Iterative learning control of FES applied to the upper extremity for rehabilitation

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

“…Using this control structure, voluntary movement by the patient can be treated as iteration-invariant disturbance and can be compensated for [21]. A robust ILC scheme can deal with dynamic changes and model inaccuracy due to fatigue, spasticity and other physiological effects [6]. The phase-lead ILC algorithm has the form…”

“…Evolving from the previous systems that used planar light tracking [6] or virtual reality 3D object tracking tasks [8], this system uses functionally relevant real world tasks. The tasks include: (1) switching a low light switch (shoulder height), (2) switching a high light switch (head height), (3) closing a drawer (shoulder height), (4) stabilising an object on a table with the affected arm whilst manipulating the object with the unaffected arm, (5) repositioning a drinksized object on a table, (6) pressing buttons positioned on a table.…”

“…Previous studies combining ILC and FES for upper limb rehabilitation have used planar light tracking tasks [6] or virtual reality 3D object tracking [8], incorporating explicit reference trajectories for the patient to follow. To directly target activities of daily living, the current system assists real-world tasks that require manipulation of objects using the hand and arm.…”

“…The data collected throughout each repetition of the movement is then used to update the control input signal for the next execution with the objective of increasing the accuracy of the movement. Recent work has successfully used ILC to regulate the timing and intensity of FES applied to stroke patient's muscles whilst performing planar [6], [7] and 3D [8], [9], [10] reaching tasks. These previous studies reported significant improvements in both performance accuracy and clinical outcome measures of movement for stroke patients following clinical trials, demonstrating the potential of ILC and FES during rehabilitation.…”

Iterative learning control method for improving the effectiveness of upper limb rehabilitation

“…Section IV gives conclusions of this work. Fig 1 shows the ILC Control Structure [23].UP represents the upper limb robot; C is the feedback of the controller, and L is the feed-forward of the controllers; MEM is the memory for the system; the control law Learning algorithm convergence analysis is very important for ILC. An adequate condition called spectral radius method is a good way to measure whether proposed update control law could guarantee robustness and convergence.…”