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

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 $15^\circ$ 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.Comment: Accepted for publication in the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2014

RIDI: Robust IMU Double Integration

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research

Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths

This is a no brainer. Using bicycles to commute is the most sustainable form of transport, is the least expensive to use and are pollution-free. Towns and cities have to be made bicycle-friendly to encourage their wide usage. Therefore, cycling paths should be more convenient, comfortable, and safe to ride. This paper investigates a smartphone application, which passively monitors the road conditions during cyclists ride. To overcome the problems of monitoring roads, we present novel algorithms that sense the rough cycling paths and locate road bumps. Each event is detected in real time to improve the user friendliness of the application. Cyclists may keep their smartphones at any random orientation and placement. Moreover, different smartphones sense the same incident dissimilarly and hence report discrepant sensor values. We further address the aforementioned difficulties that limit such crowd-sourcing application. We evaluate our sensing application on cycling paths in Singapore, and show that it can successfully detect such bad road conditions.Comment: 6 pages, 5 figures, Accepted by IEEE 4th World Forum on Internet of Things (WF-IoT) 201

Microelectromechanical system gravimeters as a new tool for gravity imaging

A microelectromechanical system (MEMS) gravimeter has been manufactured with a sensitivity of 40 ppb in an integration time of 1 s. This sensor has been used to measure the Earth tides: the elastic deformation of the globe due to tidal forces. No such measurement has been demonstrated before now with a MEMS gravimeter. Since this measurement, the gravimeter has been miniaturized and tested in the field. Measurements of the free-air and Bouguer effects have been demonstrated by monitoring the change in gravitational acceleration measured while going up and down a lift shaft of 20.7 m, and up and down a local hill of 275 m. These tests demonstrate that the device has the potential to be a useful field-portable instrument. The development of an even smaller device is underway, with a total package size similar to that of a smartphone

Field tests of a portable MEMS gravimeter

Gravimeters are used to measure density anomalies under the ground. They are applied in many different fields from volcanology to oil and gas exploration, but present commercial systems are costly and massive. A new type of gravity sensor has been developed that utilises the same fabrication methods as those used to make mobile phone accelerometers. In this study, we describe the first results of a field-portable microelectromechanical system (MEMS) gravimeter. The stability of the gravimeter is demonstrated through undertaking a multi-day measurement with a standard deviation of 5.58 × 10−6 ms−2 . It is then demonstrated that a change in gravitational acceleration of 4.5 × 10−5 ms−2 can be measured as the device is moved between the top and the bottom of a 20.7 m lift shaft with a signal-to-noise ratio (SNR) of 14.25. Finally, the device is demonstrated to be stable in a more harsh environment: a 4.5 × 10−4 ms−2 gravity variation is measured between the top and bottom of a 275-m hill with an SNR of 15.88. These initial field-tests are an important step towards a chip-sized gravity senso