Adaptive Stimulation Profiles Modulation for Foot Drop Correction Using Functional Electrical Stimulation: A Proof of Concept Study

Li, Yurong; Xu, Yuehua; Zhou, Yue-Zhu; Du, Min; Yang, Yuan

doi:10.1109/jbhi.2020.2989747

Cited by 20 publications

(11 citation statements)

References 18 publications

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“…rough the training of historical trajectory data, the trajectory prediction model is obtained, so that early warning can be achieved [1,2]. In terms of health care, the use of an LSTM network to independently detect the contraction curve of an ECG or EMG signal, based on the predicted signal, provides a powerful assistant for medical staff to quickly diagnose and help patients recover [3][4][5]. Adopting LSTM can achieve higher accuracy than traditional mathematical models in predicting future disease trends [6].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

Xiao

Song

2022

Computational Intelligence and Neuroscience

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Interference detection is an important part of the electronic defense system. It is difficult to detect interference with the traditional method of extracting characteristic parameters for interference generated at the same frequency as the original signal. Aiming at this special time-frequency overlapping interference signal, this paper proposes an interference detection algorithm based on the long short-term memory-support vector machines (LSTM-SVM) model. LSTM is used for the time series prediction of the received signal. The difference between the predicted signal and the received signal is used as the feature sample, and the SVM algorithm is used to classify the feature samples to obtain the recognition rate of whether the sample has interference. The LSTM-SVM model is compared with the gate recurrent unit-support vector machines (GRU-SVM) model, and the comparison results are visualized using a confusion matrix. The simulation results show that this LSTM-SVM model algorithm cannot only detect the existence of the interference signal but also can determine the specific position of the interference signal in the received waveform, and the detection performance is better than the GRU-SVM model.

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Section: Introductionmentioning

confidence: 99%

An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

Xiao

Song

2022

Computational Intelligence and Neuroscience

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“…In this paper, the modulation of the stimulus intensity is modulated by the EMG signal of a healthy person, so for the patient it is necessary to predict the EMG signal of a healthy person during gait. The FES modulation method in the literature [11] is used to set the maximum value of the predicted EMG signal as the tolerance value, the minimum value as the threshold value, and the remaining values of the EMG signal are mapped proportionally, so that the pulse width modulation based on the threshold and tolerance values is achieved.…”

Section: Emg Signal Prediction Model Based On Bp Neural Networkmentioning

confidence: 99%

“…The premise of predicting the EMG signal of healthy subjects by the angular velocity signal during walking of patients is to establish a mathematical model of the angular velocity signal and EMG signal of healthy subjects. The data used in this paper are the angular velocity signal of the lower leg and the TA EMG signals of seven healthy subjects collected in the literature [11] during normal walking on flat ground. In this paper, the generalization ability of the BP neural network is examined from both intra-and inter-individual perspectives.…”

Section: Generalization Ability Test Of Emg Signal Prediction Modelmentioning

confidence: 99%

“…Even a FSR with a Bluetooth transmission function will be limited in use occasions due to its integration with the insole. In the early stage of this research group, related research was carried out on the relationship between gait information and the TA EMG signal under natural gait [11], based on long short-term memory (LSTM) network, we tried to use angular velocity signal to predict EMG signal, and then realizes the self-adaptive adjustment of the electric stimulation intensity. We proved the feasibility of predicting the EMG signal from the angular velocity in principle.…”

Section: Introductionmentioning

confidence: 99%

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Research and Application of Functional Electrical Stimulation Control System Based on Adaptive EMG Modulation

Chen

Zheng

et al. 2021

Lecture Notes in Electrical Engineering

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Functional electrical stimulation (FES) stimulates the patient's tibials anterior (TA) to achieve foot dorsiflexion and assists the patient in walking. It is an effective way for foot drop correction. In order to realize the activation mode of tibialis anterior muscle similar to natural gait under electrical stimulation, this paper proposed a natural activation electromyography (EMG) prediction algorithm based on dynamic BP neural network, which uses angular velocity of gait to predict EMG signal in real time, and then modulate FES output intensity. Based on STM32F407 microcontroller, a wearable self-adaptive EMG modulation functional electrical stimulation therapy device was developed. Seven healthy people were recruited for establishment of EMG prediction model, and a foot drop patient was recruited for an FES gait experiment. The electrical evoked muscle response produced by FES was compared with the natural activation of healthy subjects under natural gait. The correlation coefficient between the two reaches 0.7490 ± 0.1185, which is higher than the traditional trapezoidal profiles stimulation method (0.5318 ± 0.1418). The results show that the control system is more capable of assisting patients to walk in a near-natural gait.

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“…Besides that, the FES-assisted exercise has also gained interest among clinicians, engineers and researchers. The development of such systems is still on-going, with new innovations and techniques being invented [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model

et al. 2021

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Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

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Adaptive Stimulation Profiles Modulation for Foot Drop Correction Using Functional Electrical Stimulation: A Proof of Concept Study

Cited by 20 publications

References 18 publications

An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

Research and Application of Functional Electrical Stimulation Control System Based on Adaptive EMG Modulation

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model

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