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Volitional EMG Estimation Method during Functional Electrical Stimulation by Dual-Channel Surface EMGs

Joon-Young Jung, Dong-Woo Lee, Yong Ki Son, Bae Sun Kim, Hyung Cheol Shin

2021Sensors20 citationsDOIOpen Access PDF

Abstract

We propose a novel dual-channel electromyography (EMG) spatio-temporal differential (DESTD) method that can estimate volitional electromyography (vEMG) signals during time-varying functional electrical stimulation (FES). The proposed method uses two pairs of EMG signals from the same stimulated muscle to calculate the spatio-temporal difference between the signals. We performed an experimental study with five healthy participants to evaluate the vEMG signal estimation performance of the DESTD method and compare it with that of the conventional comb filter and Gram–Schmidt methods. The normalized root mean square error (NRMSE) values between the semi-simulated raw vEMG signal and vEMG signals which were estimated using the DESTD method and conventional methods, and the two-tailed t-test and analysis of variance were conducted. The results showed that under the stimulation of the gastrocnemius muscle with rapid and dynamically modulated stimulation intensity, the DESTD method had a lower NRMSE compared to the conventional methods (p < 0.01) for all stimulation intensities (maximum 5, 10, 15, and 20 mA). We demonstrated that the DESTD method could be applied to wearable EMG-controlled FES systems because it estimated vEMG signals more effectively compared to the conventional methods under dynamic FES conditions and removed unnecessary FES-induced EMG signals.

Topics & Concepts

ElectromyographyFunctional electrical stimulationSIGNAL (programming language)Root mean squareStimulationBiomedical engineeringComputer scienceFilter (signal processing)Channel (broadcasting)Pattern recognition (psychology)Physical medicine and rehabilitationArtificial intelligenceEngineeringMedicineComputer visionTelecommunicationsProgramming languageInternal medicineElectrical engineeringMuscle activation and electromyography studiesAdvanced Sensor and Energy Harvesting MaterialsEEG and Brain-Computer Interfaces