Verena Nitsch

Position and force augmentation in a telepresence system and their effects on perceived realism

Haptic assistance functions for a telepresence system are presented and assessed. These assistance functions are based on the augmentation of exchanged position and force data, and they are intended to increase the transparency of the telepresence system while maintaining stability. We present the concept and implementation of different assistance functions. Furthermore, we show the setup and results of a psychophysical experiment, which was designed to evaluate the effects of the assistance functions on perceived realism. As a result, the position assistance can increase stability and safety without negatively affecting transparency, and the force assistance can even increase the feeling of presence under certain conditions.

A Meta-Analysis of the Effects of Haptic Interfaces on Task Performance with Teleoperation Systems

Human task performance with teleoperation systems is characterized by long task completion times, handling errors, and excessive force application to objects in the remote environment. Haptic interfaces promise to address these challenges by providing the human user with sensory feedback from the remote environment that would otherwise be lacking. Until now, only few attempts have been made to present current research efforts from a broader, more integrative perspective. To address this need, several meta-analyses were conducted, which aimed at establishing the overall effectiveness of haptic interfaces in improving the critical performance aspects in teleoperation systems. In this context, the influence of potential moderator variables (i.e., virtual versus real teleoperation setup; vibrotactile versus kinaesthetic force feedback) as well as outcome-specific effects (i.e., force regulation ability; task completion time; performance errors) were investigated.

An experimental study of lossy compression in a real telepresence and teleaction system

High network requirements of multiple degrees of freedom haptic data exchange represent a challenge in modern telepresence and teleaction systems. This study presents a systematic evaluation of a psychophysics-based approach to lossy haptic data compression proposed in the literature: the deadband approach and its extension which introduces additional signal model based prediction. In an experimental study, the effects of these approaches on packet rate reduction, perceived interface quality, and relevant task performance criteria were investigated in a three degrees of freedom telepresence and teleaction system. The results indicate that the extended approach did not lead to significant data reduction and adversely affected perceived interface quality as well as task performance. Without prediction, the deadband approach showed excellent rate reduction performance without adversely affecting perceived interface quality and most task performance criteria. We conclude from our study that the combined deadband and prediction approach is not practical for a telepresence and teleaction system with the used control structure, while the deadband compression approach alone exceeded expectations.

Investigating the effects of robot behavior and attitude towards technology on social human-robot interactions

Many envision a future in which personal service robots share our homes and take part in our daily lives. These robots should possess a certain “social intelligence”, so that people are willing, if not eager, to interact with them. In this endeavor, applied psychologists and roboticists have conducted numerous studies to identify the factors that affect social interactions between humans and robots, both positively and negatively. In order to ascertain the extent to which the social human-robot interaction might be influenced by robot behavior and a persons attitude towards technology, an experiment was conducted using the UG paradigm, in which participants (N=48) interacted with a robot, which displayed either animated or apathetic behavior. The results suggest that although the interaction with a robot displaying animated behavior is overall rated more favorably, people may nevertheless act differently towards such robots, depending on their perceived technological competence and their enthusiasm for technology.

Implementation and evaluation of a gesture-based input method in robotic surgery

The introduction of robotic master-slave systems for minimally invasive surgery has created new opportunities in assisting surgeons with partial or fully autonomous functions. While autonomy is an ongoing field of research, the question of how the growing number of offered features can be triggered in a time-saving manner at the master console is not well investigated. We have implemented a gesture-based user interface, whereas the haptic input devices that are commonly used to control the surgical instruments, are used to trigger actions. Intuitive and customizable gestures are learned by the system once, linked to a certain command, and recalled during operation as the gesture is presented by the surgeon. Experimental user studies with 24 participants have been conducted to evaluate the efficiency, accuracy and user experience of this input method compared to a traditional menu. The results have shown the potential of gesture-based input, especially in terms of time savings and enhanced user experience.

On the impact of haptic data reduction and feedback modality on quality and task performance in a telepresence and teleaction system

The perceptual deadband (PD) data reduction scheme allows for a significant reduction of network load in real-time haptic communication scenarios. Aiming to ascertain how the optimum PD data reduction parameter k is best determined, an experiment was conducted in which a possible interaction of PD data reduction and visual feedback on haptic feedback quality and task performance was investigated. The results show that when haptic feedback is complemented with visual feedback, haptic data reduction affects haptic feedback quality at a lower data reduction rate than it affects task performance. When visual feedback is missing, however, this effect is reversed. These results imply that the perception of haptic feedback quality is markedly influenced by visual sensory input as well as task requirements; hence, it is recommended to consider feedback quality and task performance in the optimization of PD reduction before such schemes may be applied to industrial telepresence and teleaction systems.

Effects of Driver Characteristics and Driver State on Predicting Turning Maneuvers in Urban Areas: Is There a Need for Individualized Parametrization?

In future, advanced driver assistance systems (ADAS) may be able to adapt to the needs of the driver, thus reducing the risk of information overload in complex traffic situations. One way of achieving this may include the use of predictive algorithms that anticipate the driver’s intention to perform a certain traffic maneuver based on vehicle data, such as acceleration and deceleration parameters. In order to explore whether the predictive quality of such algorithms may be mitigated by individual driver-specific parameters such as driver characteristics (i.e. emotional driving [ED] and uncritical self-awareness [US]) as well as driver state (specifically stress), an empirical test-track study was conducted with N = 40 participants. The results indicate that maximum longitudinal and lateral acceleration vary significantly depending on driver characteristics. Moreover, analyses of the collected data suggest that incorporating psychological aspects into driver models can promote new insights into driving behavior.

Opening the haptic loop: Network degradation limits for haptic task performance

For realtime teleoperation with haptic feedback, network-induced artifacts like delay, packet loss and lossy coding of haptic data deteriorate the operability of the system. We provide a systematic quantification of the limits within which, these network-induced degradations are tolerable for human task performance in executing a given haptic task. These limits are conservative in the sense that we do not account for any stabilizing approaches from control engineering to counter the impact of the network-induced degradations. Through experimental evaluation with a pursuit tracking task, we show that task performance is most affected by delay on the network and a strong lossy coding scheme. With statistical analysis we show that task performance is significantly decreased for one-way delay higher than 14 ms and strong lossy coding with a deadband parameter higher than 27 %. For the given task, packet loss is found not to affect task performance significantly.

Using multisensory cues for direction information in teleoperation: More is not always better

When full automation of mobile robots is not possible or desirable, teleoperation constitutes an alternative. The human operator can be supported with direction cues to facilitate localization or navigation. These cues are presented typically in the auditory, haptic and/or visual modality. An experiment was conducted to evaluate systematically and empirically the (uni-modal and multi-modal) effects of auditory and haptic feedback compared to visual feedback on target localization accuracy. Results show that haptic as well as auditory direction cues lead to significantly lower accuracy than visual cues. Moreover, combining feedback cues does not necessarily lead to better performance and can even reduce accuracy. Based on the results, possible implications for multi-modal human machine interface design are discussed.

Is Cross-Modal Matching Necessary? A Bayesian Analysis of Individual Reference Cues

The number of research activities on multi-modal feedback cues and their potential to enhance the performance of human operators during teleoperation tasks is growing. Yet, it is still unclear how stimulus intensities of cues in different modalities should be matched when investigating the effects of feedback modality on task performance. Previous work has shown a high within- and between-subject variability of multi-modal intensity adjustments. The source of this variability is not yet clear. In this study, we investigate the individual perception of the cues by setting individual reference intensities for cross-modal matching. In addition to traditional frequentist models, Bayesian models are used for data analysis. The results suggest that haptic as well as auditory cue intensity adjustments are not influenced by modality when combining it with a visual reference. Thus, it seems sufficient to let subjects adjust the intensity for haptic and auditory cues individually prior to investigating the effectiveness of haptic-auditory cues. In contrast, with a 99% probability, visual cues are adjusted to lower intensities by subjects if combined with haptic or auditory cues, which points to the importance of individual cross-modal matching when investigating the effects of visual-haptic or visual-auditory feedback cues.