1. An Improved PIR Sensors Model for Indoor People Activity Detection
Fabio Salice
Politecnico di Milano - DEIB
Sara Comai, Matteo Matteucci, Hassan Saidinejad and Fabio Veronese

2. Behaviour Drift Compensation for Autonomous and Independent Living
FRAMEWORK
BRIDGe
BRIDGe Project
Behaviour Drift Compensation for Autonomous and Independent Living
CRAiS: one out of five CTVAI (centro territoriale per la vita autonoma e indipendente) of Regione Lombardia
ATG: Assistive Technology Group
Interdisciplinary
Behavioral Drift Identification and Compensation - BRIDGe
Behavioral Finger Print Identification
Mutual Reassurance for Autonomous and Independent Living

3. GOAL
User activities detection through wireless Pyroelectric Infrared (PIR)
PIR stochastic characterization
Stochastic model, experimentally calibrated for the detection of a moving person, which takes into account also speed, direction of movement, and distance from the sensing element.
Effect of PIR placement and interaction.

4. PREVIUOS WORKS
Simple model: deterministic model where activation is “1” if a person crosses the sensible area (detection range)
It ignores the speed of movement of the person, the distance from the sensitive element, the emission area of the person and the period of insensitivity
Cornel Model: experimental approach where the detection distance is a function of the other parameters
Habib Model:
probability of detecting an event at point p by sensor si
β physical parameter
2 ≤ α ≤ 4 (equal to 2 in free-space),
δ(,) Euclidean distance between the si and the object.
P(p, si) = {e-βδ(p,si)α if δ(p,si)≤r; 0 otherwise}

5. SINGLE PIR MODEL
Proposed PIR model: combination of a geometric model and a motion model.
The geometric model:
the maximum detection angle (field of view),
the discretization of the detection angle into sectors,
the detection depth with its discretization into traces.
Example: wall-mounted

6. SINGLE PIR MODEL
Proposed PIR model: combination of a geometric model and a motion model.
The motion model:
the direction of the movement (radial or tangential)
the user speed
four intervals representing the average behavior of people during their daily life (i.e., slow movement, slow steps, normal step and quick steps).

7. SINGLE PIR MODEL
Each elementary geometric area is characterized by a probability to detect a movement with respect to the movement direction and speed.
Example: wall-mounted - radial detection

8. SINGLE PIR MODEL Arbitrary motion detection – Example - 3 detectors
K detectors
Example - 3 detectors
Pics = P||ics (1-|sin(βc)|)+Pics |sin(βc)|
Pcs = 1 - j=1..k (1-Pjcs)
Detection probability with 3 sensors with motion at an angle of 45°, for each motion model

9. SINGLE PIR MODEL Arbitrary motion detection – 1 detector
Arbitrary motion detection – K detectors
Pics = P||ics (1-|sin(βc)|)+Pics |sin(βc)|
Example: Probability of detection at an angle of 45°
Pcs = 1 - j=1..k (1-Pjcs)

10. EXPERIMENTAL RESULTS Two different Z-Wave Sensor Experiments:
Everspring SP814-1
Fibaro FGMS-001 V2.4
Experiments:
A fixed point in the room
25 measurements for the four speed models, along 4 directions (0°, 45°, 90° , 135°)
A total of 400 measures for each point.
Figure of merit:
Ai exp : experimental average detection activity
Pi mod : detection probability from the model.
 =i=1..4|Ai exp – Pi mod|

11. EXPERIMENTAL RESULTS Everspring – SP814-1 FIBARO Motion Sensors
Note: in both cases is an under estimation

12. CONCLUSIONS The stochastic model is reliable
The model allows the design of PIR positioning to maximize the people indoor detection probability
Usable also for coarse grain localization
Future works: sensors fusion to implement dependable solutions
Fault detection and toleration

13. GRAZIE PER L’ATTENZIONE
Il lavoro è stato parzialmente finanziato dal progetto ADALGISA Regione Lombardia
CUP: E68F