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Die Forschung zur kontrovers diskutierten Robotik in der Pflege und Begleitung von Personen mit Demenz steht noch am Anfang, wenngleich bereits erste Systeme auf dem Markt sind. Der Beitrag gibt entlang beispielhafter, fallbezogener Auszüge Einblicke in das laufende multidisziplinäre Projekt EmoRobot, das sich explorativ und interpretativ mit der Erkundung des Einsatzes von Robotik in der emotionsorientierten Pflege und Versorgung von Personen mit Demenz befasst. Fokussiert werden dabei die je eigenen Relevanzen der Personen mit Demenz.
Die Forschung zur kontrovers diskutierten Robotik in der Pflege und Begleitung von Personen mit Demenz steht noch am Anfang, obwohl bereits erste Systeme auf dem Markt sind. Die zentrale Frage besteht darin, ob und wie sich Telepräsenzroboter im Alltag von Personen mit Demenz integrieren lassen und wie sich diese Integration auf das Lebensarrangement der Menschen mit Demenz und der betreuenden Personen auswirkt. Die (Pflege)Wissenschaft wird sich damit auseinanderzusetzen haben, ob ein fiktionales und/oder ein innovatives Potenzial zur Telepräsenzrobotik in der Pflege von Personen mit Demenz vorliegt. Zu erwarten ist ein Nutzen der Forschungsergebnisse in dreifacher Hinsicht: für die Pflegewissenschaft (Grundlagen für das theoretische Verständnis der Lebenswelten von Personen mit Demenz und erweiterte robotikunterstützte Versorgungs-konzepte), für die Ingenieurwissenschaften (Impulse für die Entwicklung robotischer Unterstützungssysteme) und für Betroffene, Angehörige und Fachpersonen (neue Versorgungsoptionen).
Inductive Logic Programming (ILP) induces first-order clausal theories from learning examples (positive and negative) and knowledge of the domain. Such theories can be used for gradually increasing the understanding of a robot about its world through iterative learning process. In this work we present a method for autonomous creation of negative examples for an ILP learner (i.e. sifting method). We also present a method for managing the iterative learning process (i.e. step transition method) for an autonomous robot that uses the ILP learner for learning. The sifting method uses `out-of-domain' values of the parameters involved in the learning process to create a set of possible negative examples. From these examples the robot autonomously selects those which allow it to efficiently learn better hypotheses. The step transition method enables the autonomous robot to decide how should it learn the new knowledge such that it can also gain profit from its experience. The robot makes this decision by comparing results of two different learning processes conducted on different data sets produced by the same action of the robot. The proposed methods are developed using the ILP learner Aleph, however they can also be used with other similar ILP learners. We experiment with these methods for learning primitive concepts for a mobile autonomous robot in a simple world. Results of the experiments show that the robot learns meaningful definitions of different physical notions in a hierarchical manner.
The positive influence of physical activity for people at all life stages is well known. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract the increase of cardiovascular diseases in our aging society. An easy and good measure of the cardiovascular feedback is the heart rate. Being able to model and predict the response of a subject’s heart rate on work load input allows the development of more advanced smart devices and analytic tools. These tools can monitor and control the subject’s activity and thus avoid overstrain which would eliminate the positive effect on the cardiovascular system. Current heart rate models were developed for a specific scenario and evaluated on unique data sets only. Additionally, most of these models were tested in indoor environments, e.g. on treadmills and bicycle ergometers. However, many people prefer to do sports in outdoors environments and use their smart phone to record their training data. In this paper, we present an evaluation of existing heart rate models and compare their prediction performance for indoor as well as for outdoor running exercises. For this purpose, we investigate analytical models as well as machine learning approaches in two training sets: one indoor exercise set recorded on a treadmill and one outdoor exercise set recorded by a smart phone.
With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.
