Recently developed flexible mechanosensors based on inorganic silicon, organic semiconductors, carbon nanotubes, graphene platelets, pressure-sensitive rubber and self-powered devices are highly sensitive and can be applied to human skin. However, the development of a multifunctional sensor satisfying the requirements of ultrahigh mechanosensitivity, flexibility and durability remains a challenge. In nature, spiders sense extremely small variations in mechanical stress using crack-shaped slit organs near their leg joints. Here we demonstrate that sensors based on nanoscale crack junctions and inspired by the geometry of a spider's slit organ can attain ultrahigh sensitivity and serve multiple purposes. The sensors are sensitive to strain (with a gauge factor of over 2,000 in the 0-2 per cent strain range) and vibration (with the ability to detect amplitudes of approximately 10 nanometres). The device is reversible, reproducible, durable and mechanically flexible, and can thus be easily mounted on human skin as an electronic multipixel array. The ultrahigh mechanosensitivity is attributed to the disconnection-reconnection process undergone by the zip-like nanoscale crack junctions under strain or vibration. The proposed theoretical model is consistent with experimental data that we report here. We also demonstrate that sensors based on nanoscale crack junctions are applicable to highly selective speech pattern recognition and the detection of physiological signals. The nanoscale crack junction-based sensory system could be useful in diverse applications requiring ultrahigh displacement sensitivity.

The sensitivity of a nanoscale crack-based sensor is enhanced markedly by modulating the crack depth. The crack-depth-propagated sensor exhibits ≈16 000 gauge factor at 2% strain and a superior signal-to-noise ratio of ≈35, which facilitates detection of target signals for voice-pattern recognition.

Humans have a myriad of sensory receptors in different sense organs that form the five traditionally recognized senses of sight, hearing, smell, taste, and touch. These receptors detect diverse stimuli originating from the world and turn them into brain‐interpretable electrical impulses for sensory cognitive processing, enabling us to communicate and socialize. Developments in biologically inspired electronics have led to the demonstration of a wide range of electronic sensors in all five traditional categories, with the potential to impact a broad spectrum of applications. Here, recent advances in bioinspired electronics that can function as potential artificial sensory systems, including prosthesis and humanoid robots are reviewed. The mechanisms and demonstrations in mimicking biological sensory systems are individually discussed and the remaining future challenges that must be solved for their versatile use are analyzed. Recent progress in bioinspired electronic sensors shows that the five traditional senses are successfully mimicked using novel electronic components and the performance regarding sensitivity, selectivity, and accuracy have improved to levels that outperform human sensory organs. Finally, neural interfacing techniques for connecting artificial sensors to the brain are discussed.

Bioelectronics needs to continuously monitor mechanical and electrophysiological signals for patients. However, the signals always include artifacts by patients’ unexpected movement (such as walking and respiration under approximately 30 hertz). The current method to remove them is a signal process that uses a bandpass filter, which may cause signal loss. We present an unconventional bandpass filter material—viscoelastic gelatin-chitosan hydrogel damper, inspired by the viscoelastic cuticular pad in a spider—to remove dynamic mechanical noise artifacts selectively. The hydrogel exhibits frequency-dependent phase transition that results in a rubbery state that damps low-frequency noise and a glassy state that transmits the desired high-frequency signals. It serves as an adaptable passfilter that enables the acquisition of high-quality signals from patients while minimizing signal process for advanced bioelectronics.