Analysis and Simple Circuit Design of Double Differential EMG Active Electrode

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

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

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.

Non-invasive detection of low-level muscle fatigue using surface EMG with wavelet decomposition

Median frequency (MDF) is widely used for detection and tracking of muscle fatigue using surface electromyography (sEMG). However, MDF does not behave consistently or accurately distinguish fatigued from non-fatigued states. In this paper, we study the concept of low-level fatigue and propose increasing average ratio (IAR) and trigger pattern index (TPI) based on discrete wavelet transform (DWT) for distinguishing low-level muscle fatigue. We recorded sEMG using an 8-electode linear monopolar array during isometric contractions from brachioradialis (BRD), biceps brachii long head (BBL), and biceps brachii short head (BBS) muscles of different subjects when performing force exertion. We then calculated the proposed parameters for characterizing low-level fatigue. The analysis indicated that the proposed approach is more consistent and stable when distinguishing low-level muscle fatigue and sheds light on the behavior of sEMG in frequency domain with respect to low-fatigue force exertion.

Real-time continuous recognition of knee motion using multi-channel mechanomyography signals detected on clothes

Mechanomyography (MMG) signal has been recently investigated for pattern recognition of human motion. In theory, it is no need of direct skin contact to be detected and unaffected by changes in skin impedance. So, it is hopeful for developing wearable sensing device with clothes. However, there have been no studies so far to detect MMG signal on clothes and verify the feasibility of pattern recognition. For this study, 4-channel MMG signals were detected on clothes from the thigh muscles of 8 able-bodied participants. The support vector machines (SVM) classifier with 4 common features was used to recognize 6 knee motions and the average accuracy of nearly 88% was achieved. The accuracy can be further improved up to 91% by introducing a new proposed feature of the difference of mean absolute value (DMAV), but not by root mean square (RMS) or mean absolute value (MAV). Furthermore, the first-order Markov chain model was combined with the SVM classifier and it can avoid the misclassifications in some cases. For application to wearable power-assisted devices, this study would promote the developments of more flexible, more comfortable, and minimally obtrusive wearable sensing devices with clothes and recognition techniques of human motion intention.

A simple encoding method for Sigma-Delta ADC based biopotential acquisition systems

Sigma Delta analogue-to-digital converters allow acquiring the full dynamic range of biomedical signals at the electrodes, resulting in less complex hardware and increased measurement robustness. However, the increased data size per sample (typically 24 bits) demands the transmission of extremely large volumes of data across the isolation barrier, thus increasing power consumption on the patient side. This problem is accentuated when a large number of channels is used as in current 128-256 electrodes biopotential acquisition systems, that usually opt for an optic fibre link to the computer. An analogous problem occurs for simpler low-power acquisition platforms that transmit data through a wireless link to a computing platform. In this paper, a low-complexity encoding method is presented to decrease sample data size without losses, while preserving the full DC-coupled signal. The method achieved a 2.3 average compression ratio evaluated over an ECG and EMG signal bank acquired with equipment based on Sigma-Delta converters. It demands a very low processing load: a C language implementation is presented that resulted in an 110 clock cycles average execution on an 8-bit microcontroller.

A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions

In this study, a multimodal fusion framework based on three different modal biosignals is developed to recognize human intentions related to lower limb multi-joint motions which commonly appear in daily life. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from twelve subjects while performing nine lower limb multi-joint motions. These multimodal data are used as the inputs of the fusion framework for identification of different motion intentions. Twelve fusion techniques are evaluated in this framework and a large number of comparative experiments are carried out. The results show that a support vector machine-based three-modal fusion scheme can achieve average accuracies of 98.61%, 97.78% and 96.85%, respectively, under three different data division forms. Furthermore, the relevant statistical tests reveal that this fusion scheme brings significant accuracy improvement in comparison with the cases of two-modal fusion or only a single modality. These promising results indicate the potential of the multimodal fusion framework for facilitating the future development of human-robot interaction for lower limb rehabilitation.