Wearable Active Electrode for sEMG Monitoring Using Two-Channel Brass Dry Electrodes with Reduced Electronics

Abstract

Gel-based electrodes are employed to record sEMG signals for prolonged periods. These signals are used for the control of myoelectric prostheses, clinical analysis, or sports medicine. However, when the gel dries, the electrode-skin impedance increases considerably. Using dry active electrodes (AEs) to compensate variations of impedance is an alternative for long-term recording. This work describes the optimization of the electronic design of a conventional AE by removing the impedance coupling stage and two filters. The proposed work consisted of 5 stages: electrodes, amplification (X250), 2.2 Vdc offset, low-pass filter, and ADC with USART communication. The device did not need the use of electrolytic gel. The measurements of CMRR (96 dB), amplitude of the output sEMG signal (∼1.6 Vp-p), and system bandwidth (15–450 Hz) were performed in order to confirm the reliability of the device as an sEMG signal acquisition system. The SNR values from seven movements performed by eleven volunteers were compared in order to measure the repeatability of the measurements (average 30.32 dB for a wrist flexion). The SNR for wrist flexion measured with the proposed and the commercial system was compared; the values were 49 dB and 60 dB, respectively.

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

“…The use of LIF neurons and local spike-driven plasticity rules opens up the possibility of implementing such architecture on neuromorphic chips, leading to an even more efficient online and wearable implementation. We considered muscles in the forearm (extrinsic), to target the use of myoelectric armbands and bracelets for monitoring users' activity [37,38], and in the hand (intrinsic), so far considered mainly in neurophysiology [36,39,40].…”

Active Surface Electromyogram (sEMG) Electrodes band with Enhanced Features based technique for Hand Gesture Classification

“…1 (c) and 1 (d). The signal to noise (SNR) was calculated by separating the signal in two composing region energy at contraction and energy at stable region [23]. Additionally, pre-processing is performed on each EMG channel to improve the signal quality and eliminate any unwanted noise or artifacts.…”

Comparison of two Wireless Electromyography Sensor Module Designs using wet electrodes and dry electrodes at the time of Sitting motion to stand

“…The signal was further amplified with a gain of 300, and the sampling frequency was 1925.93 Hz [1]. Furthermore, J Antonio conducted a study that designed an active electronic electrode by eliminating the impedance and filter coupling stage with the aim of optimizing the circuit [2]. In addition, several researchers were also working on electrode comparison for electromyography signal detection.…”

“…In addition, several researchers were also working on electrode comparison for electromyography signal detection. Among them, A Searle et al worked on quantitative comparisons of three types of bioelectrodes by testing electrode impedance, static interference, and motion artifacts [3] [2]. C. Pylatiuk et al also compared the use of electrodes with four different polymeric materials [3].…”