I can't see any use for this. By the time you're chronic insurance has stopped paying and I can't see this being affordable for anyone. And if it only reduces impairment rather than gets to 100% recovery it really is pie in the sky.
Function electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke
Journal of NeuroEngineering and Rehabilitation volume 9, Article number: 32 (2012)
Abstract
Background
Novel 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.
Methods
Five 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.
Results
From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.
Conclusions
The 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.
Background
Stroke is a leading cause of death and disability in the UK, and about 50% of people who survive a stroke require some form of rehabilitation to reduce impairment and assist with activities of daily living [1–3]. Upper limb function is particularly important in regaining independence following stroke; impairments impact on daily living and well-being [4, 5].
Research has consistently identified treatment intensity and goal oriented strategies as critical elements for successful therapeutic outcomes [6–10]. To further maximise rehabilitation effects, novel therapeutic and cost-effective rehabilitation interventions need to be developed and may combine different methodological techniques. For example, the combined use of electrical stimulation (ES), robot-aided therapy and virtual reality (VR) environments has been suggested to be particularly promising with respect to upper limb rehabilitation in chronic stroke [10, 11].
Following stroke, robot and ES therapies have been demonstrated to reduce upper limb motor impairments [6, 7, 10, 12–14]. Furthermore, these techniques have been highlighted as a way to facilitate the intensity of the training received [10], and allow training despite muscle weakness and without the aid of a therapist. In addition, when used with a real-time system which displays the participants’ arm and hand movements in a VR environment, the practiced movements can be very task-specific [11, 15]. These types of technologies may be more easily transferred into patients’ homes, increasing the intensity and task specificity of the training and reducing the time and expense constraints on therapists [16].
The therapeutic effect of ES in rehabilitation is known to increase when associated with a person’s voluntary effort [12]. However, a disadvantage of many ES approaches is that they fail to encourage voluntary contribution. In addition, the vast majority of upper limb stroke patient trials using ES employ open-loop or triggered controllers [12, 17], which can lead to imprecise control of movement. In the few cases that closed-loop control has been employed, a simplistic structure and lack of a model means accurate performance is still rarely achieved [18]. Employed mainly with spinal cord injury patients, one of the few advanced control methodologies used comprises artificial neural networks [19, 20]. However such model-free approaches have limited ability to adapt to changing physiological conditions, must be re-trained for use with different movements, and being of a “black-box” structure, do not permit stability and performance analysis.
The study reported in this paper investigates the feasibility and effectiveness of a novel 3D rehabilitation platform which combines robotic support, ES and VR. The system allows patients to receive the benefits of muscle-specific targeted ES within a tightly controlled, safe and motivating environment. In this platform, ES is mediated by iterative learning control (ILC), a technology transferred from industrial robotics which is applicable to systems which repeatedly perform a finite duration tracking operation [21]. After each repetition, ILC uses data gathered on previous executions of the task, often in combination with a model of the underlying system, to update the ES signal that will be applied on the subsequent trial. Hence ILC learns from previous experience the stimulation which maximises performance, and can effectively respond to changes in the model. ILC calculates the required control action in an optimal setting, allowing strict regulation of the amount of ES, its trial-to-trial variation, and the resulting movement error. Through use of appropriate weighting parameters a precise balance can be placed between encouraging voluntary effort and ensuring accurate movement [22, 23].
ILC is one of very few model-based upper limb ES control methodologies that has previously been used in a clinical study [24–26]. During this study, stroke participants attended 18 intervention sessions of 1 hour duration in which they practiced planar reaching tasks, tracking a moving spot of light. These movements were assisted by ILC mediated ES applied to the triceps of the impaired arm. Unassisted tracking performance (i.e., movements without the aid of ES) improved over the course of the intervention and changes in muscle activation patterns towards those of unimpaired participants were also observed [24, 25]. Whilst establishing the feasibility of advanced upper limb ES control approaches in the clinical domain, this planar system did not assist shoulder movement and by providing full mechanical support to the forearm, allowed very limited shoulder elevation.
To address these limitations and increase the potential of this novel approach to stroke rehabilitation, a new system has been developed to assist participants in performing more functional, 3D reaching tasks with ES applied to triceps and anterior deltoid muscles [22, 23]. Termed SAIL: Stimulation Assistance through Iterative Learning, this system comprises a commercial robotic arm support interfaced with custom-designed ES hardware and real-time ES control environment, together with a custom-made VR task display system (see Figure 1).
SAIL system components: 1) Hocoma ArmeoSpring® support, 2) surface electrodes on triceps brachii and anterior deltoid muscles, 3) realtime processor and interface module, 4) monitor displaying VR task, and 5) monitor displaying therapist user interface. 6) shows an example of a reaching task displayed to a stroke participant with left hemipshere damage. An image of their own arm is shown and they are encouraged to follow a sphere which moves along a reference path (the trajectory); in this case from bottom right to top left.
The commercial exoskeleton robot is a purely passive ‘un-weighing’ system which supports the patient’s arm against gravity via two springs incorporated into the mechanism. Each of its joints contains a resolver which records its angular position and this information is used by both the ES control system, and the VR task display. Whilst building on previous work, the ES controller incorporates substantial developments in terms of biomechanical modelling, identification, and control complexity compared with the planar system previously reported. In particular, a five degree-of-freedom biomechanical model of the combined human and robotic arm system was developed, along with identification procedures using kinetic, kinematic and ES input data which are suitable for patients [23, 27]. Then parallel feedback and feedforward controllers were derived using techniques from nonlinear optimisation to achieve robust tracking whilst maintaining strict trial-to-trial bounds on the change in input, and the patients’ arm dynamics occurring along each trial [22, 23, 28, 29]. Moreover, the muscle structures used in the model, identification procedure and controller have been specifically developed for application to stroke patients [27].
Preliminary tests to assess whether the ILC algorithms were accurately mediating the ES took place with unimpaired participants. Results confirmed that SAIL was effective in moving the arm to produce precise reaching movements, and that tracking performance improved over a series of trials see [22, 28, 29]. The aim of the study reported in this article was to assess the technological feasibility and rehabilitation effectiveness of the SAIL system with chronic stroke participants.
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