3DTouch: A wearable 3D input device with an optical sensor and a 9-DOF inertial measurement unit

We present 3DTouch, a novel 3D wearable input device worn on the fingertip for 3D manipulation tasks. 3DTouch is designed to fill the missing gap of a 3D input device that is self-contained, mobile, and universally working across various 3D platforms. This paper presents a low-cost solution to designing and implementing such a device. Our approach relies on relative positioning technique using an optical laser sensor and a 9-DOF inertial measurement unit. 3DTouch is self-contained, and designed to universally work on various 3D platforms. The device employs touch input for the benefits of passive haptic feedback, and movement stability. On the other hand, with touch interaction, 3DTouch is conceptually less fatiguing to use over many hours than 3D spatial input devices. We propose a set of 3D interaction techniques including selection, translation, and rotation using 3DTouch. An evaluation also demonstrates the device's tracking accuracy of 1.10 mm and 2.33 degrees for subtle touch interaction in 3D space. Modular solutions like 3DTouch opens up a whole new design space for interaction techniques to further develop on.Comment: 8 pages, 7 figure

WatchMI: pressure touch, twist and pan gesture input on unmodified smartwatches

The screen size of a smartwatch provides limited space to enable expressive multi-touch input, resulting in a markedly difficult and limited experience. We present WatchMI: Watch Movement Input that enhances touch interaction on a smartwatch to support continuous pressure touch, twist, pan gestures and their combinations. Our novel approach relies on software that analyzes, in real-time, the data from a built-in Inertial Measurement Unit (IMU) in order to determine with great accuracy and different levels of granularity the actions performed by the user, without requiring additional hardware or modification of the watch. We report the results of an evaluation with the system, and demonstrate that the three proposed input interfaces are accurate, noise-resistant, easy to use and can be deployed on a variety of smartwatches. We then showcase the potential of this work with seven different applications including, map navigation, an alarm clock, a music player, pan gesture recognition, text entry, file explorer and controlling remote devices or a game character.Postprin

Digital fabrication of custom interactive objects with rich materials

As ubiquitous computing is becoming reality, people interact with an increasing number of computer interfaces embedded in physical objects. Today, interaction with those objects largely relies on integrated touchscreens. In contrast, humans are capable of rich interaction with physical objects and their materials through sensory feedback and dexterous manipulation skills. However, developing physical user interfaces that offer versatile interaction and leverage these capabilities is challenging. It requires novel technologies for prototyping interfaces with custom interactivity that support rich materials of everyday objects. Moreover, such technologies need to be accessible to empower a wide audience of researchers, makers, and users. This thesis investigates digital fabrication as a key technology to address these challenges. It contributes four novel design and fabrication approaches for interactive objects with rich materials. The contributions enable easy, accessible, and versatile design and fabrication of interactive objects with custom stretchability, input and output on complex geometries and diverse materials, tactile output on 3D-object geometries, and capabilities of changing their shape and material properties. Together, the contributions of this thesis advance the fields of digital fabrication, rapid prototyping, and ubiquitous computing towards the bigger goal of exploring interactive objects with rich materials as a new generation of physical interfaces.Computer werden zunehmend in Geräten integriert, mit welchen Menschen im Alltag interagieren. Heutzutage basiert diese Interaktion weitgehend auf Touchscreens. Im Kontrast dazu steht die reichhaltige Interaktion mit physischen Objekten und Materialien durch sensorisches Feedback und geschickte Manipulation. Interfaces zu entwerfen, die diese Fähigkeiten nutzen, ist allerdings problematisch. Hierfür sind Technologien zum Prototyping neuer Interfaces mit benutzerdefinierter Interaktivität und Kompatibilität mit vielfältigen Materialien erforderlich. Zudem sollten solche Technologien zugänglich sein, um ein breites Publikum zu erreichen. Diese Dissertation erforscht die digitale Fabrikation als Schlüsseltechnologie, um diese Probleme zu adressieren. Sie trägt vier neue Design- und Fabrikationsansätze für das Prototyping interaktiver Objekte mit reichhaltigen Materialien bei. Diese ermöglichen einfaches, zugängliches und vielseitiges Design und Fabrikation von interaktiven Objekten mit individueller Dehnbarkeit, Ein- und Ausgabe auf komplexen Geometrien und vielfältigen Materialien, taktiler Ausgabe auf 3D-Objektgeometrien und der Fähigkeit ihre Form und Materialeigenschaften zu ändern. Insgesamt trägt diese Dissertation zum Fortschritt der Bereiche der digitalen Fabrikation, des Rapid Prototyping und des Ubiquitous Computing in Richtung des größeren Ziels, der Exploration interaktiver Objekte mit reichhaltigen Materialien als eine neue Generation von physischen Interfaces, bei

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

An Evaluation of Touch and Pressure-Based Scrolling and Haptic Feedback for In-car Touchscreens

An in-car study was conducted to examine different input techniques for list-based scrolling tasks and the effectiveness of haptic feedback for in-car touchscreens. The use of physical switchgear on centre consoles is decreasing which allows designers to develop new ways to interact with in-car applications. However, these new methods need to be evaluated to ensure they are usable. Therefore, three input techniques were tested: direct scrolling, pressure-based scrolling and scrolling using onscreen buttons on a touchscreen. The results showed that direct scrolling was less accurate than using onscreen buttons and pressure input, but took almost half the time when compared to the onscreen buttons and was almost three times quicker than pressure input. Vibrotactile feedback did not improve input performance but was preferred by the users. Understanding the speed vs. accuracy trade-off between these input techniques will allow better decisions when designing safer in-car interfaces for scrolling applications

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations