occupancy level estimation using pir sensors only
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Occupancy level estimation using PIR sensors onlyBASTIEN PIETROPAOLI , DAVID ROJAS, PABLO CORBALAN, KIERAN DELANEY, DIRK PESCH
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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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BASTIEN PIETROPAOLI 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. 2011Erickson et al. 2013
Hutchins et al. 2007
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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
BASTIEN PIETROPAOLI 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
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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!
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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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BASTIEN PIETROPAOLI 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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BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 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
BASTIEN PIETROPAOLI 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.
BASTIEN PIETROPAOLI 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 𝑥𝑚 , 1 ⋯ 𝑥𝑚 ,𝑛)
𝑌=( 𝑦1⋮𝑦𝑚)
Θ=(𝜃0⋮𝜃𝑛)=¿
𝑌=𝑋 Θ 𝑦 𝑖=𝜃0+∑𝑗=1
𝑛
𝜃 𝑗 .𝑥 𝑖 , 𝑗with
BASTIEN PIETROPAOLI 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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BASTIEN PIETROPAOLI 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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BASTIEN PIETROPAOLI 24
Visualising the results (3/4) Accuracy (when rounded)• Proportion of correct estimates
Best• Degree 1• 900s integration
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BASTIEN PIETROPAOLI 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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BASTIEN PIETROPAOLI 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%
BASTIEN PIETROPAOLI 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
15/02/2016
BASTIEN PIETROPAOLI 28
Pros and consRequires 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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BASTIEN PIETROPAOLI 29
Conclusion We did it!• Small training set• Computationally light• Model easily understood• Cheap sensors• Acceptable accuracy
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