The concept of presence, or “being there” is a frequently emphasized factor in immersive mediated environments. It is often assumed that greater levels of immersive quality elicit higher levels of presence, in turn enhancing the effectiveness of a mediated experience. To investigate this assumption the current meta-analysis synthesizes decades of empirical research examining the effect of immersive system technology on user experiences of presence. Aggregating 115 effect sizes from 83 studies, it finds that technological immersion has a medium-sized effect on presence. Additionally, results show that increased levels of user-tracking, the use of stereoscopic visuals, and wider fields of view of visual displays are significantly more impactful than improvements to most other immersive system features, including quality of visual and auditory content. These findings are discussed in light of theoretical accounts of the presence construct as well as practical implications for design.

How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence

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Enhancing Our Lives with Immersive Virtual Reality

Supersizing the Mind: Embodiment, Action and Cognitive Extension.

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Feature Extraction and Selection for Emotion Recognition from EEG

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Emotions Recognition Using EEG Signals: A Survey

A Survey of Environment-, Operator-, and Task-adapted Controllers for Teleoperation Systems

A robot controller is developed for human-robot handshaking. The focus of the work is to provide realistic experiences for the human participant in haptic interactions with a robot. To achieve this goal, a position-based admittance controller is implemented. By using haptic data as inputs, a Hidden Markov Model-based high-level controller is used to estimate human intentions and modify the reference trajectory accordingly. The overall control framework is implemented onto a robot with validation experiments carried out with human participants.

An HMM approach to realistic haptic human-robot interaction

The concept of a new mobile haptic interface for bimanual manipulations in 6 DOFs is presented. The design of this mobile haptic interface is based on a modular system consisting of two components: two admittance-type haptic interfaces and a mobile platform. While the haptic interfaces cover only parts of the human arm workspace, the mobile platform extends these to arbitrarily wide remote environments. This paper mainly addresses the design and control concepts of the haptic interfaces, which are planned to be mounted on the mobile platform. The interfaces dispose of a large workspace and a high force/torque capability. A special design and control concept of the haptic interfaces makes it possible to decouple translational from rotational movements. This decoupling helps to significantly simplify the control algorithms that handle the interaction between the single components. Evaluation results concerning the Cartesian position tracking performance and the impedance display fidelity are provided. In addition, the following performance measures are analyzed: dextrous workspace, output capability, and backdrivability. In order to reduce the measurement effort, some of these measures are determined by model-based, others by measurement-based performance evaluation.

A New Admittance-Type Haptic Interface for Bimanual Manipulations

Recent research focuses on developing robots that are meant to be partners of humans instead of pure machines. This makes enhanced communication necessary. Especially in scenarios embedding physical interaction between the two partners dominance is an urgent matter. To overcome one-sided dominance as in passive following or trajectory replay in favor of intuitive collaboration, human-human collaboration and the involved dominance distribution needs to be addressed. Even though some attempts are reported in literature, to our best knowledge no experimental analysis of dominance distribution in a kinesthetic task reports actual values of dominance. Therefore, the current paper discusses dominance measures appropriate in haptic interaction and investigates the dominance distribution in a tracking-task experiment. In the analysis we focus on the influence of mutual haptic feedback between the partners on dominance distribution by contrasting this condition to vision-only partner feedback trials. Furthermore, this paper investigates the consistency of dominance behavior across different partners based on methodologies transferred from social psychology. Results show that participants work with a dominance distribution, whereby the feedback condition does not effect this distribution. A high amount of variability in individual dominance behavior can be considered person dependent. Here, feedback has an effect as the dominance behavior is even more stable across partners when mutual haptic feedback is provided.

Angelika Peer

Papers

Feature Extraction and Selection for Emotion Recognition from EEG

A Survey of Environment-, Operator-, and Task-adapted Controllers for Teleoperation Systems

An HMM approach to realistic haptic human-robot interaction

A New Admittance-Type Haptic Interface for Bimanual Manipulations

Experimental analysis of dominance in haptic collaboration