In this paper, a new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.

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Research on execution monitoring in its own is still not very common within the field of robotics and autonomous systems. It is more common that researchers interested in control architectures or execution planning include monitoring as a small part of their work when they realize that it is needed. On the other hand, execution monitoring has been a well studied topic within industrial control, although control theorists seldom use this term. Instead they refer to the problem of fault detection and isolation (FDI). This survey will use the knowledge and terminology from industrial control in order to classify different execution monitoring approaches applied to robotics. The survey is particularly focused on autonomous mobile robotics.

Abstract-This article describes a novel add-on for powered wheelchairs that is composed of a gaze-driven control system and a navigation support system. The add-on was tested by three users. All of the users were individuals with severe disabilities and no possibility of moving independently. The system is an add-on to a standard power wheelchair and can be customized for different levels of support according to the cognitive level, motor control, perceptual skills, and specific needs of the user. The primary aim of this study was to test the functionality and safety of the system in the user's home environment. The secondary aim was to evaluate whether access to a gaze-driven powered wheelchair with navigation support is perceived as meaningful in terms of independence and participation. The results show that the system has the potential to provide safe, independent indoor mobility and that the users perceive doing so as fun, meaningful, and a way to reduce dependency on others. Independent mobility has numerous benefits in addition to psychological and emotional well-being. By observing users' actions, caregivers and healthcare professionals can assess the individual's capabilities, which was not previously possible. Rehabilitation can be better adapted to the individual's specific needs, and driving a wheelchair independently can be a valuable, motivating training tool.

In the near future, autonomous mobile robots are expected to help humans by performing service tasks in many different areas, including personal assistance, transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment without the aid of humans, the robot must have the ability to plan its actions and to execute them robustly and safely. The robot must also have the ability to detect when the execution does not proceed as planned and to correctly identify the causes of the failure. An execution monitoring system allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot's actions by using some sort of predictive model and comparing the predicted outcomes with the observed ones. In contrary, this paper explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. In this paper, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty execution. We investigate the use of several such techniques and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments.