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

BACKGROUND: Functional 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. METHODS: Five 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. RESULTS: Fugl-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. CONCLUSIONS: This 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.

A hybrid orthosis combining functional electrical stimulation and soft robotics for improved assistance of drop-foot.

Drop-foot is characterised by an inability to lift the foot, and affects an estimated 3 million people worldwide. Current treatment methods include rigid splints, electromechanical systems, and functional electrical stimulation (FES). However, these all have limitations, with electromechanical systems being bulky and FES leading to muscle fatigue. This paper addresses the limitations with current treatments by developing a novel orthosis combining FES with a pneumatic artificial muscle (PAM). It is the first system to combine FES and soft robotics for application to the lower limb, as well as the first to employ a model of their interaction within the control scheme. The system embeds a hybrid controller based on model predictive control (MPC), which combines FES and PAM components to optimally balance gait cycle tracking, fatigue reduction and pressure demands. Model parameters are found using a clinically feasible model identification procedure. Experimental evaluation using the system with three healthy subjects demonstrated a reduction in fatigue compared with the case of only using FES, which is supported by numerical simulation results.

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

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.

Stroke participants' perceptions of robotic and electrical stimulation therapy: a new approach.

PURPOSE: User perceptions are critical, yet often ignored factors in the design and development of rehabilitation technologies. In this article, measures for collection of patient perceptions are developed and applied to a novel upper limb workstation that combines robotic therapy and electrical stimulation (ES). METHOD: Five participants with chronic upper limb hemiplegia post-stroke used a robotic workstation to undertake supported tracking tasks augmented by precisely controlled ES to their triceps muscle. Following a 6 week trial, a purpose designed set of questions was developed and individual interviews were conducted by an independent health psychologist. RESULTS: The simple, quick to administer question set showed that participants had a positive response to the system, and contributed valuable feedback with regard to its usability and effectiveness. Participants want a home-based system targeting their whole arm. CONCLUSION: This article demonstrates the value in assessing user perceptions of a rehabilitation system via a simple question set. While the results of this study have implications for a wider audience, our recommendations are for a qualitative study to develop a generic evaluation tool which could be used across the growing number of devices to provide feedback to enhance future development of any new technology for rehabilitation.

Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation.

A model of the upper extremity using FES for stroke rehabilitation.

A model of the upper extremity is developed in which the forearm is constrained to lie in a horizontal plane and electrical stimulation is applied to the triceps muscle. Identification procedures are described to estimate the unknown parameters using tests that can be performed in a short period of time. Examples of identified parameters obtained experimentally are presented for both stroke patients and unimpaired subjects. A discussion concerning the identification's repeatability, together with results confirming the accuracy of the overall representation, is given. The model has been used during clinical trials in which electrical stimulation is applied to the triceps muscle of a number of stroke patients for the purpose of improving both their performance at reaching tasks and their level of voluntary control over their impaired arm. Its purpose in this context is threefold: Firstly, changes occurring in the levels of stiffness and spasticity in each subject's arm can be monitored by comparing frictional components of models identified at different times during treatment. Secondly, the model is used to calculate the moments applied during tracking tasks that are due to a patient's voluntary effort, and it therefore constitutes a useful tool with which to analyze their performance. Thirdly, the model is used to derive the advanced controllers that govern the level of stimulation applied to subjects over the course of the treatment. Details are provided to show how the model is applied in each case, and sample results are shown.