Arturo Bertomeu-Motos

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. Furthermore, 2D virtual environments are used to represent the tasks with a low degree of realism using techniques of bidimensional graphics. However, the type of visualization can influence the quality of perception of the task, affecting the patients sensorimotor performance. The purpose of this paper was to evaluate if there were differences in patterns of kinematic movements when post-stroke patients performed a reach task viewing a virtual therapeutic game with two different type of visualization of virtual environment: 2D and 3D. Nine post-stroke patients have participated in the study receiving a virtual therapy assisted by PUPArm rehabilitation robot. Horizontal movements of the upper limb were performed to complete the aim of the tasks, which consist in reaching peripheral or perspective targets depending on the virtual environment shown. Various parameter types such as the maximum speed, reaction time, path length, or initial movement are analyzed from the data acquired objectively by the robotic device to evaluate the influence of the task visualization. At the end of the study, a usability survey was provided to each patient to analysis his/her satisfaction level. For all patients, the movement trajectories were enhanced when they completed the therapy. This fact suggests that patients motor recovery was increased. Despite of the similarity in majority of the kinematic parameters, differences in reaction time and path length were higher using the 3D task. Regarding the success rates were very similar. In conclusion, the using of 2D environments in virtual therapy may be a more appropriate and comfortable way to perform tasks for upper limb rehabilitation of post-stroke patients, in terms of accuracy in order to effectuate optimal kinematic trajectories.

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. Furthermore, the range of motion (ROM) of 50 healthy subjects is studied from two different trials, one trying to avoid shoulder movements and the second one forcing them. Moreover, the shoulder movement in the second trial is also estimated accurately. Besides the fact that the posture of the patient can be corrected during the exercise, the therapist could use the presented algorithm as an objective assessment tool. In conclusion, the joints’ estimation enables a better adjustment of the therapy, taking into account the needs of the patient, and consequently, the arm motion improves faster.

Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices

BackgroundEnd-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient’s hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm.MethodsThe proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815–28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the ‘PUPArm’ planar robot with three post-stroke patients.ResultsThe proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients.ConclusionsThe proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment.

Delta-Theta Intertrial Phase Coherence Increases During Task Switching in a BCI Paradigm

A broad variety of perceptual, sensorimotor and cognitive operations have shown to be linked to electroencephalographic (eeg) oscillatory activity. For instance, movement preparation or cognitive processing were linked to delta band (1–5 Hz) oscillations. Such link could be exploited in brain-computer interface (bci) paradigms translating modulations of brain activity into control signals of external devices or computers. However, current bcis are often driven by fast rhythmic brain activity, e.g. in the alpha (9–15 Hz) or beta band (15–30 Hz). Introducing slower oscillations, such as delta or theta (4–8 Hz) band activity, might extent the spectrum of bci applications, particularly in the context of bci-related restoration of movements. To detect voluntary modulations of motor cortical activity in such paradign, an active interval during which users are instructed to e.g. imagine hand movements becomes compared to a task-free interval during which users are instructed to relax. We report that cortical oscillations of eeg in delta and theta frequencies clearly synchronize at the onset and at the end of a bci task, what might be a physiological marker for task switching that could be useful for improving bci control. We also found that inter-trial-phase coherence (itpc) significantly increased at the end of reference intervals during which participants were instructed to relax. This may indicate that during initial phases of bci learning, users are actively relaxing, a finding with important implications for monitoring bci learning and control.

Mechanical Design of a Novel Hand Exoskeleton Driven by Linear Actuators

This paper presents the mechanical design of a novel hand exoskeleton for assistance and rehabilitation therapies. As a solution for the movement transmission, the proposed device uses modular linkage that are attached to each finger by means of snap-in fixations. The linkage is kinematically and dynamically analyzed by means of simulations with AnyBody Simulation Software to obtain an estimation of the range of motion and admissible forces. In order to check the deviations of the real performance respect to the simulated results, due to uncertain variables, a first prototype is built and tested.

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 patients arm to provide objective information to the therapist about the progress of the patient and to study the effectiveness of these kind of therapies.

Sensory Feedback with a Hand Exoskeleton Increases EEG Modulation in a Brain-Machine Interface System

Brain-machine interfaces (bci) translate brain activity into control signals of external devices, such as robots, prostheses or computers. A well-established bci paradigm uses signal power modulations of fast rhythmic brain activity. Such power modulations are linked to a broad variety of sensorimotor, cognitive and perceptual tasks, and feedback for the user can be provided by different sensory modalities, so we decided to investigate whether different sensory modalities of feedback might differently modulate the electroencephalography (eeg) during a bci task. Ten healthy volunteers performed bci motor imagery session while controlling a hand exoskeleton. Participants received feedback with different sensory modalities: visual, somatosensory (using a hand exoskeleton) or auditory. As expected, we found that cortical oscillations of eeg in beta frequencies were modulated by movements. Our main finding was that modulation of beta band in eeg was strongly increased by somatosensory feedback using the exoskeleton, a finding with important implications for design and implementation of bci experiments.

Modulation of Functional Connectivity Evaluated by Surface EEG in Alpha and Beta Band During a Motor-Imagery Based BCI Task

Brain-computer interfaces (BCI) has being used to treat and assist neurologic patients to make the activities of daily living through electroencephalography (EEG) signals. Real movement or imagery movement produce changes in power of sensorimotor rhythms, called event-related (de)synchronization for the supression or for the increase of oscillatory activities. Also, changes between functional connectivity of cortical areas have been reported, evaluated by different recording techniques. Using the phase-locking-value index (PLV), we compared the changes in functional connectivity with the changes in power of surface EEG of ten healthy subjects during a BCI task using motor imagery. Modulation of functional connectivity related to task and to the discriminate band frequencies were studied, what might have implications for improving control of BCI systems.

Grasping Detection with Force Sensor Embedded in a Hand Exoskeleton

This paper presents the results of the force measurements performed with an industrial-grade load cell embedded in the linkage of a hand exoskeleton. The force sensor is placed such that it measures the interaction force between the index finger of the user and the actuator that controls its motion. This architecture has been used in an experimental test in which users had to grasp an object (cup or bottle), interact with it and then release it. Force measurements shows that this disposition allows to discern between successful and unsuccessful grasping.

Multimodal Control Architecture for Assistive Robotics

This document present a multimodal control architecture for assistive robotics which try to minimize the possible aleatory error during the grasping process by means of visual servoing techniques. Through the gaze tracking information provided by the Tobii Pro Glasses 2 the user is capable to interact with the system in order to select the desirable object as well as indicate the intention to grasp it. At the same time, employing the 6DoF optical tracking information provided by the OptiTrack V120:Trio, the system defines the position to reach and also supervises the movement of the robot to detect some deviation in the trajectory execution.