Wearable lower limb neuroprosthesis: system architecture and control tuning

The use of functional electrical stimulation (FES) through neuroprosthesis is becoming a promising solution in lower limb neurorehabilitation. However, the wearability constraints and time-consuming tuning of stimulation parameters still limit the daily use of neuroprostheses. This work proposes two major contributions, namely: (i) a conceptual design and technical architecture of a fully wearable lower limb neuroprosthesis; and (ii) a Matlab-OpenSim framework that enables fast subject-and muscle-specific tuning of FES controllers based on OpenSim musculoskeletal models. The validation procedures for this study were divided into three phases: (i) Verification of the system architecture real-time requirements; (ii) evaluation of the reliability of the MATLAB-OpenSim framework for tuning PID controller; and (iii) its subsequent use in the neuroprosthesis control with a healthy subject. The obtained results demonstrated that the neuroprosthesis system was able to meet the real-time requirements, with control and data acquisition call periods below 10 ms. Further findings indicated reliable and stable behavior of the simulation-tuned PID controller with an overshoot of 9.82% and a rise time of 0.063 s. The trajectory tracking control results with the neuroprosthesis corroborated the robustness of the tuned PID controller in tracking the desired ankle trajectory (RMSE = 17.23 ± 2.97º and time delay = 0.21 ± 0.070 s).This work has been supported by the Fundação para a Ciência e Tecnologia (FCT) through the Reference Scholarship under Grant SFRH/BD/147878/2019, the Stimulus of Scientific Employment under Grant 2020.03393.CEECIND, and in part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the SmartOs project under Grant NORTE-01-0145-FEDER-030386. It is also supported under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Achieving Practical Functional Electrical Stimulation-driven Reaching Motions In An Individual With Tetraplegia

Functional electrical stimulation (FES) is a promising technique for restoring the ability to complete reaching motions to individuals with tetraplegia due to a spinal cord injury (SCI). FES has proven to be a successful technique for controlling many functional tasks such as grasping, standing, and even limited walking. However, translating these successes to reaching motions has proven difficult due to the complexity of the arm and the goaldirected nature of reaching motions. The state-of-the-art systems either use robots to assist the FES-driven reaching motions or control the arm of healthy subjects to complete planar motions. These controllers do not directly translate to controlling the full-arm of an individual with tetraplegia because the muscle capabilities of individuals with spinal cord injuries are unique and often limited due to muscle atrophy and the loss of function caused by lower motor neuron damage. This dissertation aims to develop a full-arm FES-driven reaching controller that is capable of achieving 3D reaching motions in an individual with a spinal cord injury. Aim 1 was to develop a complete-arm FES-driven reaching controller that can hold static hand positions for an individual with high tetraplegia due to SCI. We developed a combined feedforward-feedback controller which used the subject-specific model to automatically determine the muscle stimulation commands necessary to hold a desired static hand position. Aim 2 was to develop a subject-specific model-based control strategy to use FES to drive the arm of an individual with high tetraplegia due to SCI along a desired path in the subject’s workspace. We used trajectory optimization to find feasible trajectories which explicitly account for the unique muscle characteristics and the simulated arm dynamics of our subject with tetraplegia. We then developed a model predictive control controller to iii control the arm along the desired trajectory. The controller developed in this dissertation is a significant step towards restoring full arm reaching function to individuals with spinal cord injuries

Event-based control for sit-to-stand transition using a wearable exoskeleton

Sit-to-stand transition is an essential step in a lower limb rehabilitation therapy, mainly for assisting the patient to transit from wheel chair to the next level of therapy. A mixed stiffness-damping control adaptation is proposed for this task which will help in reaching the final position with a constant velocity. A combination of control model is proposed to ensure the initiation and the final stage of the transition, such as to ensure stability and to maintain the equilibrium. The combined control model helps in reaching the goal position with equal participation from the user. For patient studies, such as with paraplegic patients, a combinational control model with muscle stimulation can be included to provide a complete assistance. The role of muscle stimulation and joint movement assistance is also considered in this control model. Further, final stage of this transition must ensure keeping or helping the user to maintain the upright position.Peer ReviewedPostprint (author's final draft

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

Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-to-target paths. We tested our trajectory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers (

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

Multifingered robot hand robot operates using teleoperation

The purpose of research on anthropomorphic dextrous manipulation is to develop anthropomorphic dextrous robot hand which approximates the versatility and sensitivity of the human hand by teleoperation methods that will communicate in master– slave manners. Glove operates as master part and multi-fingered hand as slave. The communication medium between operator and multi-fingered hand is via KC-21 Bluetooth wireless modules. Multi-fingered hand developed using 5 volt, 298:1 gear ratio micro metal dc motors which controlled using L293D motor drivers and actuator controlled the movement of robot hand combined with dextrous human ability by PIC18F4520 microcontroller. The slave components of 5 fingers designed with 15 Degree of Freedom (DOF) by 3 DOF for each finger. Fingers design, by modified IGUS 07-16-038-0 enclosed zipper lead E-Chain® Cable Carrier System, used in order to shape mimic as human size. FLEX sensor, bend sensing resistance used for both master and slave part and attached as feedback to the system, in order to control position configuration. Finally, the intelligence, learning and experience aspects of the human can be combined with the strength, endurance and speed of the robot in order to generate proper output of this project

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)