Headtrack: Tracking head orientation using wireless signals

Estimating and tracking the head orientation of the user is an important problem for numerous mobile computing applications. Current solutions to the problem require deploying infrastructure (namely, cameras and lasers) along with expensive (IMU) sensors. These infrastructure-based approaches bound the user to a limited area of tracking and also disable the portability and mobility of the user. This work presents HeadTrack and explores the feasibility of designing a necklace-like wearable consisting of a headset and a chest-piece that can be used to estimate the user's head orientation using wireless (radio frequency) signals. The core problem presented in this thesis is to accurately estimate multiple distances between the chest-piece on the torso and the headset using the ultra-wide band (UWB) radios. Such a wearable not only enables portability but also mobility by decoupling the user's head motion from the body motion. Although the ultra-wide band radios have a 1GHz bandwidth and high-speed clocks, they are unable to do sub-centimeter ranging. We improve the typical ~10cm accuracy of the UWB radios by introducing a wired path between the transmitters and the receivers to serve as a reference point. We split the signal at the transmitter and route it through the wired as well as the wireless paths to improve the accuracy to about 5mm. We use ViCon to collect the ground truth for our experiments and evaluate our system. HeadTrack uses an IMU to resolve the phase wrap ambiguities and is able to track the head orientation of the user with an accuracy of 6.5 degrees. HeadTrack provides a wearable, occlusion-free, portable, and cost-effective solution to the problem of head orientation tracking with a bounded and non-diverging error.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

EPJ Data Science / Collective aspects of privacy in the Twitter social network

Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service. We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet. Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.(VLID)477385