Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots’ understanding to patients’ physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients’ limitations.
The ultimate goal of ambient assisted living is to help elderly people live a healthy life in the convenience of their homes by making more intelligent technology bring them a set of required assistive tools. In this paper we describe a robotic fitness coach that learns a set of physical exercises from a professional trainer, and assists elderly subjects in performing these gestures. The gestures were selected from an actual training programme at an elderly care home. When demonstrating gestures, the robot performs the learned gestures to the best of its abilities, and while monitoring the elderly subject with an RGB-D camera, provides verbal guidance to complement the visual display, correcting gestures on the fly. We provide a detailed description of the training programme, the gesture acquisition, replication and evaluation algorithms, our solution to the robot stability problem, and a set of preliminary user tests to validate our approach. The TARDIS project aims to build a scenario-based serious-game simulation platform for NEETs and job-inclusion associations that supports social training and coaching in the context of job interviews. This paper presents the general architecture of the TARDIS job interview simulator, and the serious game paradigm that we are developing. For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation "in the wild." The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions. Engineers developing socially assistive robots for promoting a healthy and active lifestyle are facing several mechanical and interactive design challenges. In this paper we are answering the question whether a humanoid exercising partner capable of working out together with its user makes significant improvement in the user's engagement. We conducted a study with 56 participants doing five isometric exercises in three conditions comparing the differences between working out alone (IC), with an robot instructor (RI) guiding the user, or with a robot companion (RC) exercising co-actively with the user. Our results show that the RC brings a motivational gain to exercise longer compared to all other conditions. In order to keep healthy health of elderly people, daily exercise is an important factor. Therefore, we have developed an exercise support system with robot partner to provide the daily exercise program. Furthermore, Human-like-motion can have a contribution to exert a strong influence on the person through the human-robot interaction, because bodily expressions convey important and effective information. If robots could adapt the features of human behavior to their motions and skills, the communication would become more smooth and natural. In this paper, we propose a learning structure for imitation learning. We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors. Virtual Reality (VR) has the potential to support motor learning in ways exceeding beyond the possibilities provided by real world environments. New feedback mechanisms can be implemented that support motor learning during the performance of the trainee and afterwards as a performance review. As a consequence, VR environments excel in controlled evaluations, which has been proven in many other application scenarios. However, in the context of motor learning of complex tasks, including full-body movements, questions regarding the main technical parameters of such a system, in particular that of the required maximum latency, have not been addressed in depth. To fill this gap, we propose a set of requirements towards VR systems for motor learning, with a special focus on motion capturing and rendering. We then assess and evaluate state-of-the-art techniques and technologies for motion capturing and rendering, in order to provide data on latencies for different setups. We focus on the end-to-end latency of the overall system, and present an evaluation of an exemplary system that has been developed to meet these requirements.