Recently, there has been an increase in the number of reports on textile-based dry electrodes that can detect biopotentials without the need for electrolytic gels. However, these textile electrodes have a higher electrode skin interface impedance due to the improper contact between the skin and the electrode, diminishing the reliability and repeatability of the sensor. To facilitate improved skin–electrode contact, the effects of load and holding contact pressure were monitored for an embroidered textile electrode composed of multifilament hybrid thread for its application as a surface electromyography (sEMG) sensor. The effect of the textile’s inter-electrode distance and double layering of embroidery that increases the density of the conductive threads were studied. Electrodes embroidered onto an elastic strap were wrapped around the forearm with a hook and loop fastener and tested for their performance. Time domain features such as the Root Mean Square (RMS), Average Rectified Value (ARV), and Signal to Noise Ratio (SNR) were quantitatively monitored in relation to the contact pressure and load. Experiments were performed in triplicates, and the sEMG signal characteristics were observed for various loads (0, 2, 4, and 6 kg) and holding contact pressures (5, 10, and 20 mmHg). sEMG signals recorded with textile electrodes were comparable in amplitude to those recorded using typical Ag/AgCl electrodes (28.45 dB recorded), while the signal-to-noise ratios were, 11.77, 19.60, 19.91, and 20.93 dB for the different loads, and 21.33, 23.34, and 17.45 dB for different holding pressures. The signal quality increased as the elastic strap was tightened further, but a pressure higher than 20 mmHg is not recommended because of the discomfort experienced by the subjects during data collection.

Due to recent developments in wearable sensor technology, textile electrodes are routinely being employed in electromyography (EMG) for continuous monitoring of the biosignals from the muscles. However, the performance of such smart textile-based health monitoring devices depends on several factors such as, the sensitivity (impedance), durability (reusable/washable), users' comfort ability, integrability, and automatability. In this article we review the characteristics and the performance of the EMG textile electrodes, in the context of functional textile materials, smart textile materials, and smart textile systems for biosignals monitoring. The functional textile materials are confined to signal transmission alone, whereas, the smart textile materials include signal transducers and sensors. The more advanced smart textile systems include signal conditioning circuits with displays. Nowadays, textile-based sensors embedded in garments are becoming a part of users' normal life, in particular, the textile systems that continuously monitor the vital physiological signals from muscles are being sought after in healthcare settings.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an “Internet of Things”-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA’s score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

The diagnosis of neuromuscular diseases is complicated by overlapping symptoms from other conditions. Textile-based surface electromyography (sEMG) of skeletal muscles, offer promising potential in diagnosis, treatment, and rehabilitation of various neuromuscular disorders. However, it is important to consider the impact of load and pressure on EMG signals, as this can significantly affect the signal’s accuracy. This study seeks to investigate the influence of load and pressure on EMG signals and establish a processing framework for these signals in the diagnosis of neuromuscular diseases. The sEMG data were collected from healthy subjects using a textile electrode developed from polyester multi-filament conductive hybrid thread (CleverTex). The textrode was embroidered directly on an elastic bandage (Velcro® strap) placed on volunteer’s muscles while different activities were performed with varying loads and pressure. The collected data were pre-processed using standard techniques of the discrete wavelet transform to remove noise and artifacts. The performance of the proposed denoising algorithm was evaluated using the signal-to-noise ratio (SNR), percentage root mean square difference (PRD), and root mean square error (RMSE). Various signal processing approaches (filters) were considered and the results were compared with the proposed EMG noise reduction algorithms. Based on the experimental results, the fourth level of decomposition for the sym5 wavelets with the Rigrsure threshold method achieved the highest signal-to-noise ratio (SNR) values of 16.69 and 21.91, for soft and hard thresholding functions, respectively. The SNR values of 22.11, 21.54, and 2.78 at three different pressure levels 5 mmHg, 10 mmHg, and 20 mmHg, respectively, indicate the superior performance of wavelet multiresolution filter in de-noising applications. The results of this study suggest that our methodology is effective, precise, and reliable for analysing sEMG data and provide insights into both physiological and pathological neuromuscular conditions.

Identifying a suitable textile electrode that would be durable and assist in recording high-quality bio-signal quality is crucial in the production of medical devices. Therefore, this study is aimed at comparing the time domain characteristics of silver-plated-polyamide-embroidered cotton (SPEC), copper-nickel-plated polyester (CNP), and stainless-steel-fabric (SSF) dry textile electromyography (EMG) electrodes through principal component analysis (PCA). The standard silver/silver chloride (Ag/AgCl) gel electrode was considered as the reference for all the test textile electrodes mentioned above. The EMG signal was measured by activation of the bicep and tibialis anterior muscles, and the time domain features such as root mean square (RMS) voltage, average rectified value (ARV) voltage, signal to noise ratio (SNR), kurtosis, and skewness were extracted from the EMG signal. The SSF electrode outperformed CNP and SPEC electrodes. Each textile electrode exhibited signal-to-noise ratio (SNR) values comparable to that of the standard electrode. The SNR values were 24.38 dB, 17.72 dB, 15.55 dB, and 13.30 dB for Ag/AgCl, SSF, CNP and SPEC electrodes, respectively. The performance of all the conductive textile electrodes was comparable to that of Ag/AgCl. However, the gel electrode required skin preparation and exhibited short-term stability, whereas, textile electrode materials were long-lasting and could be used for biological signal monitoring at home without the assistance of medical professionals.