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Energy consumption is increasing with the rapid growth of externally powered electronics. A vast amount of energy is needed for indoor heating, and body heat is dissipated to the surroundings. Recently, wearable heaters have attracted interest for their efficiency in providing articular thermotherapy. Herein, the fabrication of a personal thermal management device with a self‐powering ability to generate heat through triboelectricity is reported. Composites are prepared with vertically aligned silver tipped nickel cobalt selenide (Ag@NixCo1−xSe) nanowire arrays synthesized on the surface of woven Kevlar fiber (WKF) sheets and reduced graphene oxide (rGO) dispersed in polydimethylsiloxane (PDMS). The Ag@NixCo1−xSe with rGO induces effective Joule heating in the composites (79 °C at 2.1 V). The WKF/Ag@NixCo1−xSe/PDMS composite shows higher infrared reflectivity (98.1%) and thermal insulation (54.8%) than WKF/PDMS. The WKF/Ag@NixCo1−xSe/PDMS/rGO composite has an impact resistance and tensile strength that are 152.2% and 92.1% higher, respectively, than those of WKF/PDMS. A maximum output power density of 1.1 mW cm−2 at a low frequency of 5 Hz confirms efficient mechanical energy harvesting of the composites, which enables self‐heating. The high flexibility, breathability, washability, and effective heat generation achieved during body movement satisfy the wearability requirement and can address global energy concerns.

Accurate transmission of biosignals without interference of surrounding noises is a key factor for the realization of human-machine interfaces (HMIs). We propose frequency-selective acoustic and haptic sensors for dual-mode HMIs based on triboelectric sensors with hierarchical macrodome/micropore/nanoparticle structure of ferroelectric composites. Our sensor shows a high sensitivity and linearity under a wide range of dynamic pressures and resonance frequency, which enables high acoustic frequency selectivity in a wide frequency range (145 to 9000 Hz), thus rendering noise-independent voice recognition possible. Our frequency-selective multichannel acoustic sensor array combined with an artificial neural network demonstrates over 95% accurate voice recognition for different frequency noises ranging from 100 to 8000 Hz. We demonstrate that our dual-mode sensor with linear response and frequency selectivity over a wide range of dynamic pressures facilitates the differentiation of surface texture and control of an avatar robot using both acoustic and mechanical inputs without interference from surrounding noise.

Many modern user interfaces are based on touch, and such sensors are widely used in displays, Internet of Things (IoT) projects, and robotics. From lamps to touchscreens of smartphones, these user interfaces can be found in an array of applications. However, traditional touch sensors are bulky, complicated, inflexible, and difficult-to-wear devices made of stiff materials. The touch screen is gaining further importance with the trend of current IoT technology flexibly and comfortably used on the skin or clothing to affect different aspects of human life. This review presents an updated overview of the recent advances in this area. Exciting advances in various aspects of touch sensing are discussed, with particular focus on materials, manufacturing, enhancements, and applications of flexible wearable sensors. This review further elaborates on the theoretical principles of various types of touch sensors, including resistive, piezoelectric, and capacitive sensors. The traditional and novel hybrid materials and manufacturing technologies of flexible sensors are considered. This review highlights the multidisciplinary applications of flexible touch sensors, such as e-textiles, e-skins, e-control, and e-healthcare. Finally, the obstacles and prospects for future research that are critical to the broader development and adoption of the technology are surveyed.

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