A framework for user adaptation and profiling for social robotics in rehabilitation

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metricThis work is partially funded by grant RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovación - Ministerio de Universidades - Agencia Estatal de Investigació

Robot-assisted therapy for upper limb impairments in cerebral palsy:A scoping review and suggestions for future research

A growing number of studies investigate the use of robotics therapy for motor (re)habilitation with children with cerebral palsy (CP). Most of these studies use functional robots in very repetitive sessions. While the therapy is effective, very few studies employ social robots, which appears to be a missed opportunity to design more compelling and enjoyable sessions for the children. In this article, we will review robot-assisted upper limb motor (re)habilitation for children with CP. Previous reviews of robot-assisted therapy for CP had mostly focused on lower limbs, or the review was made from a medical point of view, with the sole concern being the therapy's effectiveness. Here, we focus our review on robot-assisted upper limb (re)habilitation and address human-robot interaction considerations. We searched PubMed, Scopus, and IEEE databases and argue that although this area of research is promising and already effective, it would benefit from the inclusion of social robots for a more engaging and enjoyable experience. We suggest four scenarios that could be developed in this direction. The goal of this article is to highlight the relevance of the past work and encourage the development of new ideas where therapy will socially engage and motivate children.</p

Developing a robot-guided interactive simon game for physical and cognitive training

Enveloping cognitive or physical rehabilitation into a game highly increases the patients' commitment with their treatment. Specially with children, keeping them motivated is a very time-consuming work, so therapists are demanding tools to help them with this task. NAOTherapist is a generic robotic architecture that uses Automated Planning techniques to autonomously drive noncontact upper-limb rehabilitation sessions for children with a humanoid NAO robot. Our aim is to develop more robotic games for this platform to enrich its variability and possibilities of interaction. The goal of this work is to present our first attempt to develop a different, more complex game that reuses the previous architecture. We contribute with the design description of a novel robotic Simon game that employs upper-limb poses instead of colors and could qualify as a cognitive and physical training. Statistics of evaluation tests with 14 adults and 56 children are displayed and the outcomes are analyzed in terms of human-robot interaction (HRI) quality. The results demonstrate the application-domain generalization capabilities of the NAOTherapist architecture and give an insight to further analyze the therapeutic benefits of the new developed Simon game.This work is partially funded by grant TIN2012-38079-C03-02 and TIN2015-65686- C5-1-R of Spanish Ministerio de Economía y Competitividad. We also want to thank the Joan Miró school of Leganés for their assistance with the evaluations, to the teachers and the management team for their support, and specially to all the children who kindly participated in the evaluation and enjoyed playing with our robots

Hybrid Position and Orientation Tracking for a Passive Rehabilitation Table-Top Robot

This paper presents a real time hybrid 2D position and orientation tracking system developed for an upper limb rehabilitation robot. Designed to work on a table-top, the robot is to enable home-based upper-limb rehabilitative exercise for stroke patients. Estimates of the robot's position are computed by fusing data from two tracking systems, each utilizing a different sensor type: laser optical sensors and a webcam. Two laser optical sensors are mounted on the underside of the robot and track the relative motion of the robot with respect to the surface on which it is placed. The webcam is positioned directly above the workspace, mounted on a fixed stand, and tracks the robot's position with respect to a fixed coordinate system. The optical sensors sample the position data at a higher frequency than the webcam, and a position and orientation fusion scheme is proposed to fuse the data from the two tracking systems. The proposed fusion scheme is validated through an experimental set-up whereby the rehabilitation robot is moved by a humanoid robotic arm replicating previously recorded movements of a stroke patient. The results prove that the presented hybrid position tracking system can track the position and orientation with greater accuracy than the webcam or optical sensors alone. The results also confirm that the developed system is capable of tracking recovery trends during rehabilitation therapy

A three-layer planning architecture for the autonomous control of rehabilitation therapies based on social robots

This manuscript focuses on the description of a novel cognitive architecture called NAOTherapist, which provides a social robot with enough autonomy to carry out a non-contact upper limb rehabilitation therapy for patients with physical impairments, such as cerebral palsy and obstetric brachial plexus palsy. NAOTherapist comprises three levels of Automated Planning. In the high-level planning, the physician establishes the parameters of the therapy such as the scheduling of the sessions, the therapeutic objectives to be achieved and certain constraints based on the medical records of the patient. This information is used to establish a customized therapy plan. The objective of the medium-level planning is to execute and monitor every previous planned session with the humanoid robot. Finally, the low-level planning involves the execution of path-planning actions by the robot to carry out different low-level instructions such as performing poses. The technical evaluation shows an accurate definition and monitoring of the therapies and sessions and a fluent interaction with the robot. This automated process is expected to save time for the professionals while guaranteeing the medical criteria.This work is partially funded by grant TIN2015-65686-C5-1-R and TIN2012-38079-C03-02 of Spanish Ministerio de Economía y Competitividad

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented

Robotics Technology in Mental Health Care

This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the practitioner's toolbox. The goal of this chapter is to provide a brief overview of the field, discuss the recent use of robotics technology in mental health care practice, explore some of the design issues and ethical issues of using robots in this space, and finally to explore the potential of emerging technology