Identification of electrically stimulated muscle models of stroke patients

Le, Fengmin; Markovsky, Ivan; Freeman, Christopher; Rogers, Eric

doi:10.1016/j.conengprac.2009.12.007

Cited by 109 publications

(65 citation statements)

References 63 publications

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“…Since h IRC,f cr (u) and h IRC,ecr (u) can be represented as polynomial functions [14], the continuous compound function h IRC (u) can be represented by the form…”

Section: Mathematical Representation Of System Identification Problemmentioning

confidence: 99%

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System identification for FES-based tremor suppression

Copur

Freeman

Chu

et al. 2016

European Journal of Control

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Tremor is an involuntary motion which is a common complication of Parkinson's disease and Multiple Sclerosis. A promising treatment is to artificially contract the muscle through application of induced electrical stimulation. However, existing controllers have either provided only modest levels of suppression or have been applied only in simulation. To enable more advanced, model-based control schemes, an accurate model of the relevant limb dynamics is required, together with identification procedures that are suitable for clinical application. This paper proposes such a solution, explicitly addressing limitations of existing methodologies. These include model structures that: (i) neglect critical features, and (ii) restrict the range of admissible control schemes, together with identification procedures that: (iii) employ stimulation inputs that are uncomfortable for patients, (iv) are overly complex and time-consuming for clinical use, and (v) cannot be automated. Experimental results confirm the efficacy of the proposed identification procedures, and show that high levels of accuracy can be achieved in a short identification time using test procedures that are suitable for future transference to the clinical domain.

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“…Since h IRC,f cr (u) and h IRC,ecr (u) can be represented as polynomial functions [14], the continuous compound function h IRC (u) can be represented by the form…”

Section: Mathematical Representation Of System Identification Problemmentioning

confidence: 99%

“…Given an estimateθ n of nonlinear parameter vector θ n , cost function (14) can be minimized with respect to the linear parameter vector. This linear least squares minimization problem is given bŷ…”

Section: Linear Parameter Identificationmentioning

confidence: 99%

System identification for FES-based tremor suppression

Copur

Freeman

Chu

et al. 2016

European Journal of Control

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“…Consult Lynch & Popovic (2008) for comparative discussion, with supporting references, of the various model structures currently available for this area. More recent results on the identification of a model for muscle response to FES are given in Le et al (2010Le et al ( , 2012.…”

Section: Muscle Models and Fatiguementioning

confidence: 99%

Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue

Wang

Chu

Rogers

2014

Control Engineering Practice

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The use of iterative learning control to regulate assistive functional electrical stimulation applied to the muscles of patients undergoing robotic-assisted upper limb stroke rehabilitation has been followed through to small scale clinical trials. These trials confirmed that an increase in patient ability to complete the specified task also led to a reduction in the level of electrical stimulation required. This previous work assumed that the effects of muscle fatigue could be neglected but if a patient suffers fatigue during a rehabilitation session then their the session goals are not achieved or, more likely, the session must be abandoned due to the time limits imposed by the ethical approval required to conduct such sessions. In this paper the results of the first investigation into enhancing the control scheme to remove or lessen the effects of fatigue and hence make better use of the time available for a session are given. The scheme considered adds a feedback loop around the muscle model used, where the performance results given are based on a model for the dynamics constructed using patient data collected in previous clinical trials.

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“…The latter are considered in this section and a robotic-based rehabilitation system is described from design through to clinical trial results [33]- [35], [39]- [41], given in the next section, where model-based ILC is used to control the level of FES applied.…”

Section: Ilc-based Stroke Rehabilitation For Planar Tasksmentioning

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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Identification of electrically stimulated muscle models of stroke patients

Cited by 109 publications

References 63 publications

System identification for FES-based tremor suppression

System identification for FES-based tremor suppression

Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue

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

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