Flexible triboelectric nanogenerators (TENGs) have attracted increasing interest since their advent in 2012. In comparison with other flexible electrodes, hydrogels possess transparency, stretchability, biocompatibility, and tunable ionic conductivity, which together provide great potential as current collectors in TENGs for wearable applications. The development of hydrogel‐based TENGs (H‐TENGs) is currently a burgeoning field but research efforts have lagged behind those of other common flexible TENGs. In order to spur research and development of this important area, a comprehensive review that summarizes recent advances and challenges of H‐TENGs will be very useful to researchers and engineers in this emerging field. Herein, the advantages and types of hydrogels as soft ionic conductors in TENGs are presented, followed by detailed descriptions of the advanced functions, enhanced output performance, as well as flexible and wearable applications of H‐TENGs. Finally, the challenges and prospects of H‐TENGs are discussed.

Mobile health technology and activity tracking with wearable sensors enable continuous unobtrusive monitoring of movement and biophysical parameters. Advancements in clothing‐based wearable devices have employed textiles as transmission lines, communication hubs, and various sensing modalities; this area of research is moving towards complete integration of circuitry into textile components. A current limitation for motion tracking is the need for communication protocols demanding physical connection of textile with rigid devices, or vector network analyzers (VNA) with limited portability and lower sampling rates. Inductor–capacitor (LC) circuits are ideal candidates as textile sensors can be easily implemented with textile components and allow wireless communication. In this paper, the authors report a smart garment that can sense movement and wirelessly transmit data in real time. The garment features a passive LC sensor circuit constructed of electrified textile elements that sense strain and communicate through inductive coupling. A portable, lightweight reader (fReader) is developed for achieving a faster sampling rate than a downsized VNA to track body movement, and for wirelessly reading sensor information suitable for deployment with a smartphone. The smart garment–fReader system monitors human movement in real‐time and exemplifies the potential of textile‐based electronics moving forward.

The development of flexible strain sensors over the past decade has focused on accessing high strain percentages and high sensitivity (i.e., gauge factors). Strain sensors that employ capacitance as the electrical signal to correlate to strain are typically restricted in sensitivity because of the Poisson effect. By employing auxetic structures, the limits of sensitivity for capacitive sensors have been exceeded, which has improved the competitiveness of this modality of sensing. In this work, the first employment of helical auxetic yarns as capacitive sensors is presented. It is found that the response of the helical auxetic yarn capacitive sensors (termed as HACS) is dependent on the two main fabrication variables—the ratio of diameters and the helical wrapping length. Depending on these variables, sensors that respond to strain with increasing or decreasing capacitance values can be obtained. A greater auxetic character results in larger sensitivities accessible at smaller strains—a characteristic that is not commonly found when accessing high gauge factors. In addition, the highest sensitivity for auxetic capacitive sensors reported thus far is obtained. A mechanism of sensor response that explains both the variable capacitance response and the high gauge factors obtained experimentally is proposed.

Wearable multiaxes motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size‐adjustable inductive sensor for kinematic motion tracking are introduced. The sensor is highly stable and able to track high‐frequency (>15 Hz) and high strain rates (>450% s⁻¹). Four sensors are used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm is used to predict the sagittal, transverse, and frontal hip joint angle, using the raw signals from sport shorts during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R² = 0.98 and root mean squared error of 2° in all three planes. Herein, an alternative strain sensor is provided to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that can find applications in smart wearable devices, robotics, or direct integration into textiles.

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of “smart” clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.