Muscle Fatigue and the Importance of Electrical Stimulation Parameters on Functional Electrical Stimulation

Moreira, Marina; Bó, Antônio Padilha Lanari

doi:10.1007/978-981-13-2119-1_47

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Namely, adjusting the frequency of electrical stimulation can effectively help patients to gain muscle force in a short time [23], [24]. Although using a too-high frequency can accelerate muscle fatigue [22], it has been documented that increasing the frequency of electrical stimulation contributes to muscle force more than increasing the pulse amplitude and pulse width [24]. Therefore, in this study, the frequency is changed to provide electrical stimulation to patients to improve their muscle force output.…”

Section: B Electrical Stimulation Output Subsystemmentioning

confidence: 98%

Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation

Lu,

Ge,

Tang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the R 2 value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications.

show abstract