Vital signs such as heart rate, oxygen saturation, and blood pressure are crucial information for healthcare workers to identify clinical deterioration of ward patients. Currently, medical devices monitor these vital signs and trigger alarms when the vital signs are not in the normal ranges based on predefined thresholds, which suggests the presence of clinical deterioration. However, such threshold-based approach is not robust for patient monitoring. This is because vital signs differ among patients due to human physiology and change across time based on the action performed by a patient. In this work, we want to tackle these problems by building adaptable action-aware vital models. These models can understand the changes in vital signs caused by patient's actions and can be adapted to the normal vital sign ranges of individual patients. Our experimental results show that general vital sign patterns for different actions exist and can be personalized to new patients. Additionally, we investigate the possibility of estimating the initial vital model for an unobserved action using models of observed actions for model personalization. The resulting adaptable action-aware vital models have the potential to improve patient monitoring by reducing false clinical alarms.

This paper describes an adaptive fuzzy control algorithm that may be used for the automation of medical devices. The automation system consists of a set of controllers containing the expertise of the physician and a main fuzzy controller that creates new rules based on this knowledge plus additional information about the patient. An adaptive mechanism evaluates the performance of the controller by using reference models and modifies its rules when needed. As a test case the automation of an extra-corporeal support system is presented, where a centrifugal pump speed is adjusted based on desired pressures and flow values.

Telesurgery systems integrate multimodal communication and robotic technologies to enable surgical procedures to be performed from remote locations. They allow human surgeons to intuitively control laparoscopic instruments and to navigate within the human body. In this paper, we present selected topics on multimodal interaction in the context of telesurgery applications. These are results from the collaborative research project SFB 453 on ldquoHigh-Fidelity Telepresence and Teleactionrdquo which is funded by the German Research Foundation in the larger Munich area. The focus in this paper is on multimodal information processing and communication including simulation of surgical targets in the human body. Furthermore, we present an overview of our advanced multimodal telesurgery demonstrators that provide a comprehensive platform for our collaborative telepresence research.

The term scaffolding, with respect to human education, was first coined in the 1970ies, although the basic concept originates back to the 1930ies. The main idea is to formalize the superior knowledge of a teacher in a certain way to generate support for a trainee. In practice, this concept can be implemented as concrete as a cloze, which assists pupils in learning a foreign language, or it might be as abstract as a social environment, which facilitates learning of specific tasks. This paper introduces a novel approach towards robotic learning by means of such a scaffolding framework. In this case, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during "Learning by Demonstration". The methodology was applied to a real-world scenario of robot-assisted surgery.

A popular method for an easy and also flexible programming of robots is learning by demonstration. An intelligent controller learns a task from several examples carried out by an experienced user. Afterwards, the task can be adapted to new, formerly unknown environments. One particular challenge arising with this technique is generalization of demonstrations in order to get a generic description of the task. In this paper a new methodology for solving this problem is proposed. The main part of the algorithm exploits principles known from fluid dynamics.