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.
The uDirect system @cite_7 @cite_10 employs a similar technique to estimate the user direction; They identify a point in the middle between the user's detected heel strike and toe-off moments as the point where the device orientation is close to the device orientation in the standing mode. These systems, however, require a model for the acceleration pattern within a step for each phone position; A model for the phone placed in the pants pocket was presented in @cite_7 @cite_10 . Deriving the model for a new position is not straightforward and the acceleration pattern for other positions may not be as clear as in the case of the presented pants pocket. Furthermore, the magnetic field noise, which affects the acceleration pattern used in heading estimation, degrades uDirect accuracy indoors as we quantify in .
@cite_10 @cite_7
2114182580 2116274755
A novel method for a mobile phone centric observation of a user’s facing direction is presented. To estimate this direction, our proposed technique exploits the acceleration pattern that can be measured by a smartphone as the user is walking. For an accurate analysis of the acceleration pattern, the proposed approach benefits from a new trigonometric interpolation scheme. Our algorithm is independent of the initial orientation of the device and is adaptable to various wearing positions on a user’s body, which gives the user a larger degree of freedom. A detailed description of the algorithm, which has been customized for a trouser pocket is presented. In addition, complementary hints for adaptation of the algorithm to other wearing positions along with an example of chest pocket position are provided. We have evaluated a prototype implementation of our algorithm on a smartphone, through several field experiments. It has been observed that our algorithm outperforms the conventional GPS and PCA-based techniques in terms of accuracy, reliability and energy consumption. The results also show that our approach has been able to handle the sudden variations of the user’s direction. We have further incorporated our algorithm into a dead-reckoning application as an example of its real-world utility. In this paper we present the uDirect algorithm as a novel approach for mobile phone centric observation of a user's facing direction, through which the device and user orientations relative to earth coordinate are estimated. While the device orientation estimation is based on accelerometer and magnetometer measurements in standing mode, the unique behavior of measured acceleration during stance phase of a human's walking cycle is used for detecting user direction. Furthermore, the algorithm is independent of initial orientation of the device which gives the user higher space of freedom for long term observations. As the algorithm only relies on embedded accelerometer and magnetometer sensors of the mobile phone, it is not susceptible to shadowing effect as GPS. In addition, by performing independent estimations during each step of walking the model is robust to error accumulation. Evaluating the algorithm with 180 data samples from 10 participates has empirically confirmed the assumptions of our analytical model about the unique characteristics of the human stance phase for direction estimation. Moreover, our initial inspection has shown a system based on our algorithm outperforms conventional use of GPS and PCA analysis based techniques for walking distances more than 2 steps.