Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Brain-machine interfaces for rehabilitation in stroke: A review

BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

MOSAR: A Soft-Assistive Mobilizer for Upper Limb Active Use and Rehabilitation

In this study, a soft assisted mobilizer called MOSAR from (Mobilizador Suave de Asistencia y Rehabilitación) for upper limb rehabilitation was developed for a 11 years old child with right paretic side. The mobilizer provides a new therapeutic approach to augment his upper limb active use and rehabilitation, by means of exerting elbow (flexion-extension), forearm (pronation-supination) and (flexion-extension along with ulnar-radial deviations) at the wrist. Preliminarily, the design concept of the soft mobilizer was developed through Reverse Engineering of his upper limb: first casting model, silicone model, and later computational model were obtained by 3D scan, which was the parameterized reference for MOSAR development. Then, the manufacture of fabric inflatable soft actuators for driving the MOSAR system were carried out. Lastly, a law close loop control for the inflation-deflation process was implemented to validate FISAs performance. The results demonstrated the feasibility and effectiveness of the FISAs for being a functional tool for upper limb rehabilitation protocols by achieving those previous target motions similar to the range of motion (ROM) of a healthy person or being used in other applications

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Diseño de entornos de realidad virtual aplicables a sistemas de robótica asistencial: un análisis literario

Virtual Reality (VR) environments can be applied to assistive robotics to improve the effectiveness and the user experience perception in the rehabilitation process due to its innovative nature, getting to entertain patients while they recover their motor functions. This literature review pretends to analyze some design principles of VR environments developed for upper limb rehabilitation processes. The idea is to identify features related to peripheral and central nervous systems, types of information included as feedback to increase the user's levels of immersion having a positive impact on the user's performance and experience during the treatment. A total of 32 articles published in Scopus, IEEE, PubMed, and Web of Science in the last four years were reviewed. We present the article selection process, the division by concepts presented previously, and the guidelines that can be considered for the design of VR environments applicable to assistive robots for upper limbs rehabilitation processes.Los entornos de Realidad Virtual (RV) aplicables a sistemas de robótica asistencial pueden ser diseñados de manera que mejoren la efectividad y la experiencia de usuario de los procesos de rehabilitación debido a su naturaleza novedosa, logrando entretener a los pacientes mientras recuperan sus funciones motoras. Esta revisión literaria pretende analizar los criterios de diseño de entornos de RV utilizados en procesos de rehabilitación de miembro superior, identificando las características de entornos para rehabilitación de problemas asociados el sistema nervioso central y periféricos, los tipos de información que se realimenta al usuario para beneficiar los niveles de inmersión y su impacto en términos del desempeño y la experiencia del usuario en tratamiento. Un total de 32 artículos publicados en revistas indexadas de Scopus, IEEE, PubMed y Web of Science en los últimos cuatro años fueron revisados. Se presenta el proceso de selección de artículos, la división por las temáticas presentadas anteriormente y los lineamientos generales que pueden ser considerados para el diseño de entornos de RV aplicables a robots asistenciales en procesos de rehabilitación de miembro superior