A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists' demonstrations.

Background: A promising approach to improving motor recovery during rehabilitation is the use of robotic rehabilitation devices. These robotic devices provide tools to monitor the patient's recovery progress while providing highly standardized and intensive therapy. A major challenge in using these robotic devices is the ability to decide when to assist the user.

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions.

Background And Objective: One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user's skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session.

Patients' physiological reactions to competitive rehabilitation therapies assisted by robotic devices.

Background: The aging of the population and the progressive increase in life expectancy in developed countries is leading to a high incidence of cerebrovascular diseases. Several studies have demonstrated that robot-assisted rehabilitation therapies combined with serious games can improve rehabilitation outcomes. Social interaction in the form of multiplayer games has been highlighted as a potential element to increase patient's motivation and exercise intensity, which professionals have described as one of the determining factors in maximizing rehabilitation outcomes.

Development of a single actuator exoskeleton for wrist and forearm rehabilitation.

Recent estimations state that the absolute number of strokes will increase in the future. For this reason, novel rehabilitation therapies, such as robot-assisted therapy, are essential to speed up patient recovery. This paper describes the design, development, and control aspects of a light-exoskeleton addressing forearm and wrist motions using one actuator.

Differences in Physiological Reactions Due to a Competitive Rehabilitation Game Modality.

Interpersonal rehabilitation games, compared to single-player games, enhance motivation and intensity level. Usually, it is complicated to restrict the use of the system to pairs of impaired patients who have a similar skill level. Thus, such games must be dynamically adapted.

User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients.

Background: Assistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. Several studies have demonstrated the effectiveness in the use of assistive technology for environment control and communication systems. The progress of brain-computer interfaces (BCI) research together with exoskeleton enable a person with motor impairment to interact with new elements in the environment.

Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms.

Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project.

A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients.

Post-stroke neurorehabilitation based on virtual therapies are performed completing repetitive exercises shown in visual electronic devices, whose content represents imaginary or daily life tasks. Currently, there are two ways of visualization of these task. 3D virtual environments are used to get a three dimensional space that represents the real world with a high level of detail, whose realism is determinated by the resolucion and fidelity of the objects of the task.

Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot.

This paper presents a kinematic reconstruction algorithm for the variables of the human arm joints in robot-aided neurorehabilitation therapies. The presented algorithm uses the end effector of a rehabilitation robot and an accelerometer placed onto the upper arm to compute accurate values of the human arm chain. The goal of this algorithm is to obtain the joint values of the patient's arm to provide objective information to the therapist about the progress of the patient and to study the effectiveness of these kind of therapies.

Estimation of Human Arm Joints Using Two Wireless Sensors in Robotic Rehabilitation Tasks.

This paper presents a novel kinematic reconstruction of the human arm chain with five degrees of freedom and the estimation of the shoulder location during rehabilitation therapy assisted by end-effector robotic devices. This algorithm is based on the pseudoinverse of the Jacobian through the acceleration of the upper arm, measured using an accelerometer, and the orientation of the shoulder, estimated with a magnetic angular rate and gravity (MARG) device. The results show a high accuracy in terms of arm joints and shoulder movement with respect to the real arm measured through an optoelectronic system.

Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described.