Kirchner, Elsa Andrea
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Kirchner, Elsa Andrea
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Kirchner, Elsa Andrea
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Kirchner, Elsa A.
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Item-typ:Veröffentlichung, Embedded Brain Reading(2014-03-04); ; ; Current autonomous robots and interfaces are far from exhibiting the adaptability of biological beings regarding changes in their environment or during interaction. They are not always able to provide humans the best and a situation-specific support. Giving the robot or its interface insight into the human mind can open up new possibilities for the integration of human cognitive resources into robots and interfaces, i.e., into their intelligent control systems, and can particularly improve human-machine interaction. In this thesis embedded Brain Reading (eBR) is developed. It empowers a human-machine interface (HMI), which can be a robotic system, to infer the human's intention and hence her/his upcoming interaction behavior based on the context of the interaction and the human's brain state. To enable eBR, an automatic context recognition or generation as well as online, single-trial brain signal decoding, i.e., Brain Reading (BR) for the detection of specific brain states, are required. The human's electroencephalogram (EEG) recorded from the head's surface is used in this work as a measure of brain activity. Experiments are conducted in controlled experimental setups, where subjects have to execute differently complex and demanding simple and dual-task behavior as it is performed during human-machine interaction. Using these experiments the applicability and reliability of BR is confirmed as well as training procedures for BR are improved. Furthermore, a formal model for eBR is developed and shown to be applicable for different implementations of eBR. The formal model is the first step to check implementations of eBR for their correctness and completeness. By means of robotic applications for telemanipulation and rehabilitation it is further shown that eBR can be applied to either adapt or to drive HMIs, i.e., can be used to implement predictive HMIs for passive or active support. In case that eBR is applied for passive support, it is shown that malfunction of the whole system can be avoided. On the other hand, in case that eBR is applied for active support, i.e., to actively drive an HMI, it is shown that an individual adaptation of the support with respect to the requirements of different users can be facilitated by utilizing multi-modal signal analysis in eBR. Finally, it is shown that even in case of passive support eBR can measurably improve human-machine interaction.Dissertation793 221 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Adaptive multimodal biosignal control for exoskeleton supported stroke rehabilitation(IEEE, 2017-11-30); ; ; ; A relevant issue of neuro-interfacing wearable robots in rehabilitation is the necessity to have training data, since the collection of sufficient data from patients within a reasonable recording time is not always possible. However, the use of historic data (e.g., session-to-session transfer, subject-to-subject transfer) can often lead to a reduction in classification performance which is affected by the selection of the historic data (i.e., which historic data was chosen for transfer). In this paper, we analyze two approaches to handle this reduction. First, we used incremental algorithms that can be adapted to the current session when trainable components (the spatial filter and the classifier) are transferred between different sessions. Second, we increased the number of sessions to learn more generalized models. To evaluate the approaches, we used electroencephalographic data that was recorded as training data for demonstrating our neuro-interfacing wearable robot in the application of upper-body sensorimotor rehabilitation. The data was collected from the same healthy subject on 14 different days (14 sessions). Our results showed that the use of a mixture of training sessions improved the classification performance. Further, we could show that the adaptive approaches contributed to less variability in performance that allows the system to be more robust. Hence, one can efficiently use both approaches (i.e., adapting and generalizing the models) depending on how much training data is available. Finally, the analyzed approaches are very promising to increase system applicability in upper-body sensorimotor robotic rehabilitation.Wissenschaftlicher Artikel93 104 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Embedded multimodal interfaces in robotics: applications, future trends, and societal implicationsIn the past, robots were primarily used to perform work that was either too hard, too dangerous or simply too repetitive for humans, e.g., assembly line work, or work that could be done much faster by a robotic system, such as placement work. In the future, human-robot interaction will cover a much broader range of scenarios, from working interactively with humans in the context of industrial manufacturing to robotic appliances designed to care the elderly; even in applied areas, such as autonomous robots in space or operating underwater, the demand for robots to interact or to be intuitively controlled is growing. Hence, interaction will not only involve direct control of a robot or information exchange but will include direct cooperation and physical interaction between human and robot, i.e., humanrobot cooperation. While direct cooperation has tremendous advantages it also presents a number of significant challenges that should not be underestimated. Advanced interfaces to enable human-robot cooperation will be required to meet these challenges and the needs of human-robot interaction in the future.Wissenschaftlicher Artikel192 225