Designing an FES Control Algorithm: Important Considerations

Braz, Gustavo P.; Smith, Richard M.; Davis, Glen M.

doi:10.1007/978-3-540-36841-0_720

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Artificial neural networks have been applied to both the upper (Lan, Feng, & Crago, 1994;Tresadern, Thies, Kenney, Howard, & Goulermas, 2006) and lower limbs (Graupe & Kordylewski, 1997) of paretic subjects, although disadvantages to this last approach have been reported (Braz, Smith, & Davis, 2006).…”

Section: Introductionmentioning

confidence: 99%

Identification of electrically stimulated muscle models of stroke patients

Markovsky

Freeman

et al. 2010

Control Engineering Practice

109

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a b s t r a c tDespite significant recent interest in the identification of electrically stimulated muscle models, current methods are based on underlying models and identification techniques that make them unsuitable for use with subjects who have incomplete paralysis. One consequence of this is that very few model-based controllers have been used in clinical trials. Motivated by one case where a model-based controller has been applied to the upper limb of stroke patients, and the modelling limitations that were encountered, this paper first undertakes a review of existing modelling techniques with particular emphasis on their limitations. A Hammerstein structure, already known in this area, is then selected, and a suitable identification procedure and set of excitation inputs are developed to address these short-comings. The technique that is proposed to obtain the model parameters from measured data is a combination of two iterative schemes: the first of these has rapid convergence and is based on alternating least squares, and the second is a more complex method to further improve accuracy. Finally, experimental results are used to assess the efficacy of the overall approach.

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Section: Introductionmentioning

confidence: 99%

Identification of electrically stimulated muscle models of stroke patients

Markovsky

Freeman

et al. 2010

Control Engineering Practice

109

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“…Examples include optimal [17], H` [18], and fuzzy [19] control of standing, sliding mode control of shank movement [20], data-driven control [21] of the knee joint, and multichannel proportional integral derivative (PID) control of the wrist [22]. Artificial neural networks have been applied to both the upper [23], [24] and lower limbs [25] of paretic subjects, although disadvantages to the approach have been reported [26].…”

Section: E E E P R O O Fmentioning

confidence: 99%

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

et al. 2012

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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

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A Comparison of Closed-Loop Control Algorithms for Regulating Electrically Stimulated Knee Movements in Individuals With Spinal Cord Injury

Lynch

Popović

2012

IEEE Trans. Neural Syst. Rehabil. Eng.

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Functional electrical stimulation (FES) is the most commonly used technology for improving motor function in individuals who have spinal cord injury. Despite the wide range of FES applications reported in the literature, few electrical stimulation systems that can generate meaningful functional outcomes are currently available for use outside research laboratories. We tested proportional-integral-derivative, gain scheduling, and sliding mode control closed-loop control algorithms in a simulation of electrically induced knee extension against gravity to uncover some of the reasons why closed-loop control is not being more widely used in real-world FES systems. We also subjected the simulated FES system to muscle fatigue, muscle spasms, and the effects of muscle retraining. All of the controllers exhibited significantly degraded performance when these real-world nonlinear effects were included in the simulation. Moreover, all of the controllers were sensitive to variation in the parameters of the muscle recruitment function, which are subject to change during real-world FES use. We suggest several ways to improve the performance of closed-loop control algorithms for use in FES applications. We believe that closed-loop controllers have an important place in future FES applications, but the performance of these algorithms must be greatly improved before they can be implemented in real-world systems.

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Designing an FES Control Algorithm: Important Considerations

Cited by 3 publications

References 19 publications

Identification of electrically stimulated muscle models of stroke patients

Identification of electrically stimulated muscle models of stroke patients

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

A Comparison of Closed-Loop Control Algorithms for Regulating Electrically Stimulated Knee Movements in Individuals With Spinal Cord Injury

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