Flow is an affective state of optimal experience, total immersion and high productivity. While often associated with (professional) sports, it is a valuable information in several scenarios ranging from work environments to user experience evaluations, and we expect it to be a potential reward signal for human-in-the-loop reinforcement learning systems. Traditionally, flow has been assessed through questionnaires which prevents its use in online, real-time environments. In this work, we present our findings towards estimating a user's flow state based on physiological signals measured using wearable devices. We conducted a study with participants playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using an end-to-end deep learning architecture, we achieve an accuracy of 67.50% in recognizing high flow vs. low flow states and 49.23% in distinguishing all three affective states boredom, flow, and stress.

In recent years, location-based services and indoor positioning systems gained increasing importance for both, research and industry. Visual localization systems have the advantage of not being dependent on dedicated infrastructure and thus are especially interesting for navigation within buildings. While there are already approaches of using pre-recorded databases of reference images to obtain an absolute position for a given query image, suitable means to estimate the relative movement of pedestrians from an ego perspective video are still missing. This paper presents a novel visual odometry system for pedestrians. The user carries a mobile device while walking - the camera aims into the direction of walking. Using only the video stream as input, the system generates a two-dimensional trajectory, which describes the path traveled by the user. Both, the user's current heading as well as the walking direction are estimated based on the movement of visual feature points in successive video frames. In order to assess the accuracy of the system, it is evaluated in three different scenarios (indoors in an university building, in an urban area and in a city park). Not relying on reference points (for instance provided by a database, which references visual feature points with geo-data), the error accumulates with distance traveled. After a walked distance of 100 meters, the average error lies between 4.6 and 13.9 meters (depending on the scenario). Consequently, the system is a promising approach for visual odometry, which can be used in conjunction with existing absolute visual positioning systems or as a core part of a future SLAM (simultaneous localization and mapping) system.

Since the beginning of the ubiquitous computing era, context-aware applications have been envisioned and pursued, with location and especially proximity information being one of the primary building blocks. To date, there is still a lack of feasible solutions to perform proximity tests between mobile entities in a privacy-preserving manner, i.e., one that does not disclose one's location in case the other party is not in proximity. In this paper, we present our novel approach based on location tags built from surrounding Wi-Fi signals originating only from mobile devices. Since the set of mobile devices at a given location changes over time, this approach ensures the user's privacy when performing proximity tests. To improve the robustness of similarity calculations, we introduce a novel extension of the commonly used cosine similarity measure to allow for weighing its components while preserving the signal strength semantics. Our system is evaluated extensively in various settings, ranging from office scenarios to crowded mass events. The results show that our system allows for robust short-range proximity detection while preserving the participants' privacy.

Due to the increasing popularity of location-based services, the need for reliable and cost-effective indoor positioning methods is rising. As an alternative to radio-based localization methods, in 2011, we introduced MoVIPS (Mobile Visual Indoor Positioning System), which is based on the idea to extract visual feature points from a query image and compare them to those of previously collected geo-referenced images. The general feasibility of positioning by SURF points on a conventional smartphone was already shown in our previous work. However, the system still faced several shortcomings concerning real-world usage such as request times being too high and distance estimation being unreliable because of the employed estimation method not being rotation invariant. In this paper, three extensions are presented that improve the practical applicability of MoVIPS. To speed up request times, both a dead reckoning approach (based on step counting using the accelerometer) and an orientation estimation (based on the smartphones compass) are introduced to filter relevant images from the database and thus to reduce the amount of images to compare the query image to. Furthermore, the vectors of the SURF points are quantized. For this purpose, clusters are calculated from all SURF points from the database. As a result, each image can be represented by a histogram of cluster frequencies, which can be compared with each other a lot more efficiently. The third extension is an improvement of the distance estimation method, which uses the matched feature points of an image to perform a perspective transformation and to determine the actual position with the aid of the transformation matrix.

Many existing approaches for WIFI-based indoor positioning systems use the received signal strength indicator (RSSI) of all the access points in reach to estimate the current location of a mobile device. Those systems' accuracy, however, is strongly influenced by external interferences and suffers both from short-term and long-term changes in the respective environments. Time-of-Flight-based (TOF) approaches bypass these problems by relying on the relation between distance and the time it takes a radio signal to travel that distance. Furthermore and in contrast to RSSI-based fingerprinting methods, they require neither a time-consuming calibration phase nor an extensive database. In this paper, we assess the advantages and limitations of TOF-based positioning techniques and compare different approaches in literature in terms of communication flow, hardware components, method of time measurement and positioning accuracy. Additionally, we present a novel approach using NULL-ACK-sequences, off-the-shelf hardware components and the CPU's time stamp counter offering a nanosecond resolution. An association with access points is hence not required and there is no need for modifications of client or infrastructure WIFI-components. We evaluate our system in various settings. Our results indicate that the accuracy of such TOF-based approaches depends on both the used hardware and the characteristics of the given environment. We find that time fluctuations caused by varying delays of the interrupt service routine as well as multipath effects render precise distance estimations based on a single measurement infeasible. In order to obtain stable ranging results we try to minimize these effects by utilizing a high amount of NULL-ACK-sequences. We investigate several filters and statistical estimations and compare them within our system settings. Using a band-pass filter and taking the average of a series of measurements, we are able to achieve a ranging accuracy with a mean absolute error of less than 1.33 meters in an ideal environment.

Decentralized Online Social Networks (OSNs) attempt to improve user privacy and security. One example is Vegas, a Peer-to-Peer (P2P) OSN which attempts to bring its users back into complete control of the data they share. Due to its decentralized characteristics, P2P OSNs cannot support social search functions in the same way users of centralized OSNs like Facebook are familiar with. Well-known and efficient P2P search algorithms cannot always be applied as knowledge about the structure of the social graph can be very limited. In this paper, we present an in-depth analysis of forwarding strategies to enable social search for secure and privacy preserving P2P OSNs. We compare well-known metrics from the field of unstructured P2P networks with metrics from the area of social network analysis and evaluate their applicability for P2P OSNs like Vegas. We simulate all metrics on four distinct datasets which were generated artificially from the ER- and the BA-model and from crawling data of Lastfm and Flickr. Our evaluation shows that prioritization based on knowledge from the ego network often yields the best results.