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

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

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

Evaluating the child-robot interaction of the NAOTherapist platform in pediatric rehabilitation

NAOTherapist is a cognitive robotic architecture whose main goal is to develop non-contact upper-limb rehabilitation sessions autonomously with a social robot for patients with physical impairments. In order to achieve a fluent interaction and an active engagement with the patients, the system should be able to adapt by itself in accordance with the perceived environment. In this paper, we describe the interaction mechanisms that are necessary to supervise and help the patient to carry out the prescribed exercises correctly. We also provide an evaluation focused on the child-robot interaction of the robotic platform with a large number of schoolchildren and the experience of a first contact with three pediatric rehabilitation patients. The results presented are obtained through questionnaires, video analysis and system logs, and have proven to be consistent with the hypotheses proposed in this work

An integrative framework for tailoring virtual reality based motor rehabilitation after stroke

Stroke is a leading cause of life-lasting motor impairments, undermining the quality of life of stroke survivors and their families, and representing a major chal lenge for a world population that is ageing at a dramatic rate. Important technologi cal developments and neuroscientific discoveries have contributed to a better under standing of stroke recovery. Virtual Reality (VR) arises as a powerful tool because it allows merging contributions from engineering, human computer interaction, reha bilitation medicine and neuroscience to propose novel and more effective paradigms for motor rehabilitation. However, despite evidence of the benefits of these novel training paradigms, most of them still rely on the choice of particular technologi cal solutions tailored to specific subsets of patients. Here we present an integrative framework that utilizes concepts of human computer confluence to 1) enable VR neu rorehabilitation through interface technologies, making VR rehabilitation paradigms accessible to wide populations of patients, and 2) create VR training environments that allow the personalization of training to address the individual needs of stroke patients. The use of these features is demonstrated in pilot studies using VR training environments in different configurations: as an online low-cost version, with a myo electric robotic orthosis, and in a neurofeedback paradigm. Finally, we argue about the need of coupling VR approaches and neurocomputational modelling to further study stroke and its recovery process, aiding on the design of optimal rehabilitation programs tailored to the requirements of each user.info:eu-repo/semantics/publishedVersio

THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children

Neurorehabilitation therapies exploiting the use-dependent plasticity of our neuromuscular system are devised to help patients who suffer from injuries or diseases of this system. These therapies take advantage of the fact that the motor activity alters the properties of our neurons and muscles, including the pattern of their connectivity, and thus their functionality. Hence, a sensor-motor treatment where patients makes certain movements will help them (re)learn how to move the affected body parts. But these traditional rehabilitation processes are usually repetitive and lengthy, reducing motivation and adherence to the treatment, and thus limiting the benefits for the patients

CLARA: Building a Socially Assistive Robot to Interact with Elderly People

Although the global population is aging, the proportion of potential caregivers is not keeping pace. It is necessary for society to adapt to this demographic change, and new technologies are a powerful resource for achieving this. New tools and devices can help to ease independent living and alleviate the workload of caregivers. Among them, socially assistive robots (SARs), which assist people with social interactions, are an interesting tool for caregivers thanks to their proactivity, autonomy, interaction capabilities, and adaptability. This article describes the different design and implementation phases of a SAR, the CLARA robot, both from a physical and software point of view, from 2016 to 2022. During this period, the design methodology evolved from traditional approaches based on technical feasibility to user-centered co-creative processes. The cognitive architecture of the robot, CORTEX, keeps its core idea of using an inner representation of the world to enable inter-procedural dialogue between perceptual, reactive, and deliberative modules. However, CORTEX also evolved by incorporating components that use non-functional properties to maximize efficiency through adaptability. The robot has been employed in several projects for different uses in hospitals and retirement homes. This paper describes the main outcomes of the functional and user experience evaluations of these experiments.This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116), the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 825003 (DIH-HERO SUSTAIN), the RoQME and MiRON Integrated Technical Projects funded, in turn, by the EU RobMoSys project (H20202-732410), the project RTI2018-099522-B-C41, funded by the Gobierno de España and FEDER funds, the AT17-5509-UMA and UMA18-FEDERJA-074 projects funded by the Junta de Andalucía, and the ARMORI (CEIATECH-10) and B1-2021_26 projects funded by the University of Málaga. Partial funding for open access charge: Universidad de Málaga

An Automated Planning Model for HRI: Use Cases on Social Assistive Robotics

Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors’ experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116), and grants TIN2017-88476-C2-2-R and RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovación-Ministerio de Universidades-Agencia Estatal de Investigación. Javier García is partially supported by the Comunidad de Madrid funds under the project 2016-T2/TIC-1712

On the application of classical planning to real social robotic tasks

Pittsburgh, USA (19-20 June 2017)Automated Planning is now a mature area offering several techniques and search heuristics extremely useful to solve problems in realistic domains. However, its application to real and dynamic environments as Social Robotics requires much work focused, not only in the efficiency of the planners, but also in tractable task modeling and efficient execution and monitoring of the plan into the robotic control architecture. This paper identifies the main issues that must be taken into account while using classical Automated Planning for the control of a social robot and contributes some practical solutions to overcome such inherent difficulties. Some of them are the discrimination between predicates for internal control and external sensing, the concept of predicted nominal behavior with corrective actions or plans, the continuous monitoring of the plan execution and the handling of action interruptions. This manuscript highlights the dependencies between all the design and deployment activities involved: task modeling, plan generation, and action execution and monitoring. A task of Comprehensive Geriatric Assessment (CGA) is used as an illustrative example that can be easily generalized to any other interactive task