Biopotential electrodes play an integral role within smart wearables and clothing in capturing vital signals like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). This study focuses on dry e-textile electrodes (E1–E6) and a laser-cut knit electrode (E7), to assess their impedance characteristics under varying contact forces and moisture conditions. Synthetic perspiration was applied using a moisture management tester and impedance was measured before and after exposure, followed by a 24 h controlled drying period. Concurrently, the signal-to-noise ratio (SNR) of the dry electrode was evaluated during ECG data collection on a healthy participant. Our findings revealed that, prior to moisture exposure, the impedance of electrodes E7, E5, and E2 was below 200 ohm, dropping to below 120 ohm post-exposure. Embroidered electrodes E6 and E4 exhibited an over 25% decrease in mean impedance after moisture exposure, indicating the impact of stitch design and moisture on impedance. Following the controlled drying, certain electrodes (E1, E2, E3, and E4) experienced an over 30% increase in mean impedance. Overall, knit electrode E7, and embroidered electrodes E2 and E6, demonstrated superior performance in terms of impedance, moisture retention, and ECG signal quality, revealing promising avenues for future biopotential electrode designs.

The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale–exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.

Among many advancements in wearable technology, there is a strong demand to develop wearable smart textiles for respiration monitoring in daily life settings. The existing respiration monitoring methods are insufficient to monitor respiration events continuously in real-life settings and are not validated extensively. Motivated by this need, we developed a wearable multi-sensory e-textile system called “RespWear” for accurate and reliable respiration monitoring in daily life settings. RespWear consists of a textile pressure sensor belt to monitor the chest movements due to breathing and a wearable data acquisition system. The sensor belt is connected to a microcontroller which supports wireless communication to create a completely wearable and wireless system. The data collected from the sensor belt was sent to an edge computing device via ESP-Now for feature extraction and respiration rate analysis. We recruited 4 participants (2 female and 2 male) to validate the RespWear for monitoring different respiration rates (number of breaths per minute) and different postures (standing, sitting, bending, reclining). An OptiTrack IR camera system was used as a gold standard to validate RespWear performance. Our results showed that RespWear had an excellent correlation (r-value = 0.836) with the data collected using the OptiTrack camera system for the respiration rate calculation. These validation results proved that the RespWear was a good candidate for being a wireless, wearable respiration monitoring system in daily life settings.

In-sensor computing (ISC) represents a paradigm shift in sensor integration technologies. ISC system combines sensing and processing elements on a single chip, enabling real-time processing and reducing computation latency by eliminating massive data transfer and analog-to-digital conversion. However, due to the high integration of sensors and computation units, ISC could suffer from new security attacks that have not been explored yet. In this work, we investigate a covert channel attack that leverages the analog nature of ISC to showcase the feasibility of security attacks and severe consequences in the context of real applications. We envision that this research paper will inspire more researchers to brainstorm new defense mechanisms for the emerging ISC.