Assistive robotics: research challenges and ethics education initiatives

Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials. Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations. In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft

On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Lag-lead based assessment and adaptation of exercise speed for stroke survivors

This document is the Accepted Manuscript version of the following article: Angelo Basteris, Sharon M. Mijenhuis, Jaap H. Buurke, Gerdienke B. Prange, and Farshid Amirabdolllahian, ‘Lag–lead based assessment and adaptation of exercise speed for stroke survivors’, Robotics and Autonomous Systems, Vol. 73: 144-154, November 2015. The final, published version is available online at doi: https://doi.org/10.1016/j.robot.2014.08.013.The SCRIPT project aims at delivering machine-mediated hand and wrist exercises to people with stroke in their homes. In this context, adapting the exercise to the individual needs potentially enhances recovery. We designed a system composed of a passive-actuated wearable device, a personal computer and an arm support. The system enables users to exercise their hand and wrist movements by playing interactive games which were developed as part of the project. Movements and their required speed are tailored on the individual's capabilities. During the exercise the system assesses whether the subject is in advance (leading) or in delay (lagging) with respect to a reference trajectory. This information provides input to an adaptive mechanism which changes the required movement speed in order to make the exercise neither too easy nor too challenging. In this paper, we show results of the adaptation process in a study involving seven persons with chronic stroke who completed a six weeks training in their homes. Based on the patterns observed in difficulty and lag-lead score, we defined five session types (challenging, challenging-then supporting, supporting, under-supporting and under-challenging). We show that the mechanism of adaptation has been effective in 195 of 248 (78.6%) sessions. Based on our results, we propose the lag-lead based assessment and adaptation as an auto-tuning tool for machine based exercise, with particular focus on rehabilitation robotics. Also, the classification of sessions among different types can be applied to other studies in order to better understanding the progression of therapy in order to maximize its outcome.Peer reviewe

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Simulation of interactive motor behaviours in game theory framework for upper-limb rehabilitation

An increasing number of individuals are affected by neurological diseases worldwide. Nowadays, stroke is the leading cause of adult disability in western countries, with upper limb hemiparesis being one of the most common consequences. Therefore, there is a growing interest in developing robotic interfaces to provide neurologically affected individuals the right amount of assistance to guarantee a great recovery. The interactive control of such rehabilitation robots with a stroke survivor is critical to motor recovery, and a successful rehabilitation requires the patient to be engaged in motor task execution. This thesis focuses on the new development of an interactive robot controller, and aims to ensure that differential game theory can be used as a framework to describe various interactive behaviours between a robot and a human user. In this thesis, it will be simulated the interaction between a robot and an injured human user who is recovering after stroke in the game theory framework, demonstrating that it can induce a stable interaction between the two partners by identifying each other’s control law and allow them to successfully perform the task with minimum effort. In this thesis is expected to find a detailed description of the different interactive motor behaviours that exist between a rehabilitation robot and a human user: collaboration, cooperation, competition and co-activity. It will also contain the simulation of these behaviours. In the description of the human-robot interactive motor behaviours, it will be seen that some of these behaviours are modelled in the simulation in the game theory framework, such as collaboration, cooperation and competition, while co-activity consists on a problem where the robot and the human are modelled as two independent linear quadratic regulators. Finally, it will be provided a comparison between the use of a game theory controller and the use of a linear quadratic regulator controller for the development of a rehabilitation robot and it will be demonstrated why a game theory controller is a better option for robots that work in physical contact with humans