Lower limb rehabilitation robotics: The current understanding and technology

BACKGROUND: With the increasing rate of ambulatory disabilities and rise in the elderly population, advance methods to deliver the rehabilitation and assistive services to patients have become important. Lower limb robotic therapeutic and assistive aids have been found to improve the rehabilitation outcome. OBJECTIVE: The article aims to present the updated understanding in the field of lower limb rehabilitation robotics and identify future research avenues. METHODS: Groups of keywords relating to assistive technology, rehabilitation robotics, and lower limb were combined and searched in EMBASE, IEEE Xplore Digital Library, Scopus, Web of Science and Google Scholar database. RESULTS: Based on the literature collected from the databases we provide an overview of the understanding of robotics in rehabilitation and state of the art devices for lower limb rehabilitation. Technological advancements in rehabilitation robotic architecture (sensing, actuation and control) and biomechanical considerations in design have been discussed. Finally, a discussion on the major advances, research directions, and challenges is presented. CONCLUSIONS: Although the use of robotics has shown a promising approach to rehabilitation and reducing the burden on caregivers, extensive and innovative research is still required in both cognitive and physical human-robot interaction to achieve treatment efficacy and efficiency.

Explaining in Time

Explainability has emerged as a critical AI research objective, but the breadth of proposed methods and application domains suggest that criteria for explanation vary greatly. In particular, what counts as a good explanation, and what kinds of explanation are computationally feasible, has become trickier in light of oqaque “black box” systems such as deep neural networks. Explanation in such cases has drifted from what many philosophers stipulated as having to involve deductive and causal principles to mere “interpretation,” which approximates what happened in the target system to varying degrees. However, such post hoc constructed rationalizations are highly problematic for social robots that operate interactively in spaces shared with humans. For in such social contexts, explanations of behavior, and, in particular, justifications for violations of expected behavior, should make reference to socially accepted principles and norms. In this article, we show how a social robot’s actions can face explanatory demands for how it came to act on its decision, what goals, tasks, or purposes its design had those actions pursue and what norms or social constraints the system recognizes in the course of its action. As a result, we argue that explanations for social robots will need to be accurate representations of the system’s operation along causal, purposive, and justificatory lines. These explanations will need to generate appropriate references to principles and norms—explanations based on mere “interpretability” will ultimately fail to connect the robot’s behaviors to its appropriate determinants. We then lay out the foundations for a cognitive robotic architecture for HRI, together with particular component algorithms, for generating explanations and engaging in justificatory dialogues with human interactants. Such explanations track the robot’s actual decision-making and behavior, which themselves are determined by normative principles the robot can describe and use for justifications.

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 metric.