Implicit Interaction in Multimodal Human-Machine Systems

Rötting, Matthias; Zander, Thorsten O.; Trösterer, Sandra; Dzaack, Jeronimo

doi:10.1007/978-3-642-01293-8_39

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…Brain activity may provide an additional seamless communication channel between the surgeon and robot, and thus improve the robot's understanding of the cognitive challenges faced by the operator. This secondary, implicit interaction loop would provide valuable information to the robot, without demanding extra cognitive load from the operator [22]. Technology that adapts to the users in this way based on their brain activity can be termed neuroadaptive technology, and the required form of communication between the brain and the machine can be implemented using passive brain-computer interface (BCI) [23].…”

Section: Introductionmentioning

confidence: 99%

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

Zander

Shetty

Lorenz

et al. 2017

J. Med. Robot. Res.

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Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in a complex maneuver and should not be disturbed. Passive brain-computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment.

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Section: Introductionmentioning

confidence: 99%

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

Zander

Shetty

Lorenz

et al. 2017

J. Med. Robot. Res.

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“…The selected pupil dilation time bins (750-1750 ms) correspond with the peak time of target-related pupil dilation in visual oddball tasks [20]. Dwell time is a strong correlate of interest that has been used successfully in gazecontrolled HCIs [28,16].…”

Section: Discussionmentioning

confidence: 99%

Feature selection for gaze, pupillary, and EEG signals evoked in a 3D environment

Jangraw

Sajda

2013

Proceedings of the 6th Workshop on Eye Gaze in Intelligent Human Machine Interaction: Gaze in Multimodal Interaction

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As we navigate our environment, we are constantly assessing the objects we encounter and deciding on their subjective interest to us. In this study, we investigate the neural and ocular correlates of this assessment as a step towards their potential use in a mobile human-computer interface (HCI). Past research has shown that multiple physiological signals are evoked by objects of interest during visual search in the laboratory, including gaze, pupil dilation, and neural activity; these have been exploited for use in various HCIs. We use a virtual environment to explore which of these signals are also evoked during exploration of a dynamic, freeviewing 3D environment. Using a hierarchical classifier and sequential forward floating selection (SFFS), we identify a small, robust set of features across multiple modalities that can be used to distinguish targets from distractors in the virtual environment. The identification of these features may serve as an important factor in the design of mobile HCIs.

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“…Implicit interaction helps humans to understand situations of different human beings in an efficient and effective way. Unlike human-to-human communication, the traditional human computer system lacks the ability to detect implicit information as humans normally do in face-to-face interaction [30,31]. Consequently, conventional interactions between humans and computers are constrained by the latter's computing power and number of embedded sensors in harvesting and harnessing contextual information.…”

Section: Implicit and Explicit Interactionmentioning

confidence: 99%