The quest for a close human interaction with electronic devices for healthcare, safety, energy and security has driven giant leaps in portable and wearable technologies in recent years. Electronic textiles...

This research investigates the isotherm, kinetics, and thermodynamic parameters of the adsorption of two direct dyes in single and multiple dye solutions onto the modified multiwalled carbon nanotubes using poly(propylene imine) dendrimer (CNT-Den). The surface morphology and functional groups of the prepared CNT-Den were studied using field emission scanning electron microscopy (FESEM) images and Fourier transform infrared (FTIR) spectra, respectively. The Langmuir, Freundlich, and Tempkin isotherms were studied, and the results indicated that the data followed the Langmuir isotherm. The kinetic studies revealed that the pseudo-second order model was best fitted to our results rather than pseudo-first and intraparticle diffusion model. The calculated thermodynamic parameters showed the endothermic and spontaneous nature of the adsorption process. The overall results illustrated that CNT-Den can be effectively used as an adsorbent for the removal of dyes from colored wastewaters especially textile effluents.

Performance and durability of conductive yarns are essential factors to consider in the development of smart garments for textile computing applications. Conductive yarns and materials are used in various consumer and industrial products, however, their performance after washing, which is present with smart garments, is an unconventional, yet important consideration. This study investigates the impact of domestic washing on conductive silver-plated nylon and carbon-containing nylon yarns knitted into different patterns, simulating the incorporation of the conductive yarns into smart textiles. Various factors such as conductive yarn materials, types of knitting machine, and conductive feature patterns were considered. The resistance of silver-based textile electrodes increased by 100−300% over 50 wash cycles. Sulfidation and mechanical abrasion are the two main reasons for silver yarn degradation. The resistance of carbon-based textile electrodes stabilized after about five laundry cycles, showing little to no change afterward. Finally, the best performing silver and carbon electrodes were compared with gold-standard hydrogel electrodes for skin-electrode impedance and electrocardiogram measurement before and after 35 times of laundering. The results obtained demonstrated that both of the textile electrodes performed comparably to hydrogel electrodes and can be considered for continuous monitoring of biopotential signals from the human body.

Recent advances in telemedicine and personalized healthcare have motivated new developments in wearable technologies targeting continuous monitoring of biosignals. Common limitations of wearables for continuous monitoring include durability and breathability of their biopotential electrodes. This paper tackles this challenge by proposing flexible, breathable, and washable dry textile electrodes made of conductive elastomeric filaments (CEFs). First, candidate CEF fibers are characterized. Using an industrial knitting machine, CEF fibers are then directly knitted into textile electrodes. To assess their performance in more realistic circumstances, smart garments with textile electrodes are knitted. Electrocardiograms (ECGs) are acquired using an underwear garment and electrooculograms (EOGs) are acquired using a headband. ECGs and EOGs with textile electrodes are found to have comparable fidelity to that of the gold standard gel electrodes. CEF electrodes are also resistant to repeated wash and dry cycles (30×) and continue to acquire high‐fidelity biosignals. Smart underwear garments are also used to perform continuous ECG measurements in five participants over 24 h of unrestricted daily activities. Results demonstrate the success of these garments in performing high fidelity continuous ECG monitoring. Collectively, these results present CEF electrodes as a promising scalable solution to the challenges of wearable technologies for long‐term continuous electrophysiological monitoring applications.

Background: The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously. Results: In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods.