In the last few years, textile electrodes have become an interesting topic for physiological monitoring, steadily developing to be applied in innovative wearable sensing systems. The structure of textile electrodes can be fabricated by weaving, knitting, or embroidering conductive yarn and is now commercially available. The upgraded textile electrode is used in biosignal sensing and was designed in the form of a jacquard woven structure to measure the ECG. In the case of knit fabric, which has piezo‐resistive properties, the textile electrodes were developed by using the fabric’s flexible strain gauges in a bio‐monitoring system. However, the piezo‐resistive sensing fabrics have some shortcomings such as low dynamic range, poor repeatability, and performance deterioration after washing or repeated folding. Woven fabric, which is cloth woven in the warp and weft directions, has less strain properties and can be constructed more uniformly than knit fabric. Therefore, due to their more consistent woven structure, jacquard woven fabrics made with conductive yarn can be manufactured evenly on a large scale and consistently maintain their properties. The woven structure of jacquard electrodes consists of a double‐faced weave and is woven with a silver covering yarn in the weft direction. The proposed textile electrodes were composed of two groups made up of warps with either the unremoved 100% warps or the half‐removed 50% warps of jacquard woven electrodes that were convex or flat and were either with or without conductive paste. The ECG measurements for the textile electrodes were repeated three times in resting conditions. The convex jacquard electrodes of the half‐removed 50% warps with the conductive paste resulted in the most significant SNR improvement (33.67 dB). The purpose of this paper was to investigate a method to design jacquard woven electrodes uniformly on a large scale and to apply it feasibly to ECG measurement.

We propose a multichannel-PPG sensor that uses truncated singular value decomposition (SVD) for accurate estimation of heart rate (HR) during high-intensity exercise. We implemented a sensor to acquire nine channel signals simultaneously, and to enable real-time monitoring of PPG signals and HR. For denoising, we used SVD programmed in the microcontroller unit and showed that the pure pulse is concentrated mostly within the first largest singular value with the related two eigenvectors by extracting the significant feature components of the pulse signals. Our proposed sensor was tested in 32 subjects during exercise of various intensities: walking and medium- and high-intensity jogging. We compared our proposed method with the single-channel method, the multichannel weighting method, and the multichannel best signal selection. Furthermore, we incorporated acceleration signals and a HR tracking algorithm, into our proposed multichannel framework and evaluated its performance using the 12 data sets from the 2015 IEEE Signal Processing Cup Database. Our method showed the lowest absolute errors in HR estimation when compared to the single-channel, multichannel weighting, and multichannel best signal selection methods, irrespective of exercise intensity. We also found that our proposed multichannel framework incorporating acceleration signals and a HR tracking algorithm provided the lowest average absolute error of 0.94 beats/m. The results show that our proposed multichannel framework can be used for accurate HR estimation during high-intensity exercise.