This paper presents the implementation and evaluation results of the German research project SERROGA (2012 till mid 2015), which aimed at developing a robot companion for domestic health assistance for older people that helps keeping them physically and mentally fit to remain living independently in their own homes for as long as possible. The paper gives an overview of the developed companion robot, its system architecture, and essential skills, behaviors, and services required for a robotic health assistant. Moreover, it presents a new approach allowing a quantitative description and assessment of the navigation complexity of apartments to make them objectively comparable for function tests under real-life conditions. Based on this approach, the results of function tests executed in 12 apartments of project staff and seniors are described. Furthermore, the paper presents findings of a case study conducted with nine seniors (aged 68-92) in their own homes, investigating both instrumental and social-emotional functions of a robotic health assistant. The robot accompanied the seniors in their homes for up to three days assisting with tasks of their daily schedule and health care, without any supervising person being present on-site. Results revealed that the seniors appreciated the robot's health-related instrumental functions and even built emotional bonds with it.

This paper presents results of user evaluations with a socially assistive robot companion for older people suffering from mild cognitive impairment (MCI) and living (alone) at home. Within the European FP7 project “CompanionAble” (2008-2012) [1], we developed assistive technologies combining a mobile robot and smart environment with the aim to support these people and assist them living in their familiar home environment. For a final evaluation, user experience studies were conducted with volunteer users who were invited to a test home where they lived and freely used the robot and integrated system over a period of two days. Services provided by the companion robot include reminders of appointments (pre-defined or added by the users themselves or their informal carer) as well as frequent recommendations to specific activities, which were listed e.g. by their family carers. Furthermore, video contact with relatives and friends, a cognitive stimulation game designed especially to counter the progress of cognitive impairments, and the possibility to store personal items with the robot are offered. Recognition of the user entering or leaving the home is triggering situation specific reminders like agenda items due during the (expected) absence, missed calls or items not to be forgotten. Continuing our previous work published in [2], this paper presents detailed description of the implemented assistive functions and results of user studies conducted during April and May 2012 in the smart house of the Dutch project partner Smart Homes in Eindhoven, The Netherlands.

In order to act socially compliant with humans, mobile robots need to show several behaviors that require the prediction of people's motion. For example, when a robot avoids a person, it needs to respect the human's personal space [1] and the avoidance behavior needs to be smooth, so that it is understandable to the interaction partner. To achieve this, the robot needs to reason about future paths a person is likely to follow. Because humans adapt their avoidance behavior to the robot's motion, the proposed method performs lifelong learning of the people's behavior while it adapts its own behavior to their motion. The human avoidance behavior is modeled by a discrete, multi-modal, spatio-temporal distribution over the people's future occurrences. This prediction is based on the people's positions and their velocities relatively to the robot and the obstacle situation of the robot's environment. The proposed prediction method is significantly better than a simple linear prediction. Particularly, for tactical decisions, like whether to avoid a moving person on the left or on the right side, this approach is well suited. Furthermore, when the humans get used to a robot, also a long-term change of the human behavior towards the robot can be learned by our approach.

Mobile companion robots for elderly people are subject to recent research efforts. To provide useful services the robot must be aware of the position of the user. Since the field-of-view of the robot is limited, the user can easily leave the robot's perception, e.g. by going to another room. This paper proposes a method to search and locate a person not in the vicinity of the robot by driving through the apartment and checking for people. We apply restrictive as well as computationally expensive detection methods on-demand to verify the hypotheses of a real time person tracker. Additionally, the search-tour of the robot is guided by an occurrence probability histogram of previous positions of the user. The system has been tested by evaluating accuracy and time the robot needs to find a user in a 3-room scenario.

Falling down and not managing to get up again is one of the main concerns of elderly people living alone in their home. Robotic assistance for the elderly promises to have a great potential of detecting these critical situations and calling for help. This paper presents a feature-based method to detect fallen people on the ground by a mobile robot equipped with a Kinect sensor. Point clouds are segmented, layered and classified to detect fallen people, even under occlusions by parts of their body or furniture. Different features, originally from pedestrian and object detection in depth data, and different classifiers are evaluated. Evaluation was done using data of 12 people lying on the floor. Negative samples were collected from objects similar to persons, two tall dogs, and five real apartments of elderly people. The best feature-classifier combination is selected to built a robust system to detect fallen people.

Developing methods for people tracking on mobile robots is of great interest to engineers and scientists alike. Plenty of research is focused on pedestrian tracking in public areas. Yet, fewer work exists on practical people tracking in home environments with non-static cameras. This paper presents a real time people tracking system for mobile robots that filters asynchronous, multi-modal detections using a Kalman filter for each person. It allows for upright and sitting pose people tracking in home environments. We evaluate the performance of the tracking system using different detection modalities and compared it to state-of-the-art people detection methods. Evaluation was done on a newly collected indoor data set which we made publicly available for comparison and benchmarking.

In the field of human-robot interaction (HRI), detection, tracking and re-identification of humans in a robot's surroundings are crucial tasks, e. g. for socially compliant robot navigation. Besides the 3D position detection, the estimation of a person's upper-body orientation based on monocular camera images is a challenging problem on a mobile platform. To obtain real-time position tracking as well as upper-body orientation estimations, the proposed system comprises discriminative detectors whose hypotheses are tracked by a Kalman filter-based multi-hypotheses tracker. For appearance-based person recognition, a generative approach, based on a 3D shape model, is used to refine these tracked hypotheses. This model evaluates edges and color-based discrimination from the background. Furthermore, for each person the texture of his or her upper-body is learned and used for person re-identification. When computational resources are limited, the update rate of the model-based optimization reduces itself automatically. Thereby the estimation accuracy decreases, but the system keeps tracking the persons around the robot in real-time. The person's 3D pose is tracked up to a distance of 5.0 meters with an average Euclidean error of 18 cm. The achieved motion independent average upper-body orientation error is 22°. Furthermore, the upper-body texture is learned on-line which allowed a stable person re-identification in our experiments.

This paper presents an architectural overview of a robot-based visitor information system in a university building. Two mobile robots serve as mobile information terminals providing information about the employees, labs, meeting rooms, and offices in the building and are able to guide the visitors to these points of interest. The paper focuses on the different software components needed to meet the requirements of the multi-story office building. Furthermore, the integration of a multi-hypotheses person tracker is outlined, which helps the robots to interact with the people in their near surrounding. Besides first observations on interaction, the further development is outlined as well.

This paper presents results of the development of a socially assistive home robot companion for older people suffering from mild cognitive impairment (MCI) and living (alone) at home. This work was part of the European FP7 project “CompanionAble” (2008-2012) [1] which aimed at developing assistive technologies that can support these elderly and help them to remain in their familiar living environment for as long as possible. To overcome current market entry barriers, from the start we consistently adopted a user- and application-centered development process of the companion robot and focused on three main aspects: (i) the realization of a set of mandatory functionalities to support care recipients and caregivers, (ii) a strict design and usability driven realization to increase the acceptance of the robot by the different end-user groups (the elderly, their relatives, and caregivers), and (iii) the development and component selection considering production and operational costs. In continuation of the work presented in [2], this paper describes the final implementation of the companion robot and presents latest results of functional tests and early findings of user studies recently conducted in the smart house of the Dutch project partner Smart Homes in Eindhoven, The Netherlands.

The paper is addressing several aspects of our work as part of the European FP7 project ¿CompanionAble¿ and gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities and services that have been specified by the different end-user target groups of such a robot companion (the elderly, relatives, caregivers) is manifold. It reaches from situation-specific, intelligent reminding (e.g. taking medication or drinking) and cognitive stimulation, via mobile videophony with relatives or caregivers, up to the autonomous detection of dangerous situations, like falls, and their evaluation by authorized persons via mobile telepresence. From the beginning, our approach has been focused on long-term and everyday suitability and low-cost producibility as important prerequisites for the marketability of the robot companion. Against this background, the paper presents the main system requirements derived from user studies, the consequences for the hardware design and functionality of the robot companion, its system architecture, a key technology for HRI in home environments - the autonomous user tracking and searching, up to the results of already conducted and ongoing functionality tests and upcoming user studies.

The paper gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities of such a robot companion is broad and reaches from reminding functions (e.g. taking medication or drinking) and cognitive stimulation exercises, via mobile videophony with relatives or caregivers, up to the detection and evaluation of critical situations, like falls. The paper is addressing several aspects of our work as part of the European FP7 project “CompanionAble”, as for example the developed robot hardware and its software and control architecture, the implemented skills for robust user detection and tracking and user-centered navigation in the home environment, and reports on already conducted and still ongoing functionality testings and pending usability studies with the end-user target groups (the elderly, relatives, caregivers).