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Touch versus mid-air gesture interfaces in road scenarios-measuring driver performance degradation
(2016)
We present a study aimed at comparing the degradation of the driver's performance during touch gesture vs mid-air gesture use for infotainment system control. To this end, 17 participants were asked to perform the Lane Change Test. This requires each participant to steer a vehicle in a simulated driving environment while interacting with an infotainment system via touch and mid-air gestures. The decrease in performance is measured as the deviation from an optimal baseline. This study concludes comparable deviations from the baseline for the secondary task of infotainment interaction for both interaction variants. This is significant as all participants are experienced in touch interaction, however have had no experience at all with mid-air gesture interaction, favoring mid-air gestures for the long-term scenario.
Die spezifischen Herausforderungen des Fachgebiets bedürfen jedoch auch weiterhin einer Diskussion und der Entwicklung neuer Methoden und Ansätze zur Gestaltung von Informationssystemen. Diese sollen dieses Jahr adressiert werden. Generell fokussieren wir eher auf die Effekte von Technologien auf realweltliche Praktiken, als auf die isolierte Technologie. Auch der auf diesen Beiträgen basierende Workshop legt aktuelle Entwicklungen und Fragestellungen offen und gibt neue Impulse für das Forschungsgebiet. Der Workshop wird dabei zweigeteilt gestaltet: Innerhalb des ersten Teils wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten zu präsentieren. Dabei sind sowohl designorientierte, praxisbasierte Analysen und Studien, als auch entwickelte und evaluierte Prototypen neuer Technologien von Interesse. Es wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten teilweise in einem eher frühen Stadium in kompakter Form zu präsentieren und anschließend in Hinblick auf deren Weiterentwicklung diskutieren.
We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.
Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 %.
In catastrophic events, the potential of help has grown through new technologies. Voluntary help has many forms. Within this paper different categories of voluntary help are suggested. Those categories are based on properties like organizational structures, helping process, kind of prosocial behavior and many more. A focus is clearly on the organizational structure and motivational aspects of helper groups. Examples are given for each category. The categorization’s aim is to give a brief overview of possible properties a group of system users could have.
We present a light-weight real-time applicable 3D-gesture recognition system on mobile devices for improved Human-Machine Interaction. We utilize time-of-flight data coming from a single sensor and implement the whole gesture recognition pipeline on two different devices outlining the potential of integrating these sensors onto mobile devices. The main components are responsible for cropping the data to the essentials, calculation of meaningful features, training and classifying via neural networks and realizing a GUI on the device. With our system we achieve recognition rates of up to 98% on a 10-gesture set with frame rates reaching 20Hz, more than sufficient for any real-time applications.
This contribution presents a novel approach of utilizing Time-of-Flight (ToF) technology for mid-air hand gesture recognition on mobile devices. ToF sensors are capable of providing depth data at high frame rates independent of illumination making any kind of application possible for in- and outdoor situations. This comes at the cost of precision regarding depth measurements and comparatively low lateral resolution. We present a novel feature generation technique based on a rasterization of the point clouds which
realizes fixed-sized input making Deep Learning approaches applicable using Convolutional Neural Networks. In order to increase precision we introduce several methods to reduce noise and normalize the input to overcome difficulties in scaling. Backed by a large-scale database of about half
a million data samples taken from different individuals our
contribution shows how hand gesture recognition is realiz-
able on commodity tablets in real-time at frame rates of up to 17Hz. A leave-one out cross-validation experiment
demonstrates the feasibility of our approach with classification errors as low as 1,5% achieved persons unknown to the model.
Open Educational Resources (OER) intend to support access to education for everyone. However, this potential is not fully exploited due to various barriers in the production, distribution and the use of OER. In this paper, we present requirements and recommendations for systems for global OER authoring. These requirements as well as the system itself aim at helping creators of OER to overcome typical obstacles such as lack of technical skills, different types of devices and systems as well as the cultural differences in cross-border-collaboration. The system can be used collaboratively to create OER and supports multi-languages for localization. Our paper contributes to facilitate global, collaborative e-Learning and design of authoring platforms by identifying key requirements for OER authoring in a global context.
The paper provides a contextualization process to adapt Open Knowledge Resources for the need of public administrations. By help of a matching strategy, culture and context profiles of learners and learning resources are compared. The comparison allows to draw inferences how to contextualize an open knowledge resource for own learning needs. An example is illustrated and future research fields are proposed.