounded)
• Proportion of correct estimates
 Best
• Degree 1
• 900s integration
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25. Visualising the results (4/4)
 Accuracy with tolerance 1
• Take the floor or the ceiling of the
estimates
• Discriminate binary occupancy
 Best
 Degree 2
 900s integration
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26. Does the day matter?
Acceptable difference between worst and best day
 The best day tends to be the same for all the
parameter sets
 The ordering of parameter sets tend to be
respected for worst, average and best
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Int. Time Worst Average Best
15s 75.19% 77.08% 77.77%
30s 76.68% 78.97% 80.18%
45s 77.13% 79.49% 81.04%
60s 77.68% 79.83% 81.59%
90s 78.04% 79.99% 81.95%
120s 78.47% 80.18% 82.36%
150s 78.41% 80.29% 82.31%
180s 78.38% 80.33% 82.37%
300s 78.09% 80.43% 82.39%
600s 79.02% 81.30% 83.42%
900s 79.29% 81.56% 83.63%
1200s 79.66% 81.45% 83.42%
1800s 78.86% 81.09% 82.83%

27. Summary
 Existing approaches
• Sensor used
• Example of existing approaches
 Our approach
• Seeking a simpler solution
• Small deployment
• Saving energy
 Binary occupancy, the classic
 Machine learning
• What features?
• Linear regression
• Results
• Exploring the parameters
 Pros and cons/Conclusion
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28. Pros and cons
Requires only one type of sensor
 Well-known sensors
 Cheap and commonly deployed sensors
 Simple model
 Computationally extra light
 Accurate with a small training set
 Sensitive to sensor placement
 Model might be specific to the room
 Might not work in all types of environments
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29. Conclusion
 We did it!
• Small training set
• Computationally light
• Model easily understood
• Cheap sensors
• Acceptable accuracy
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30. Thanks for your
attention! Questions?
CONTACT: BASTIEN.PIETROPAOLI@GMAIL.COM/@CIT.IE
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