A functional electrical stimulation system for human walking inspired by reflexive control principles

This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions. The functionality of the functional electrical stimulation system was tested by analysis of the gait function of seven healthy volunteers during functional electrical stimulation–assisted treadmill walking compared to unassisted walking. The results demonstrated that the system was successful in synchronising muscle activation throughout the gait cycle and was able to promote functional hip and ankle movements. Overall, the study demonstrates the potential of human-inspired robotic systems in the design of assistive devices for bipedal walking

Hybrid robotic and electrical stimulation assistance can enhance performance and reduce mental demand

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improv- ing quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users’ per- formance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking per- formance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indi- cate that participants found the task less mentally demand- ing during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES train- ing might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance

Neuromechanical Biomarkers for Robotic Neurorehabilitation

: One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Tracking human upper-limb movements with sliding mode control type-II fuzzy logic

© 2016 IEEE. A knowledge of human upper-limb structure and its mechanical functions are important for developing an exoskeleton. The Sliding Mode Control with Fuzzy Type-II is proposed to control the movements of the human extremity joints. The Lagrange method is used to model the dynamics system of human upper-limb. The findings demonstrate the effectiveness of the proposed controller in tracking the desired motion and it is also able to eliminate the chattering problem as well as deal with uncertainties

Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation)

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 user's 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

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

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Development of a hybrid robotic system based on an adaptive and associative assistance for rehabilitation of reaching movement after stroke

Stroke causes irreversible neurological damage. Depending on the location and the size of this brain injury, different body functions could result affected. One of the most common consequences is motor impairments. The level of motor impairment affectation varies between post-stroke subjects, but often, it hampers the execution of most activities of daily living. Consequently, the quality of life of the stroke population is severely decreased. The rehabilitation of the upper-limb motor functions has gained special attention in the scientific community due the poor reported prognosis of post-stroke patients for recovering normal upper-extremity function after standard rehabilitation therapy. Driven by the advance of technology and the design of new rehabilitation methods, the use of robot devices, functional electrical stimulation and brain-computer interfaces as a neuromodulation system is proposed as a novel and promising rehabilitation tools. Although the uses of these technologies present potential benefits with respect to standard rehabilitation methods, there still are some milestones to be addressed for the consolidation of these methods and techniques in clinical settings. Mentioned evidences reflect the motivation for this dissertation. This thesis presents the development and validation of a hybrid robotic system based on an adaptive and associative assistance for rehabilitation of reaching movements in post-stroke subjects. The hybrid concept refers the combined use of robotic devices with functional electrical stimulation. Adaptive feature states a tailored assistance according to the users’ motor residual capabilities, while the associative term denotes a precise pairing between the users’ motor intent and the peripheral hybrid assistance. The development of the hybrid platform comprised the following tasks: 1. The identification of the current challenges for hybrid robotic system, considering twofold perspectives: technological and clinical. The hybrid systems submitted in literature were critically reviewed for such purpose. These identified features will lead the subsequent development and method framed in this work. 2. The development and validation of a hybrid robotic system, combining a mechanical exoskeleton with functional electrical stimulation to assist the execution of functional reaching movements. Several subsystems are integrated within the hybrid platform, which interact each other to cooperatively complement the rehabilitation task. Complementary, the implementation of a controller based on functional electrical stimulation to dynamically adjust the level of assistance is addressed. The controller is conceived to tackle one of the main limitations when using electrical stimulation, i.e. the highly nonlinear and time-varying muscle response. An experimental procedure was conducted with healthy and post-stroke patients to corroborate the technical feasibility and the usability evaluation of the system. 3. The implementation of an associative strategy within the hybrid platform. Three different strategies based on electroencephalography and electromyography signals were analytically compared. The main idea is to provide a precise temporal association between the hybrid assistance delivered at the periphery (arm muscles) and the users’ own intention to move and to configure a feasible clinical setup to be use in real rehabilitation scenarios. 4. Carry out a comprehensive pilot clinical intervention considering a small cohort of patient with post-stroke patients to evaluate the different proposed concepts and assess the feasibility of using the hybrid system in rehabilitation settings. In summary, the works here presented prove the feasibility of using the hybrid robotic system as a rehabilitative tool with post-stroke subjects. Moreover, it is demonstrated the adaptive controller is able to adjust the level of assistance to achieve successful tracking movement with the affected arm. Remarkably, the accurate association in time between motor cortex activation, represented through the motor-related cortical potential measured with electroencephalography, and the supplied hybrid assistance during the execution of functional (multidegree of freedom) reaching movement facilitate distributed cortical plasticity. These results encourage the validation of the overall hybrid concept in a large clinical trial including an increased number of patients with a control group, in order to achieve more robust clinical results and confirm the presented herein.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Ramón Ceres Ruiz.- Secretario: Luis Enrique Moreno Lorente.- Vocal: Antonio Olivier