Switched Controller Applied to Functional Electrical Stimulation of Lower Limbs Under Fatigue Conditions: A Linear Analysis

Nunes, Willian Ricardo Bispo Murbak; Teodoro, Ricardo Gouveia; Sanches, Marcelo Augusto Assunção; Araújo, Rafael Gavassa de; Teixeira, Marcelo Carvalho Minhoto; Carvalho, Aparecido Augusto de

doi:10.1007/978-981-13-2119-1_59

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…It has the capacity to supply currents with amplitudes of up to 140 mA, with a 1-mA step, pulse width (PW) from 0 to 500 μs, with a step of 1 μs, and frequency (F) from 20 to 300 Hz, with a step of 1 Hz. The stimulator has a currentbased output allowing its use in closed-loop control (Arcolezi et al 2019Nunes et al 2019;Gaino et al 2020;Teodoro et al 2020). The output stage of this stimulator is current, which is a great advantage for its use in control, as it makes it possible to control and predict the amount of current applied to the muscle, even if the coupling resistance and tissue impedance change.…”

Section: Test Platformmentioning

confidence: 99%

“…Note that we could adopt other values of lower limb extension, but we consider that it was a suitable value for gait control application (Nunes et al 2019). Figure 5 shows the angular position corresponding to the pulse width assessment test for patient H1.…”

Section: Stimulation Protocol For Data Acquisitionmentioning

confidence: 99%

“…Applying the same pulse width value (from the evaluation test), different lower limb extension positions, can be obtained and not belonging to the set Θ = {θ min , θ max }. This uncertainty is due to the dynamic system being nonlinear and time-varying (Nunes et al 2019).…”

Section: Stimulation Protocol For Data Acquisitionmentioning

confidence: 99%

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Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks

Arcolezi¹,

Nunes²,

Cerna³

et al. 2020

Res. Biomed. Eng.

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Purpose Recurrent neural networks (RNNs) offer a promising opportunity for identifying nonlinear systems. This paper investigates the effectiveness of the long short-term memory (LSTM) RNN architecture in the specific task of identifying the knee joint angular position under neuromuscular electrical stimulation (NMES). The standard RNN model referred to as SimpleRNN and the well-known multilayer perceptron (MLP) are used for comparison purposes. Methods Data from seven healthy and two paraplegic volunteers were experimentally acquired. These data were adequately scaled, encoded using three timestep values (1, 5, and 10), and divided into training, validation, and testing sets. These models were mainly evaluated using the root mean square error (RMSE) and training time metrics. Results The three NN models demonstrated very good fitting to data for all volunteers. The LSTM presented smaller RMSE for most of the individuals. This is even more notable when using 5 and 10 timesteps achieving half and one-third of the error from MLP and half of the error from the SimpleRNN. This higher utility comes with a substantial time-utility trade-off. Conclusion The results in this paper show that the LSTM worths deeper investigation to design control-oriented models to knee joint stimulation in closed-loop systems. Even though the LSTM takes more time for training due to a more complex architecture, time and computational costs could be increased if achieving better modeling of systems. Rather than mathematically modeling this system with several unique parameters per individual, the use of NNs is encouraged in this task where there exist high nonlinearities and time-varying parameters.

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Section: Test Platformmentioning

confidence: 99%

Section: Stimulation Protocol For Data Acquisitionmentioning

confidence: 99%

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Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks

Arcolezi¹,

Nunes²,

Cerna³

et al. 2020

Res. Biomed. Eng.

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“…In this study, seven healthy individuals (male, aged 22-28) labeled as H1-H7 and two male SCI individuals labeled as P1 and P2 participated in the experiments. values of lower limb extension, but we consider that it was a suitable value for gait control application (Nunes et al (2019)).…”

Section: Analyzed Individualsmentioning

confidence: 99%

“…Even though there are several investigations on closed-loop control of FES/NMES systems for lower limb rehabilitation (c.f. Ferrarin et al (2001); Previdi & Carpanzano (2003); Jezernik et al (2004); Cheng et al (2016); Mohammed et al (2012); Wu et al (2017); Gaino et al (2017); dos Santos et al (2015); Kirsch et al (2017); Nunes et al (2019) and the references within), these systems are hardly put into production. Alternatively, there exist commercial stimulators normally available on open-loop designs and with pre-programmed electrical stimulus, which are not enough to deal with the nonlinear and timevarying nature of muscles.…”

Section: Introductionmentioning

confidence: 99%

RISE Controller Tuning and System Identification Through Machine Learning for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation

Arcolezi,

Nunes,

de Araujo

et al. 2020

Preprint

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Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of spinal cord injured (SCI) individuals. In this context, we introduce a novel robust and intelligent control-based methodology to closed-loop NMES systems. Our approach uses a control law to guarantee the system's stability. And, machine learning tools for both optimizing the controller parameters and system identification, with the novelty of using past rehabilitation data. In this paper, we apply the proposed methodology to the rehabilitation of lower limbs using a control technique namely robust integral of the sign of the error (RISE), an off-line improved genetic algorithm optimizer, and neural network models. Although in the literature the RISE controller presented good results on healthy subjects without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue for SCI individuals.

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A Survey on Modeling Mechanism and Control Strategy of Rehabilitation Robots: Recent Trends, Current Challenges, and Future Developments

Zhu

Tong

Rui

et al. 2022

Int. J. Control Autom. Syst.

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Switched Controller Applied to Functional Electrical Stimulation of Lower Limbs Under Fatigue Conditions: A Linear Analysis

Cited by 4 publications

References 14 publications

Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks

Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks

RISE Controller Tuning and System Identification Through Machine Learning for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation

A Survey on Modeling Mechanism and Control Strategy of Rehabilitation Robots: Recent Trends, Current Challenges, and Future Developments

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