Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smart-phones. 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 prior-configuration 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 @math averaged over a wide variety of phone positions. This is @math 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.
In @cite_8 , authors used inertial sensors, a wearable camera, and an inertial head tracker. The forward direction is determined by testing whether the slope of vertical acceleration at the peak for forward acceleration is increasing. This algorithm requires sensors to be attached to the torso for correct detection of the acceleration patterns, limiting its applicability for different positions. Also, using a wearable camera imposes further limitations on the applicability of the technique and the environment (e.g. lightning).
@cite_8
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In this paper, we present a wearable augmented reality (AR) system with personal positioning based on walking locomotion analysis that allows a user to freely mover around indoors and outdoors. The user is equipped with self-contained sensors, a wearable camera, an inertial head tracker and display. The system is based on the sensor fusion of estimates for relative displacement caused by human walking locomotion and estimates for absolute position and orientation within a Kalman filtering framework. The former is based on intensive analysis of human walking behavior using self-contained sensors. The latter is based on image matching of video frames from a wearable camera with an image database that was prepared beforehand.