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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.
Gestures are part of the interaction between humans and are currently getting more and more popular in the field of Human-Machine Interaction (HMI). First systems with mid-air gesture control are available in the automotive field of application. But it is still an open question which gestures are intuitive for the users, standards do not exist. In this paper we present a 2-step user study on expectations on touchless gestures in vehicles as part of a participatory design process.
Mission- and safety-critical domains are more and more characterized by interactive and multimedia systems varying from large-scale technologies (e. g. airplanes) to wearable devices (e. g. smartglasses) operated by professional staff or volunteering laypeople. While technical availability, reliability and security of computer-based systems are of utmost importance, outcomes and performances increasingly depend on sufficient human-machine interaction or even cooperation to a large extent. While this i-com Special Issue on “Human-Machine Interaction and Cooperation in Safety-Critical Systems” presents recent research results from specific application domains like aviation, automotive, crisis management and healthcare, this introductory paper outlines the diversity of users, technologies and interaction or cooperation models involved.
Technical Report
(2016)
This internal report discusses the theoretical and practical aspects of the cluster management framework SimpleHydra, which was developed in order to allow researchers the quick setup of classical small to mid-scale computation clusters while being as lightweight and platform independent as possible. We motivate crucial design choices with a theoretical analysis in the aspect of time and space complexity, furthermore we give a comprehensive introduction regarding the frameworks usage (which includes examples and detailed description of fundamental concepts as well as data structures). In addition to that we illustrate application scenarios with complete source code examples. Furthermore we hope that this document proves valuable not only as a development report but also as a practical manual for SimpleHydra.
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.
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.
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 %.
"Quarter agile" aims to promote older people's social participation and community
via physical and cognitive training which the participants also help create. The project relies heavily on the use of smartphones as training support. Loneliness
and loss of physical and cognitive skills are to be prevented by means of training
and participation in groups. We want to investigate the effects of technology-
assisted training on physical and cognitive performance and social participation of
older people. "Quarter agile" is geared towards healthy people ages 65 and up who are residents of the specified neighborhood.
Massive open online courses (MOOCs) become more and more popular. These course formats are typically highly flexible and attract large groups of learners from heterogeneous backgrounds. So far research in this area concentrating on success factors for low dropout rates and high satisfaction on the side of the learners in MOOCs is scarce. In this chapter, we describe experiences of a large online course offered to students of two large German universities. Based on theory drawn from a social psychological perspective on the relevance of social interaction for learning, we describe the background, structure, and specific elements of the MOOC-like course. We outline evaluation results of both small group collaboration (in workshops) and mass interaction (via forum and wiki usage) as well as results of the general evaluation of the overall course concept. We argue that the specific mixture of small and large group interaction as well as teacher- and learner-generated content is especially promising with regard to satisfaction, learning outcomes, and course completion rates.