Jose Gonzalez-Vargas

A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions

Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non-negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross-correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open-access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/.

Converging clinical and engineering research on neurorehabilitation

Converging clinical and engineering research on neurorehabilitation / , Converging clinical and engineering research on neurorehabilitation / , کتابخانه دیجیتال جندی شاپور اهواز

Hybrid robotic systems for upper limb rehabilitation after stroke: A review

In recent years the combined use of functional electrical stimulation (FES) and robotic devices, called hybrid robotic rehabilitation systems, has emerged as a promising approach for rehabilitation of lower and upper limb motor functions. This paper presents a review of the state of the art of current hybrid robotic solutions for upper limb rehabilitation after stroke. For this aim, studies have been selected through a search using web databases: IEEE-Xplore, Scopus and PubMed. A total of 10 different hybrid robotic systems were identified, and they are presented in this paper. Selected systems are critically compared considering their technological components and aspects that form part of the hybrid robotic solution, the proposed control strategies that have been implemented, as well as the current technological challenges in this topic. Additionally, we will present and discuss the corresponding evidences on the effectiveness of these hybrid robotic therapies. The review also discusses the future trends in this field.

Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

The New Generation of Compliant Actuators for Use in Controllable Bio-Inspired Wearable Robots

In this paper, a conceptual design of the two iterations of compliant actuators used within BioMot project, as well as the control strategy used to operate these actuators, is presented. The result of the presented approach are 2 exoskeleton gait prototypes that will be used for incomplete spinal cord injury (iSCI) patients’ gait rehabilitation.

Attention Level Measurement During Exoskeleton Rehabilitation Through a BMI System

Brain-Machine Interfaces based on wearable robots’ control have been proposed in the research field for rehabilitation purposes. The combination of both systems allow the performance of more natural movements and a higher level of involvement of patients on their therapy. Studies focused on this topic should face several issues related to the integration of these systems. The current work is meant to test the accuracy of a real time Brain-Machine Interface based on the detection of gait attention during lower limb exoskeletal rehabilitation. Four users performed the experiment wearing an ankle exoskeleton. The system provides a coefficient between 0 and 1 depending on the level of attention experienced by the subject. These results show good similitude between real and decoded attention level.

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

Hybrid Robotic System for Reaching Rehabilitation After Stroke: Reporting an Usability Experimentation

The combined use of functional electrical stimulation and robotic exoskeleton in a hybrid rehabilitation system represents a promising research field for rehabilitation of the motor functions after stroke. In this work, we report the results obtained in a study carried out with a hybrid robotic system for reaching rehabilitation. The system was tested in two sessions with one chronic stroke subject.

A novel controller for bipedal locomotion integrating feed-forward and feedback mechanisms

It has been recognized that bipedal locomotion is controlled using feed-forward (e.g., patterned) and feedback (e.g., reflex) control schemes. However, most current controllers fail to integrate the two schemes to simplify speed control of bipedal locomotion. To solve this problem, we here propose a patterned muscle-reflex controller integrating feed-forward control with a muscle-reflex controller. In feed-forward control, the pattern generator is modeled as a Matsuoka neural oscillator that produces four basic activation patterns that mimic those extracted experimentally via electromyograms (EMGs). The associated weights of the patterns for 16 Hill-type musculotendon units (MTUs) are calculated based on a predictive model of muscle excitations under human locomotion. The weighted sums of the basic activation patterns serve as the pre-stimulations to muscle-reflex control of the Hill-type MTUs actuating a 2D-simulated biped. As a result, the proposed controller enables the biped to easily regulate its speed on an even ground by only adjusting the descending input. The speed regulation does not require re-optimizations of the controller for various walking speeds, compared to pure muscle-reflex controllers.