Publicly available datasets constitute the ground to evaluate and compare the performance of proposed approaches for monitoring older patients at home. In this chapter, we shed light on the importance of using datasets as a benchmarking tool for comparing various monitoring techniques for detecting the health threats, which we discussed in the previous chapters. The methods, which are tested by us

This chapter aims at giving an insight into a variety of available monitoring technologies and techniques, which aim to provide solutions to the issues listed in Chap. 3. First, we start with discussing possible data collection approaches, by revealing choices of available sensors and underlying constrains. Second, we provide a summary of sensors used for data acquisition in regard to needed medic

In this paper we focus on the joint problem of tracking humans and recognizing human action in scenarios such as a kitchen scenario or a scenario where a robot cooperates with a human, e.g., for a manufacturing task. In these scenarios, the human directly interacts with objects physically by using/manipulating them or by, e.g., pointing at them such as in "Give me that...", 10 recognize these type

Object segmentation is the first step towards more advanced robotic behaviors, as robots need to localize objects before attempting tasks such as grasping or manipulation. A robot vision system should be able to provide accurate object hypotheses in reasonably high frame rates, using images and possibly also depth data. This work proposes a fixation-based object segmentation algorithm able to cope

Is low-cost tracking precise enough for recognition of pointing actions? We investigate the quality of the human body tracking available with a Kinect camera by comparing it to a state-of-the-art motion capture system. The application is action recognition with parametric hidden Markov Models (PHMMs) for programming industrial robots. The data from the Kinect is overall more noisy and potentially

In this paper we discuss the use of the infinite Gaussian mixture model and Dirichlet processes for learning robot movements from demonstrations. Starting point of this work is an earlier paper where the authors learn a non-linear dynamic robot movement model from a small number of observations. The model in that work is learned using a classical finite Gaussian mixture model (FGMM) where the Gaus

Background Globally recommended measures for comprehensive tuberculosis (TB) infection control (IC) are inadequately practiced in most health care facilities in Ethiopia. The aim of this study was to assess the extent of implementation of TB IC measures before and after introducing a comprehensive technical support package in two regions of Ethiopia. Methods We used a quasi-experimental design, wh

Mobile robots that have the ability to cooperate with humans are able to provide new possibilities to manufacturing industries. In this paper, we discuss our mobile robot arm that can a) provide assistance at different locations in a factory and b) be programmed using complex human actions such as pointing in Take this object. We discuss the use of the mobile robot for a feeding scenario where a h

We present a novel low-cost device for scanning object surfaces based on structured LED (Light Emitting Diode) light and a single, monocular image from a standard machine vision camera. During the calibration phase we find the displacement of the imaged LED-projected marker points as a function of depth. Using this displacement data and a suitable interpolation technique, the depth of surface poin

Introduction: Treatment after Anterior Cruciate Ligament (ACL) injury includes physical therapist supervised neuromuscular training, with or without surgical reconstruction, aiming to improve patient-reported outcomes and muscle function. Despite this treatment, sensorimotor deficiencies persist, possibly constituting a contributing factor for risk of re-injury, early-onset osteoarthritis, and a l

In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulatio

The random distribution of features in an iris image texture allows to perform iris-based personal authentication with high confidence. We propose three new iris representations that are based on a multi-scale Taylor expansion of the iris texture. The first one is a phase-based representation that is based on binarized first and second order multi-scale Taylor coefficient. The second one is based

Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions. In this paper we observe that human actions and objects can be seen as being intertwined: we can interp

We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniq

In the area of imitation learning, one of the important research problems is action representation. There has been a growing interest in expressing actions as a combination of meaningful subparts called action primitives. Action primitives could be thought of as elementary building blocks for action representation. In this article, we present a complete concept of learning action primitives to rec

The representation of human movements for recognition and synthesis is important in many application fields such as: surveillance, human-computer interaction, motion capture, and humanoid robots. Hidden Markov models (HMMs) are a common statistical framework in this context, since they are generative and are able to deal with the intrinsic dynamic variation of movements performed by humans. In thi

This work compares iterative 2D-3D Pose Estimation methods for use in real-time applications. The compared methods are available for public as C++ code. One method is part of the openCV library, namely POSIT. Because POSIT is not applicable for planar 3D-point configurations, we include the planar POSIT version. The second method optimizes the pose parameters directly by solving a Non-linear Least

In humanoid robotics, the recognition and synthesis of parametric movements plays an extraordinary role for robot human interaction. Such a parametric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For understanding the whole meaning of a movement of a human, the recognition of its type, likewise its param

The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement