The University of Southampton

Surface Electrode Array-based Electrical Stimulation and Iterative Learning Control for Hand Rehabilitation


A normal functioning hand is one of the most important features for human independence. A complex neuromusculoskeletal structure of the human hand consists of many dedicated subsystems cooperated in a highly organised manner to form a powerful and precise device. The malfunction of any of its elements may result in disability and hand functional impairment. There exist many injuries that can result in the loss of hand function, such as i.e. stroke, spinal cord injuries and complications after hand surgery or hand traumatic injury such as i.e. tendon ruptures.

Surface electrode array stimulation is a non-invasive method of muscle activation applied via adhesive electrode arrays placed on the surface of the patient skin above the location of the desired muscles. Surface electrode array stimulation is able to induce movements in paralysed or weak limb, by delivering a series of electrical pulses to associated skeletal muscles through activation of chosen elements of electrode array. Surface electrode array stimulation is a promising technique for stroke rehabilitation of the wrist and fingers, due to its increased muscle selectivity. However, the effectiveness of the method is strongly related to the precision and accuracy of the stimulation. Locating the optimal stimulation sites via selection of the appripriate elements of the electrode array is critical to the effective application of this rehabilitation approach.

This project addresses the use of surface electrode arrays to regulate the stimulation applied to the extensor muscles of the hand and wrist in order to induce hand movement to desired posture. A general control strategy developed in this project embeds optimisation methods for selection of appropriate elements of the electrode array with iterative learning control.

In iterative learning control, the patient makes repeated attempts to complete a predefined task with the aim of gradually decreasing the error between the movement performed and desired one. A number of different gradient-based methods, such as penalty method and sparse optimisation methods has been developed and tested based on theoretical and experimental findings. These methods are used to find a sparse input vector, which is employed to select only those array elements that are critical to task completion within iterative learning control framework.

The developed methods, are presented in the context of the complete and novel design of the Hand Rehabilitation System (HaReS) to provide both, theoretical and practical indications for further expantion of this rehabilitation technology and future research. The system comprises the ILC-based control algorithms for electrode array stimulation with a game-based training environment that provides feedback to the patient.

Primary investigators

  • Anna Soska
  • Eric Rogers
  • Christopher Freeman

Associated research groups

  • Electronics and Electrical Engineering
  • Electronics and Electrical Engineering
  • Electronics and Electrical Engineering
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