Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation

Abstract

A nnually, 15 million people worldwide suffer a stroke, and 5 million are left permanently disabled. A stroke is usually caused when a blood clot blocks a vessel in the brain and acts like a dam, stopping the blood reaching the regions downstream. Alternatively, it may be caused by a hemorrhage, in which a vessel ruptures and leaks blood into surrounding areas. As a result, some of the connecting nerve cells die, and the person commonly suffers partial paralysis on one side of the body, termed hemiplegia. Cells killed in this way cannot regrow, but the brain has some spare capacity and, hence, new connections can be made. The brain is continually and rapidly changing as new skills are learned, new connections are formed, and redundant ones disappear. A person who relearns skills after a stroke goes through the same process as someone learning to play tennis or a baby learning to walk, requiring sensory feedback during the repeated practice of a task. Unfortunately, the problem is that they can hardly move and, therefore, do not receive feedback on their performance.Stroke survivors often have a complex pattern of upperlimb motor impairments, resulting in a loss of functional abilities such as reaching. The relationship between reaching and independence is reflected in measures of functional independence, such as the Barthel index [1] where the ability to reach is required for over half of the activity of daily living tasks. The current prognosis for upper-limb recovery following a stroke is poor, with a review reporting that complete recovery occurs in less than 15% of patients with initial paralysis [2]. Stroke is also an age-related disease [3], placing an increasing burden on long-term health

Iterative learning control with applications in energy generation, lasers and health care

“…Details of how this was achieved and the roles of the features marked as (6) and (7) in fig. 14 are given in [44].…”

Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study

“…The main advantage of this approach is the simplicity of the setup, with only 1 Degree of Freedom (DoF). However, to maximize the treatment’s outcomes and achieve functional improvement it is necessary to train actions with higher range of motion (> 1 DoF) and functional connotations [8, 9]. Yet, the complexity for driving a successful movement execution in such scenarios requires the implementation of a robust and reliable FES controller.…”

“…Consequently, open-loop and simple feedback controllers (e.g. proportional-integral-derivative -PID-) are not robust enough to cope with these disturbances [8, 12]. Meadmore et al presented a more suitable hybrid robotic system for functional rehabilitation scenarios [13].…”

Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation

“…Compared to the results presented in Freeman et al (2012), no learning phase was required to achieve the desired functional movements. Overall, the evaluation shows that it is possible to support the user in performing the drinking task.…”

“…This would require a long lasting calibration procedure infeasible in clinical environments or at home. For the use of NMES in stroke rehabilitation, Iterative Learning Control (ILC) has been proposed in order to generate precise functional reaching movements (Freeman et al, 2012). ILC demands a cyclic movement generation.…”