Katie Meadmore

Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation

Annually, 15 million people worldwide suffer a stroke, and 5 million are left permanently disabled. A stroke is usually caused when a blood clot blocks a vessel in the brain and acts like a dam, stopping the blood reaching the regions downstream. Alternatively, it may be caused by a hemorrhage, in which a vessel ruptures and leaks blood into surrounding areas. As a result, some of the connecting nerve cells die, and the person commonly suffers partial paralysis on one side of the body, termed hemiplegia. Cells killed in this way cannot regrow, but the brain has some spare capacity and, hence, new connections can be made. The brain is continually and rapidly changing as new skills are learned, new connections are formed, and redundant ones disappear. A person who relearns skills after a stroke goes through the same process as someone learning to play tennis or a baby learning to walk, requiring sensory feedback during the repeated practice of a task. Unfortunately, the problem is that they can hardly move and, therefore, do not receive feedback on their performance.

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

BackgroundNovel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort.MethodsFive hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression.ResultsFrom baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.ConclusionsThe concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study

BackgroundFunctional electrical stimulation (FES) during repetitive practice of everyday tasks can facilitate recovery of upper limb function following stroke. Reduction in impairment is strongly associated with how closely FES assists performance, with advanced iterative learning control (ILC) technology providing precise upper-limb assistance. The aim of this study is to investigate the feasibility of extending ILC technology to control FES of three muscle groups in the upper limb to facilitate functional motor recovery post-stroke.MethodsFive stroke participants with established hemiplegia undertook eighteen intervention sessions, each of one hour duration. During each session FES was applied to the anterior deltoid, triceps, and wrist/finger extensors to assist performance of functional tasks with real-objects, including closing a drawer and pressing a light switch. Advanced model-based ILC controllers used kinematic data from previous attempts at each task to update the FES applied to each muscle on the subsequent trial. This produced stimulation profiles that facilitated accurate completion of each task while encouraging voluntary effort by the participant. Kinematic data were collected using a Microsoft Kinect, and mechanical arm support was provided by a SaeboMAS. Participants completed Fugl-Meyer and Action Research Arm Test clinical assessments pre- and post-intervention, as well as FES-unassisted tasks during each intervention session.ResultsFugl-Meyer and Action Research Arm Test scores both significantly improved from pre- to post-intervention by 4.4 points. Improvements were also found in FES-unassisted performance, and the amount of arm support required to successfully perform the tasks was reduced.ConclusionsThis feasibility study indicates that technology comprising low-cost hardware fused with advanced FES controllers accurately assists upper limb movement and may reduce upper limb impairments following stroke.

Phase-lead iterative learning control algorithms for functional electrical stimulation-based stroke rehabilitation

A control system for stroke rehabilitation is developed which combines electrical stimulation with a robotic support system to provide assistance to stroke patients performing three-dimensional upper-limb reaching tasks in a virtual reality environment. The electrical stimulation is applied to two muscles in the subject’s arm using an iterative learning control scheme which learns from data collected over previous trials of the task in order to achieve accurate movement. The principal components of the system are described and experimental results confirm its feasibility for application to upper-limb stroke rehabilitation.

Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis

Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

FES based rehabilitation of the upper limb using input/output linearization and ILC

To provide effective stroke rehabilitation, a control scheme is developed for upper arm tracking in 3D space using electrical stimulation. In accordance with clinical need, the case where stimulation is applied to two muscles in the arm and shoulder is considered, with the arm supported against gravity by an exoskeletal mechanism. An upper limb model with five degrees of freedom is first developed to represent the unconstrained upper arm, and an input/output linearization controller is applied to decouple the actuated joint angles, and combined with a state-feedback optimal tracking controller. Linear iterative learning controllers are then designed to enforce precise tracking over repeated attempts at the task, and stability conditions for the unactuated joint angles are given. Experimental results confirm practical performance.

Goal orientated stroke rehabilitation utilising electrical stimulation, iterative learning and Microsoft Kinect

An upper-limb stroke rehabilitation system is developed that assists patients in performing real world functionally relevant reaching tasks. The system provides de-weighting of the arm via a simple spring support whilst functional electrical stimulation is applied to the anterior deltoid and triceps via surface electrodes, and to the wrist and hand extensors via a 40 element surface electrode array. Iterative learning control (ILC) is used to mediate the electrical stimulation, and updates the stimulation signal applied to each muscle group based on the error between the ideal and actual movement in the previous attempt. The control system applies the minimum amount of stimulation required, maximising voluntary effort. Low-cost, markerless motion tracking is provided via a Microsoft Kinect, with hand and wrist data provided by an electrogoniometer or data glove. The system is described and initial experimental results are presented for a stroke patient starting treatment.

Design & control of a 3D stroke rehabilitation platform

An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation.

Computational models of upper limb motion during functional reaching tasks for application in FES based stroke rehabilitation

Abstract Functional electrical stimulation (FES) has been shown to be an effective approach to upper-limb stroke rehabilitation, where it is used to assist arm and shoulder motion. Model-based FES controllers have recently confirmed significant potential to improve accuracy of functional reaching tasks, but they typically require a reference trajectory to track. Few upper-limb FES control schemes embed a computational model of the task; however, this is critical to ensure the controller reinforces the intended movement with high accuracy. This paper derives computational motor control models of functional tasks that can be directly embedded in real-time FES control schemes, removing the need for a predefined reference trajectory. Dynamic models of the electrically stimulated arm are first derived, and constrained optimisation problems are formulated to encapsulate common activities of daily living. These are solved using iterative algorithms, and results are compared with kinematic data from 12 subjects and found to fit closely (mean fitting between 63.2% and 84.0%). The optimisation is performed iteratively using kinematic variables and hence can be transformed into an iterative learning control algorithm by replacing simulation signals with experimental data. The approach is therefore capable of controlling FES in real time to assist tasks in a manner corresponding to unimpaired natural movement. By ensuring that assistance is aligned with voluntary intention, the controller hence maximises the potential effectiveness of future stroke rehabilitation trials.

Lateralisation of spatial processing and age

Studies assessing spatial ability suggest right hemisphere specialisation for coordinate spatial processing and left hemisphere specialisation for categorical spatial processing. With regard to healthy ageing, spatial abilities may be affected selectively, with right hemisphere based coordinate processes being more vulnerable to age-related decline, but previous research has been inconsistent. In the present study, age and hemispheric specialisation of categorical and coordinate spatial abilities were explored. Testing 56 right-handed younger and older participants clearly showed a left hemisphere advantage for the categorical task and a right hemisphere advantage for the coordinate spatial task, for both age groups combined. Older adults were slower to process information and make a spatial judgement; nevertheless, the neural specialisation underlying spatial abilities seems to have remained consistent with age.