Soft pressure sensors play key roles as input devices of electronic skin (E-skin) to imitate real human skin. For efficient data acquisition according to stimulus types such as detailed pressure images or macroscopic strength of stimuli, soft pressure sensors can have variable spatial resolution, just like the uneven spatial distribution of pressure-sensing receptors on the human body. However, previous methods on soft pressure sensors cannot achieve such tunability of spatial resolution because their sensor materials and read-out electrodes need to be elaborately patterned for a specific sensor density. Here, we report a universal soft pressure-sensitive platform based on anisotropically self-assembled ferromagnetic particles embedded in elastomer matrices whose spatial resolution can be facilely tuned. Various spatial densities of pressure-sensing receptors of human body parts can be implemented by simply sandwiching the film between soft electrodes with different pitches. Since the anisotropically aligned nickel particles form independent filamentous conductive paths, the pressure sensors show spatial sensing ability without crosstalk, whose spatial resolution up to 100 dpi can be achieved from a single platform. The sensor array shows a wide dynamic range capable of detecting various pressure levels, such as liquid drops (∼30 Pa) and plantar (∼300 kPa) pressures. Our universal soft pressure-sensing platform would be a key enabling technology for actually imitating the receptor systems of human skin in robot and biomedical applications.

Strain-engineered elastic platforms that can efficiently distribute mechanical stress under deformation offer adjustable mechanical compliance for stretchable electronic systems. By fully exploiting strain-free regions that are favourable for fabricating thin-film...

Electronic skin (E-skin) based on tactile sensors has great significance in next-generation electronics such as biomedical application and artificial intelligence that requires interaction with humans. To mimic the properties of human skin, high flexibility, excellent sensing capability, and sufficient spatial resolution through high-level sensor integration are required. Here, we report a highly sensitive pressure sensor matrix based on a piezoresistive cellulose/ single-walled carbon nanotube-entangled fiber network, which forms its own porous structure enabling a superior pressure sensor with a high sensitivity (9.097 kPa −1 ), a fast response speed (<2 ms), and orders of magnitude detection range with a detection limit of 1 Pa. Furthermore, the remarkable device expandability based on the ease of patterning and scalability allows easy implementation of a largearea pressure sensor matrix which has 2304 (48 × 48) pixels. Combined with a real-time pressure distribution monitoring system, a flexible 3D touch sensor that simultaneously displays plane coordinates and pressure information and a scanning device that detects the morphology of the soft body 3D surface are successfully demonstrated.

Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person variation. So far, previous approaches based on electromyogram or pressure sensors are ineffective in detecting the directional movement of facial muscles. Therefore, in this study, high-performance strain sensors are used for separately detecting x- and y-axis strain. Directional strain distribution data of facial muscle is obtained by applying three-dimensional digital image correlation. Deep learning analysis is utilized for identifying optimal positions of directional strain sensors. The recognition system with four directional strain sensors conformably attached to the face shows silent vowel recognition with 85.24% accuracy and even 76.95% for completely nonobserved subjects. These results show that detection of the directional strain distribution at the optimal facial points will be the key enabling technology for highly accurate silent speech recognition.

The change in resistance upon bending in metal films as thick as 1 mm used for underpanel force touch applications is limited by the low sensitivity, thus requiring high-performance readout circuitry. In this paper, we report inkjet-printed silver thin films having crack-inducing underlayers, which further increases the sensitivity of their resistance changes under deformation. This allows for detecting weak vertical forces even through the plates (force-receiving layer), such as 0.4 or 1.2 mm thick polyethylene terephthalate or 0.4 mm thick glass. The underplate sensors will detect a force level as low as 10 gf, which corresponds to the amount of force required for fingerprint recognition. Furthermore, such highly sensitive strain sensors can potentially solve the inaccuracy issue of wearable devices, which can occur when misplaced sensors detect relatively weak biosignals, such as heart rate and blood pressure. The sensor detects the accurate pulse patterns of the wrist artery even though it is off-centered from the artery by 6 mm or larger. The crack-based strain sensor and its usage as a hidden underplate force sensing device will create various wearable and user–machine interface applications.