Robust Compensation of Electromechanical Delay during Neuromuscular Electrical Stimulation of Antagonistic Muscles

Neuromuscular electrical stimulation (NMES) can potentially be used to restore the limb function in persons with neurological disorders, such as spinal cord injury (SCI), stroke, etc. Researches on control system design has so far focused on relatively simple unidirectional NMES applications requiring stimulation of single muscle group. However, for some advanced tasks such as pedaling or walking, stimulation of multiple muscles is required. For example, to extend as well as flex a limb joint requires electrical stimulation of an antagonistic muscle pair. This is due to the fact that muscles are unidirectional actuators. The control challenge is to allocate control inputs to antagonist muscles based on the system output, usually a limb angle error to achieve a smooth and precise transition between antagonistic muscles without causing discontinuities. Furthermore, NMES input to each muscle is delayed by an electromechanical delay (EMD), which arises due to the time lag between the electrical excitation and the force development in muscle. And EMD is known to cause instability or performance loss during closed-loop control of NMES. In this thesis, a robust delay compensation controller for EMDs in antagonistic muscles is presented. A Lyapunov stability analysis yields uniformly ultimately bounded tracking for a human limb joint actuated by antagonistic muscles. The simulation results indicate that the controller is robust and effective in switching between antagonistic muscles and compensating EMDs during a simulated NMES task. Further experiments on a dual motor testbed shows its feasibility as an NMES controller for human antagonistic muscles

Switched Kinematic and Force Control for Lower-Limb Motorized Exoskeletons and Functional Electrical Stimulation

Millions of people experience movement deficits from neurological conditions (NCs) that impair their walking ability and leg function. Exercise-based rehabilitation procedures have shown the potential to facilitate neurological reorganization and functional recovery. Lower-limb powered exoskeletons and motorized ergometers have been combined with functional electrical stimulation (FES) to provide repetitive movement, partially reduce the burden of therapists, improve range of motion, and induce therapeutic benefits. FES evokes artificial muscles contractions and can improve muscle mass and strength, and bone density in people with NCs. Stationary cycling is recommended for individuals who cannot perform load-bearing activities or have increased risks of falling. Cycling has been demonstrated to impart physiological and cardiovascular benefits. Motorized FES-cycling combines an electric motor and electrical stimulation of lower-limb muscles to facilitate coordinated, long-duration exercise, while mitigating the inherent muscle fatigue due to FES. Lower-limb exoskeletons coupled with FES, also called neuroprostheses or hybrid exoskeletons, can facilitate continuous, repetitive motion to improve gait function and build muscle capacity. The human-robot interaction during rehabilitative cycling and walking yield a mix of discrete effects (i.e., foot impact, input switching to engage lower-limb muscles and electric motors, etc.) and continuous nonlinear, uncertain, time-varying dynamics. Switching control is necessary to allocate the control inputs to lower-limb muscle groups and electric motors involved during assisted cycling and walking. Kinematic tracking has been the primary control objective for devices that combine FES and electric motors. However, there are force interactions between the machine and the human during cycling and walking that motivate the design of torque-based controllers (i.e., exploit torque or force feedback) to shape the leg dynamics through controlling joint kinematics and kinetics. Technical challenges exist to develop closed-loop feedback control strategies that integrate kinematic and force feedback in the presence of switching and discontinuous effects. The motivation in this dissertation is to design, analyze and implement switching controllers for assisted cycling and walking leveraging kinematic and force feedback while guaranteeing the stability of the human-robot closed-loop system. In Chapter 1, the motivation to design closed-loop controllers for motorized FES-cycling and powered exoskeletons is described. A survey of closed-loop kinematic and force feedback control methods is also introduced related to the tracking objectives presented in the subsequent chapters of the dissertation. In Chapter 2, the dynamics models for walking and assisted cycling are described. First, a bipedal walking system model with switched dynamics is introduced to control a powered lower-limb exoskeleton. Then, a stationary FES-cycling model with nonlinear dynamics and switched control inputs is introduced based on published literature. The muscle stimulation pattern is defined based on the kinematic effectiveness of the rider, which depends on the crank angle. The experimental setup for lower-limb exoskeleton and FES-cycling are described. In Chapter 3, a hierarchical control strategy is developed to interface a cable-driven lower-limb exoskeleton. A two-layer control system is developed to adjust cable tensions and apply torque about the knee joint using a pair of electric motors that provide knee flexion and extension. The control design is segregated into a joint-level control loop and a low-level loop using feedback of the angular positions of the electric motors to mitigate cable slacking. A Lyapunov-based stability analysis is developed to ensure exponential tracking for both control objectives. Moreover, an average dwell time analysis computes an upper bound on the number of motor switches to preserve exponential tracking. Preliminary experimental results in an able-bodied individual are depicted. The developed control strategy is extended and applied to the control of both knee and hip joints in Chapter 4 for treadmill walking. In Chapter 4, a cable-driven lower-limb exoskeleton is integrated with FES for treadmill walking at a constant speed. A nonlinear robust controller is used to activate the quadriceps and hamstrings muscle groups via FES to achieve kinematic tracking about the knee joint. Moreover, electric motors adjust the knee joint stiffness throughout the gait cycle using an integral torque feedback controller. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the kinematic and torque closed-loop error systems, while guaranteeing that the control input signals remain bounded. The developed controllers were tested in real-time walking experiments on a treadmill in three able-bodied individuals at two gait speeds. The experimental results demonstrate the feasibility of coupling a cable-driven exoskeleton with FES for treadmill walking using a switching-based control strategy and exploiting both kinematic and force feedback. In Chapter 5, input-output data is exploited using a finite-time algorithm to estimate the target desired torque leveraging an estimate of the active torque produced by muscles via FES. The convergence rate of the finite-time algorithm can be adjusted by tuning selectable parameters. To achieve cadence and torque tracking for FES-cycling, nonlinear robust tracking controllers are designed for muscles and motor. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the closed-loop cadence error system and global uniformly ultimate bounded (GUUB) torque tracking. A discrete-time Lyapunov-based stability analysis leveraging a recent tool for finite-time systems is developed to ensure convergence and guarantee that the finite-time algorithm is Holder continuous. The developed tracking controllers for the muscles and electric motor and finite-time algorithm to compute the desired torque are implemented in real-time during cycling experiments in seven able-bodied individuals. Multiple cycling trials are implemented with different gain parameters of the finite-time torque algorithm to compare tracking performance for all participants. Chapter 6 highlights the contributions of the developed control methods and provides recommendations for future research extensions

A Survey of Lower Limb Rehabilitation Systems and Algorithms Based on Functional Electrical Stimulation

Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation. Currently, it is widely used in smart medical treatment for limb rehabilitation in stroke patients. In this paper, the development of FES systems is sorted out and analyzed in a time order. Then, the progress of functional electrical stimulation in the field of rehabilitation is reviewed in details in two aspects, i.e., system development and algorithm progress. In the system aspect, the development of the first FES control and stimulation system, the core of the lower limb-based neuroprosthesis system and the system based on brain-computer interface are introduced. The algorithm optimization for control strategy is introduced in the algorithm. Asynchronous stimulation to prolong the function time of the lower limbs and a method to improve the robustness of knee joint modeling using neural networks. Representative applications in each of these aspects have been investigated and analyzed

Control Methods for Compensation and Inhibition of Muscle Fatigue in Neuroprosthetic Devices

For individuals that suffer from paraplegia activities of daily life are greatly inhibited. With over 5,000 new cases of paraplegia each year in the United States alone there is a clear need to develop technologies to restore lower extremity function to these individuals. One method that has shown promise for restoring functional movement to paralyzed limbs is the use of functional electrical stimulation (FES), which is the application of electrical stimulation to produce a muscle contraction and create a functional movement. This technique has been shown to be able to restore numerous motor functions in persons with disability; however, the application of the electrical stimulation can cause rapid muscle fatigue, limiting the duration that these devices may be used. As an alternative some research has developed fully actuated orthoses to restore motor function via electric motors. These devices have been shown to be capable of achieving greater walking durations than FES systems; however, these systems can be significantly larger and heavier. To develop smaller and more efficient systems some research has explored hybrid neuroprostheses that use both FES and electric motors. However, these hybrid systems present new research challenges. In this dissertation novel control methods to compensate/inhibit muscle fatigue in neuroprosthetic and hybrid neuroprosthetic devices are developed. Some of these methods seek to compensate for the effects of fatigue by using fatigue dynamics in the control development or by minimizing the amount of stimulation used to produce a desired movement. Other control methods presented here seek to inhibit the effects of muscle fatigue by adding an electric motor as additional actuation. These control methods use either switching or cooperative control of FES and an electric motor to achieve longer durations of use than systems that strictly use FES. Finally, the necessity for the continued study of hybrid gait restoration systems is facilitated through simulations of walking with a hybrid neuroprosthesis. The results of these simulations demonstrate the potential for hybrid neuroprosthesis gait restoration devices to be more efficient and achieve greater walking durations than systems that use strictly FES or strictly electric motors

Study, definition and analysis of pilot/system performance measurements for planetary entry experiments

Definition analysis for experimental prediction of pilot performance during planetary entr

A Human Motor Control-Inspired Control System for a Walking Hybrid Neuroprosthesis

The purpose of this research is to develop a human motor control-inspired control system for a hybrid neuroprosthesis that combines functional electrical stimulation (FES) with electric motors. This device is intended to reproduce gait for persons with spinal cord injuries (SCI). Each year approximately 17,000 people suffer from an SCI in the U.S. alone, of which about 20% of them are diagnosed with complete paraplegia. Currently, there is a lot of interest in gait restoration for subjects with paraplegia but the existing technologies use either solely FES or electric motors. These two sources of actuation both have their own limitation when used alone. Recently, there have been efforts to provide a combination of the two means of actuation, FES and motors, into gait restoration devices called hybrid neuroprostheses. In this dissertation the derivation and experimental demonstration of control systems for the hybrid neuroprosthesis are presented. Particularly, the dissertation addresses technical challenges associated with the real-time control of a FES such as nonlinear muscle dynamics, actuator dynamics, muscle fatigue, and electromechanical delays (EMD). In addition, when FES is combined with electric motors in hybrid neuroprostheses, an actuator redundancy problem is introduced. To address the actuator redundancy issue, a synergy-based control framework is derived. This synergy-based framework is inspired from the concept of muscle synergies in human motor control theory. Dynamic postural synergies are developed and used in the feedforward path of the control system for the walking hybrid neuroprosthesis. To address muscle fatigue, the stimulation levels are gradually increased based on a model-based fatigue estimate. A dynamic surface control technique, modified with a delay compensation term, is used to address the actuator dynamics and EMD in the control derivation. A Lyapunov-based stability approach is used to derive the controllers and guarantee their stability. The outcome of this research is the development of a human motor control-inspired control framework for the hybrid neuroprosthesis where both FES and electric motors can be simultaneously coordinated to reproduce gait. Multiple experiments were conducted on both able-bodied subjects and persons with SCI to validate the derived controllers

Ultrasound imaging analysis for diagnostics of functional muscle status and evaluation of rehabilitation techniques in spinal cord injury (SCI)

The spinal cord connects the brain and the rest of the body through the transmission of sensory and motor signals. A spinal cord injury (SCI) disrupts this flow of information, resulting in loss of sensation and muscle paralysis. Although the muscles are not directly affected, a lack of activation leads to structural changes in the weeks and even years after the injury has occurred. These changes include muscle atrophy, an increased accumulation of intramuscular fat, and a fibre type transformation that makes muscles more susceptible to fatigue. In order to assess muscle function to provide an accurate prognosis and monitor recovery, quantitative and sensitive assessment methods are required. Current methods have limited sensitivity and are not able to differentiate between different muscles or assess deep muscles. Ultrasound imaging (USI) provides a potential additional tool to assess muscles. This noninvasive imaging technique uses the behaviour of sound waves travelling through tissue to allow internal structures such as muscles to be visualised. USI of skeletal muscle is a well-established technique, which can make accurate measurements of muscle size, and describe muscle activity through changes in architectural parameters. It has also been widely used as a screening tool in the diagnosis of neuromuscular disorders. In addition, USI could also improve understanding of muscle behaviour during the application of neuromuscular electrical stimulation (NMES), which is widely used in the rehabilitation of paralysed muscles. NMES uses low amplitude current to artificially generate a contraction, allowing exercise or functional movements to be achieved, however, it is limited by the early onset of muscle fatigue. The underlying mechanisms of fibre recruitment during NMES are unclear and USI has the potential to provide a greater insight. The main aim of this PhD project is to investigate the suitability of USI as a diagnostic tool for assessing muscles following a spinal cord injury, by establishing if it can differentiate between measurements of structure at different times post-injury, and correlate measurements of muscle movement with different levels of muscle function. Its suitability to monitor recovery is also investigated by establishing if it can detect changes in these measurements at different times post-injury. A secondary aim of the project is to demonstrate the potential of USI to provide insight into muscle behaviour during the application of NMES. The first study presented in this thesis involved recording USI videos of muscles in SCI patients and able-bodied controls under different conditions. USI videos of the muscle at rest provided measurements of muscle structure through the use of tracking software to measure the thickness of the muscle, and greyscale analysis to measure echogenicity and echotexture. USI videos of the muscle during attempted voluntary contractions provided measurements of muscle movement. Tracking software was used to measure muscle deformation and regional displacement, which were compared between groups of SCI participants with different levels of muscle function. A simpler analysis method based on the changes in pixel intensity values, referred to throughout this thesis as the pixel difference method, was also compared to the results obtained from the tracking software. Recordings were repeated at monthly intervals for the SCI participants, allowing these measurements of structure and function to be compared over time for individual participants. USI videos were also recorded during the application of NMES, allowing measurements of muscle movement to be compared between healthy muscles and those affected by a SCI. The second study involved recording USI videos of muscles in the lower limbs of acute SCI participants during attempted isotonic contractions, and using the pixel difference method to detect very small muscle movements. The USI measurements were compared to the results of a manual muscle test (MMT), a physical exam performed by a trained physiotherapist. Finally, a further two studies are presented where USI videos were recorded in able-bodied participants during the application of NMES and measurements of muscle movement were compared between different conditions. The first of these studies compared changes in stimulation parameters, as well as time-varying patterns of these parameters; and the second investigated the effect of spatially distributed patterns of stimulation using a multi-electrode configuration. USI measurements of muscle thickness, echogenicity and echotexture described changes in muscle structure after a SCI, and could differentiate between different times post-injury. USI measurements of muscle movement could also describe the functional status of muscles, with measurements of regional displacement found to be far more successful than measurements of deformation. Furthermore, the simpler pixel difference method provided similar results without the limitations of the tracking software. These measurements of muscle structure and function also showed changes over time for individual participants, highlighting the potential of USI to monitor recovery. The most successful results were seen in acute SCI patients, where the pixel difference method was able to detect very small muscle movements. Differences were also seen in USI measurements under different conditions of NMES, differentiating between different levels of stimulation intensity; different patterns of stimulation, as the result of variations in the stimulation parameters themselves and different spatially distributed patterns created through a multi-electrode configuration; finally, differences in muscle fibre recruitment and the amount of muscle movement produced during the application of NMES could be seen between healthy muscles and those affected by a SCI. In conclusion, USI has been shown to be a useful tool for the assessment of muscle structure and function following a SCI, and for monitoring recovery. Furthermore, it can also provide greater insight into muscle behaviour during the application of NMES, demonstrating its potential for optimising its use in rehabilitation

Tyr682 in the Aβ-precursor protein intracellular domain regulates synaptic connectivity, cholinergic function, and cognitive performance.

Processing of Aβ-precursor protein (APP) plays an important role in Alzheimer's disease (AD) pathogenesis. The APP intracellular domain contains residues important in regulating APP function and processing, in particular the 682YENPTY687 motif. To dissect the functions of this sequence in vivo, we created an APP knock-in allele mutating Y682 to Gly (APP(YG/YG) mice). This mutation alters the processing of APP and TrkA signaling and leads to postnatal lethality and neuromuscular synapse defects when expressed on an APP-like protein 2 KO background. This evidence prompted us to characterize further the APP(YG/YG) mice. Here, we show that APP(YG/YG) mice develop aging-dependent decline in cognitive and neuromuscular functions, a progressive reduction in dendritic spines, cholinergic tone, and TrkA levels in brain regions governing cognitive and motor functions. These data are consistent with our previous findings linking NGF and APP signaling and suggest a causal relationship between altered synaptic connectivity, cholinergic tone depression and TrkA signaling deficit, and cognitive and neuromuscular decline in APP(YG/YG) mice. The profound deficits caused by the Y682 mutation underscore the biological importance of APP and indicate that APP(YG/YG) are a valuable mouse model to study APP functions in physiological and pathological processes