An Improved PIR Sensors Model for Indoor People Activity Detection

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

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 – • 1 detector • 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 • 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 – Pimod|

11. EXPERIMENTAL RESULTS • Everspring – SP814-1 • FIBARO Motion Sensors • Note: in bothcasesis 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 statoparzialmentefinanziato dalprogettoADALGISARegioneLombardia • CUP: E68F13000360009