Finding 9-1-1 callers in tall buildings

Abstract

Accurately determining a user's floor location is essential for minimizing delays in emergency response. This paper presents a floor localization system intended for emergency calls. We aim to provide floor-level accuracy with minimum infrastructure support. Our approach is to use multiple sensors, all available in today's smartphones, to trace a user's vertical movements inside buildings.We make three contributions. First, we present a hybrid architecture for floor localization with emergency calls in mind. The architecture combines beacon-based infrastructure and sensor-based dead reckoning, striking the right balance between accurately determining a user's location and minimizing the required infrastructure. Second, we present the elevator module for tracking a user's movement in an elevator. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Third, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users' foot steps, our stairway module uses a novel landing counting technique.

Step Detection Robust against the Dynamics of Smartphones

“…Conventional studies on step detection using accelerometers are based on thresholds [16,26,27,28,29,32,33,34] peak detection [18,21,22,23,24,25,30,35], auto-correlation [19] or spectral analysis [17]. …”

“…Threshold-based approaches include simple threshold [26,27,28,29,33,34] and zero velocity update (ZUPT) methods [16,32]. Simple threshold methods compare the magnitude of acceleration, its low-pass filtered version or other measures based on acceleration with pre-configured thresholds.…”

“…Simple threshold methods compare the magnitude of acceleration, its low-pass filtered version or other measures based on acceleration with pre-configured thresholds. ZUPT methods utilize the fact that each foot is regularly static during normal walking motion and that the accelerometer must report a certain period of inactivity [16,32]. Conventional ZUPT methods can be successful for step detection when the accelerometer is mounted on a foot [16,32].…”

“…Inertial sensors have been used in various applications, such as navigation for aerial and land vehicles, robotics, etc . The rapid development of micro-electro-mechanical systems (MEMS) has presented new possible application areas of inertial sensors, such as human activity monitoring [1,2,3,4,5], fall detection [5,6], medical treatment [7], remote rehabilitation and physical therapy [8,9,10,11,12], sports [13,14], education [15], security [16], life logging [17,18], etc . Pedestrian dead reckoning (PDR) or indoor/outdoor localization are important application areas for these sensors.…”

“…Conventional studies of step detection based on accelerometers have used threshold-based [16,26,27,28,29,32,33,34], peak detection [18,21,22,23,24,25,30,35], auto-correlation [19], or spectral analysis [17]. Most step detection methods have assumed that sensors are tied to pre-fixed positions [23,24,25,26,27,28] and have also assumed specific step modes, such as walking, running or static, for the configuration of their algorithms [24,25,26,27,29].…”