Mustafa Kutlu

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.

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.

Feedback control of electrical stimulation electrode arrays

Electrical stimulation electrode arrays are an emerging technology that enables muscles to be artificially contracted through the activation of their associated motor neurons. A principal application of electrical stimulation is to assist human motion for orthotic or therapeutic purposes. This paper develops a framework for the design of model-based electrode array feedback controllers that balance joint angle tracking performance with the degree of disturbance and modeling mismatch that can exist in the true underlying biomechanical system. This framework is used to develop a simplified control design procedure that is suitable for application in a clinical setting. Experimental results evaluate the feasibility of the control design approach through tests on ten participants using both fabric and polycarbonate electrode arrays.

Electrical stimulation and iterative learning control combined with real objects and simulated tasks to assist motor recovery in the upper extremity post-stroke

Evidence supports the combination of electrical stimulation (ES) and task specific training in rehabilitation of the upper extremity following stroke. The aim of this study is to develop a rehabilitation system that delivers precisely controlled levels of stimulation to the shoulder, elbow and wrist during goal-oriented activity utilising everyday real objects. Iterative learning control (ILC) is used to update 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 with a view to facilitating success at each given task. Markerless motion tracking is provided via a Microsoft Kinect, and a PrimeSense. Preliminary results show that ES mediated by ILC has successfully facilitated movement across the shoulder, elbow and wrist of chronic stroke patients. Overall, joint error has reduced for all participants with the mean error across all joints showing reductions for all participants. Furthermore, there was a significant reduction in extrinsic support necessary for each task. The system is described and initial intervention data are reported.

Iterative learning control as an enabler for goal oriented upper limb stroke rehabilitation

People re-learning skills after a stroke go through the same process as someone learning to play tennis, but they have a problem because they can hardly move at all so they cannot practise, which means they dont get feedback. Muscles can be made to work by electrical stimulation where electrical impulses travel along the nerves in much the same way as the electrical impulses from the brain. If stimulation is carefully controlled, a useful movement can be made. This works better if the person is attempting the movement themselves; we therefore need to combine a persons own effort with just enough extra electrical stimulation to achieve the movement. Previous research with supporting clinical trial data has shown that iterative learning control can be used to regulate the electrical stimulation applied with the essential requirement that if the patient is improving with each attempt the level of voluntary effort increase and the applied stimulation decreases. This paper reports results, including patient experimental data, where wrist and hand extensors are also stimulated using a 40 element surface electrode array and thereby moves closer to facilitating the re-learning of goal oriented tasks that are essential to move this technology towards home use.