1. Long-range capacitive sensors for indoor person location
Mihai Lazarescu Luciano Lavagno DET

2. Contents Why indoor person localization?
System objectives and specifications
Capacitive transducers and operating principles
Sensor structure and characterization
Signal acquisition and processing
Localization algorithms
Localization experiments
Results and conclusions

3. Why indoor person localization?
Improve life quality and safety (home automation, security)
Elderly assisted living (detection of routine activity and deviations from it)
Sizable market, commercial products

4. System objectives and specifications
Tagless
People forget or are reluctant to wear tags
Passive
Track the person regardless of activities (do not require system interaction)
Privacy-aware
No pictures, no sound samples
Unobtrusive
Out of sight, no interference with daily activities

5. Operation modes of capacitive transducers
Transmit and shunt modes require two galvanically- connected plates
Difficult and costly installation
Load mode senses through one connected plate
Simple, low-cost sensor and installation
Solutions exist for distances comparable to plate size
Too short for target applications

6. Sensor node structure Copper-clad transducer plate
Capacitance-controlled oscillator
Frequency proportional to total plate capacitance
MCU measures oscillator frequency once per second
Sends measurements by radio to server

7. Sensor characterization (normalized)
Three plate sizes: 4x4, 8x8, 16x16 cm
Frequency normalized: min. 1, max. 100
Plate-body distance (d): 10 cm to 2 m
Capacitance C ~ 1/d^3
noise floor limits sensitivity, mainly for small plates

8. Sensor characterization (raw)

9. Server signal acquisition and processing
Low-pass 861-taps FIR
Eliminates environmental noise
High-pass 21-sample median filter
Eliminates slow sensor drift

10. Server signal acquisition and processing
4 sensors in a 3x3 m “room”
Different training and localization walking paths
Sensor data filtered and normalized
Body orientation matters

11. Localization algorithms (average error)
k-Nearest-Neighbors (k-NN)
Naïve Bayes (NB)
Support Vector Machines (SVM)

12. Localization algorithms (recall and precision)

13. Localization algorithms (path reconstruction)
4x4 cm x8 cm x16 cm
k-Nearest- Neighbors (k-NN)
Naïve Bayes (NB)
Support Vector Machines (SVM)

14. Next steps Promising results using simple techniques
Explore other low-noise high-sensitivity low capacitance variation measurement techniques (ΔC < 100 ppm)
Reduce sensor power consumption (energy scavenging?)
Reduce overall cost (sensor, deployment)
Monitor daily routine in a “real” room
Infer typical behaviors
Detect and notify deviations