Electrocardiograms constitute the key diagnostic tool for cardiologists. While their diagnostic value is yet unparalleled, electrode placement is prone to errors, and sticky electrodes pose a risk for skin irritations and may detach in long-term measurements. Heart.AI presents a fundamentally new approach, transforming motion-based seismocardiograms into electrocardiograms interpretable by cardiologists. Measurements are conducted simply by placing a sensor on the user's chest. To generate the transformation model, we trained a convolutional autoencoder with the publicly available CEBS dataset. The transformed ECG strongly correlates with the ground truth (r=.94, p0.12). On a 5-point Likert scale, 15 cardiologists rated the morphological and rhythmological validity as high (4.63/5 and 4.8/5, respectively). Our electrodeless approach solves crucial problems of ECG measurements while being scalable, accessible and inexpensive. It contributes to telemedicine, especially in low-income and rural regions worldwide.

Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA frameworks rely on a monolithic architecture with features for the integration of specialized analysis methods, we consider a loose coupling of independent applications, where autonomous VA tools are used in predefined analysis sequences. To this end, we provide a characterization of the data exchange process among individual VA tools in the form of a taxonomy. This taxonomy can be used as a checklist to identify characteristics and improve the data flow of one's own multi-tool VA setup. For this purpose, we conducted a systematic investigation of the individual aspects of data exchange that are commonly found across different usage scenarios. We apply our taxonomy to three existing multi-tool frameworks, the open-source library ReVize, the toolchain editor AnyProc, and the visualization and monitoring framework Plant@Hand3D.

The digitization within the healthcare sector is an extensive topic for multiple ICT technologies, such as Cloud Computing, Internet of Things (IoT), and Artificial Intelligence. Over the past years, there has been an increasing interest in the storage, evaluation and visual representation of medical data. This led to a substantial amount of independent eHealth technologies, that are primarily concerned with information gathering and management. However, the multitude of existing tools makes it is difficult for the user to understand global relationships between different management systems. Therefore, we present Health@Hand an IoT system for monitoring vital and administrative data in the digital twin of an intensive care unit (ICU). The main goal of our system is to summarize and process real-time data in order to assist medical experts such as physician, head-nurses or controllers in their daily tasks. Health@Hand achieves this by providing a visual interface for the management and analysis of different medical data sources.

EarFieldSensing (EarFS) is a novel input method for mobile and wearable computing using facial expressions. Facial muscle movements induce both electric field changes and physical deformations, which are detectable with electrodes placed inside the ear canal. The chosen ear-plug form factor is rather unobtrusive and allows for facial gesture recognition while utilizing the close proximity to the face. We collected 25 facial-related gestures and used them to compare the performance levels of several electric sensing technologies (EMG, CS, EFS, EarFS) with varying electrode setups. Our developed wearable fine-tuned electric field sensing employs differential amplification to effectively cancel out environmental noise while still being sensitive towards small facial-movement-related electric field changes and artifacts from ear canal deformations. By comparing a mobile with a stationary scenario, we found that EarFS continues to perform better in a mobile scenario. Quantitative results show EarFS to be capable of detecting a set of 5 facial gestures with a precision of 90% while sitting and 85.2% while walking. We provide detailed instructions to enable replication of our low-cost sensing device. Applying it to different positions of our body will also allow to sense a variety of other gestures and activities.

Close Human-Robot Interaction (HRI) has been a great focus of research for the last decades. The outcomes of this focus is a new field in industrial robotics called collaborative robotics. A collaborative robot (cobot) is usually an industrial robot designed to operate safely in a shared work environment with the human worker. This in contrast to conventional Industrial Robots (IRs) which are operating in isolation from the worker workspace, the cobot is changing the concept of automation from fully automated operations to semiautonomous operations, where the decisions of the worker will influence the actions of the cobot and viceversa. Therefore, a communication and information control framework must exist to connect the worker and the cobot together to fulfil this semi-autonomous paradigm. This framework should be able to provide a method to represent the common knowledge which can support the collaborative manufacturing between the worker and the cobot. During this research we are proposing an ontology-based Holonic Control Architecture (HCA) as a proper solution to share and communicate the knowledge needed to achieve complex interaction scenarios between the worker and the cobot.

Involving an industrial robot in a close physical interaction with the worker became quite possible, as a result of the availability of different collaborative industrial robots in the market. The physical cooperation between the industrial robot and the worker usually done under the umbrella of the flexible manufacturing paradigm, where both the industrial robot and the worker need to change their tasks fast and efficiently, to cope with the changes in the manufacturing process. This means that a reliable manufacturing control system must stand behind this physical interaction to achieve the proper communication interaction. A holonic control architecture is an ideal solution for this problem. Therefore, during this research we study the most commonly applied model of the holonic control architecture, then we apply this architecture on our case study, where one worker cooperates with a dual-arm industrial robot to build and produce any new product. Also the research uses the worker's hand gesture recognition as a method to interact with the industrial robot during the execution of a cooperative production scenario.

Foot interfaces, such as pressure-sensitive insoles, still yield unused potential such as for implicit interaction. In this paper, we introduce CapSoles, enabling smart insoles to implicitly identify who is walking on what kind of floor. Our insole prototype relies on capacitive sensing and is able to sense plantar pressure distribution underneath the foot, plus a capacitive ground coupling effect. By using machine-learning algorithms, we evaluated the identification of 13 users, while walking, with a confidence of ∼95% after a recognition delay of ∼1s. Once the user's gait is known, again we can discover irregularities in gait plus a varying ground coupling. While both effects in combination are usually unique for several ground surfaces, we demonstrate to distinguish six kinds of floors, which are sand, lawn, paving stone, carpet, linoleum, and tartan with an average accuracy of ∼82%. Moreover, we demonstrate the unique effects of wet and electrostatically charged surfaces.

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We present Botential, an on-body interaction method for a wearable input device that can identify the location of on-body tapping gestures, using the entire human body as an interactive surface to expand the usually limited interaction space in the context of mobility. When the sensor is being touched, Botential identifies a body part's unique electric signature, which depends on its physiological and anatomical compositions. This input method exhibits a number of advantages over previous approaches, which include: 1) utilizing the existing signal the human body already emits, to accomplish input with various body parts, 2) the ability to also sense soft and long touches, 3) an increased sensing range that covers the whole body, and 4) the ability to detect taps and hovering through clothes.

This issue presents selected papers from the workshop 'Computer Graphics Technology for the Exploration of the Sea' (CES '95).The workshop was organized by the Fraunhofer Institute for Computer Graphics as an event of IFIP WG 5.10 and in co-operation with the German Computer Society (GI), Interest Groups on Imaging and Visualization (GI 4.1.2) and on Informatics for Environmental Protection (GI 4.6).It took place in Rostock in May 1995.The intention of the workshop was to promote the exchange of research results and practical experiences in the use of computer graphics technology for the exploration of the sea.The main focus of the workshop was the interdisciplinary work between computer science and marine ecology for support of such things as the analysis and interpretation of different kinds of marine data.The two-day workshop with 57 participants from USA, Great Britain, Italy, Netherlands, France and Germany was a successful interdisciplinary platform stimulating the exchange of experiences and the cooperation between Computer Science and Marine Research.14 papers and 6 posters have been presented

Abstract Existing standards for three‐dimensional graphic representations are unable to produce any realistically shaded images (except PHIGS PLUS, which provides discrete shading only). Ray Tracing or Radiosity are not taken into account up to this time. This article is intended to show one way to integrate the Ray Tracing technique into the pipeline of the graphics standards GKS‐3D and PHIGS. No modifications of the viewing concepts are required by our implementation.