Adaptive context-agnostic floor transition detection on smart mobile devices

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1 Adaptive context-agnostic floor transition detection on smart mobile devices Salvatore Vanini University of Applied Sciences of Southern Switzerland (SUPSI) CoMoRea Workshop - March 18 th, 2013

2 Contents Altitude changes estimation: basics of barometer-based height sensing Scenarios and data of preliminary experiments Characteristics of pressure readings Floor transitions detection algorithm Empirical results Conclusion and future work

3 Pressure and Altitude Basic idea: altitude changes estimation with pressure Pressure = force exerted by air per unit area (Pa= N/m 2 ; 1hPa=100Pa) The relationship between pressure P and altitude h can be defined as: h = (1 (P/P 0 ) (k * R/g) ) * T 0 /k P 0, T 0 : pressure and temperature at sea level R: universal gas constant ( m²/s²) K: temperature gradient with altitude ( K/m) g: acceleration of gravity

Experiments data and scenarios Two Android phones (Samsung Galaxy Nexus/SIII)

4 Experiments data and scenarios Two Android phones (Samsung Galaxy Nexus/SIII) Different pressure sensors: Bosch BMP180 and STM LPS331AP Sensors accuracy: ±0.12 hpa and ±0.1 hpa Pressure and acceleration in Z axis at our main building (341 m a.s.l.) in 4 different scenarios: Phone laying on a support (stationary mode) Walking on the same floor (walking mode) Stepping up/down stairs (step up/down stairs) Elevator from basement to second floor (elevator) Pressure sample rate: approx. 1 Hz

5 Absolute vs Relative pressure readings gap of about 1.7 hpa in raw pressure readings of the two phones (same place and time) Absolute pressure values vary with time Absolute pressure readings are not reliable for correlating pressure to altitude Raw pressure data presents distorted readings. Double exponential smoothing to produce unbiased pressure data: s t = αx t + (1 α)(s t-1 + b t-1 ) b t = γ(s t s t-1 ) + (1 γ) b t-1 α: smoothing factor γ: trend smoothing factor

6 Characteristics of pressure readings stationary mode: pressure (σ<0.1hpa) and acceleration are stable (±0.25 m/s 2 ) walking mode: pressure relatively stable, acc. oscillates frequently (± 3.5 m/s 2 )

7 Characteristics of pressure readings stepping up/down stairs: pressure decreases/increases, floor transitions identified by pressure discontinuities, acceleration follows layout elevator: pressure decreases/increases (1 hpa difference between floors), acceleration spikes

8 Floor transitions detection algorithm 1. Stationary state: fixed bounds of pressure and acceleration 2. Phones had the same pressure differences between floors and the same dynamics for pressure variations Algorithm: Checks the relative difference of pressure readings Identifies stationary mode (acc < threshold) If stationary mode updates pressure mean and std dev and checks if pressure comprised in PRESSURE_ACCURACY (3 * std_dev: three-sigma rule) If other dynamic mode, pressure will decrease/increase till user commutes to stationary mode: change of sign between pressure differences Checks if pressure is within PRESSURE_ACCURACY for MIN samples to confirm transition

9 Results Experiments conducted implementing an Android application showing floor transitions Scenario 1: accuracy as a function of MIN samples value when user walking in the basement, stepping up stairs and then walking on the first floor MIN samples Accuracy (%) 2 82% 4 100%

10 Results Scenario 2: user taking the elevator and stepping down stairs (PRESSURE_ACCURACY=0.11 hpa) Scenario 3: PRESSURE_ACCURACY convergence (with stationary mode conditions)

11 Results Scenario 4: independence from external factors (different building at 210 m a.s.l. higher pressure) Scenario 5: positioning of the phone. In all experiments, capability to detect floor changes was unaffected by changes in phone s on body-location and orientation

12 Features No any prior knowledge of building layout No need to include information about heights No dependence on the transport mode used No need of any additional HW nor any calibration of the barometric sensor Accuracy is independent from phone s positioning Demonstrated effectiveness of floor transitions detection

13 Future work Investigation of the power consumption Integration into an existing indoor localization system Conversion into an add-on easily integrated in a 2D indoor localization system Large-scale evaluation of the level of accuracy in indoor location

14 Questions

Implementation of Height Measurement System Based on Pressure Sensor BMP085

017 nd International Conference on Test, Measurement and Computational Method (TMCM 017) ISBN: 978-1-60595-465-3 Implementation of Height Measurement System Based on Pressure Sensor BMP085 Gao-ping LIU