It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications

Abstract

Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smartphones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications.In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no priorconfiguration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of 15 • averaged over a wide variety of phone positions. This is 558% better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.

Secure key generation using gait features for Body Sensor Networks

“…The quaternion vector q can be calculated from raw gyroscope data as followings: [18] accuracy of the gait event detection can be improved significantly, even though, the proposed key generation scheme can work without projection, as it mainly relies on the timing information.…”

TransitLabel

“…This module is responsible for preprocessing the raw sensor measurements to reduce the effect of (a) phone orientation changes and (b) noise, e.g., small direction changes while moving. To handle the former, we transform the sensor readings from the mobile coordinate system to the world coordinate system leveraging the inertial sensors [32]. To address the latter, we apply a low-pass filter to raw sensors data using local weighted regression to smooth the data [10].…”

LaneQuest: An accurate and energy-efficient lane detection system

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