Communication with plants to understand their growth mechanisms and interaction with the surrounding environment may improve production yield in agriculture and facilitate prevention of plant diseases and negative influence of environmental stress. Typical sensing technologies in plant biology and precision agriculture largely rely on techniques with low spatial and temporal resolutions, and fail to continuously and precisely determine localized variation in leaf physiology and microenvironments. Here, techniques to develop a multifunctional stretchable leaf-mounted sensor have been developed to offer optimized adaptability to plant growth and monitor leaf physiological and environmental conditions in continuous and highly sensitive manners. The multifunctional leaf sensor contains multiple heterogeneous sensing elements made of metal, carbon nanotube matrix, and silicon, leading to temperature, hydration, light illuminance, and strain sensing capabilities on a leaf. Evaluation under a controlled environment indicates excellent precision and accuracy of the sensor compared to conventional devices. Furthermore, indoor and outdoor experiments have demonstrated the multifunctional monitoring ability of the sensor in real situations. The multifunctional stretchable sensor holds the promise to advance monitoring techniques in plant biology and precision agriculture, resulting in improved capability to record slow and subtle physiological changes in plants and plant/environment interaction.

Sensing of mechanical motion based on flexible electromagnetic sensors is challenging due to the complexity of obtaining flexible magnetic membranes with confined and enhanced magnetic fields. A fully flexible electromechanical system (MEMS) sensor is developed to conduct wearable monitoring of mechanical displacement with excellent adaptability to complex surface morphology through a suspended flexible magnet enclosed within a novel setup formed by a multi‐layer flexible coil and annular origami magnetic membranes. The annular membranes not only regulate the overall distribution of the magnetic field and enhance the overall magnetism by 291%, but also greatly increase the range of the magnetic field to cover the entire region of the coil. The sensor offers a broad frequency response ranging from 1 Hz to 10 kHz and a sensitivity of 0.59 mV µm−1 at 1.7 kHz. The fully flexible format of the sensor enables various applications demonstrated by biophysical sensing, motion detection, voice recognition, and machine diagnostics through direct attachment on soft and curvilinear surfaces. Similar sensors can combine multiple sensing and energy harvesting modalities to achieve battery‐less and self‐sustainable operation, and can be deployed in large numbers to conduct distributed sensing for machine status assessment, health monitoring, rehabilitation, and speech aid.

Wearable technology has attracted significant public attention and has generated huge societal and economic impact, leading to changes of both personal lifestyles and formats of healthcare. An important type of devices in wearable technology is flexible and stretchable skin sensors used primarily for biophysiological signal sensing and biomolecule analysis on skin. These sensors offer mechanical compatibility to human skin and maximum compliance to skin morphology and motion, demonstrating great potential as promising alternatives to current wearable electronic devices based on rigid substrates and packages. The mechanisms behind the design and applications of these sensors are numerous, involving profound knowledge about the physical and chemical properties of the sensors and the skin. The corresponding materials are diverse, featuring thin elastic films and unique stretchable structures based on traditional hard or ductile materials. In addition, the fabrication techniques that range from complementary metal-oxide semiconductor (CMOS) fabrication to innovative additive manufacturing have led to various sensor formats. This paper reviews mechanisms, materials, fabrication techniques, and representative applications of flexible and stretchable skin sensors, and provides perspective of future trends of the sensors in improving biomedical sensing, human machine interfacing, and quality of life.

Coupling soft bodies and dynamic motions with multifunctional flexible electronics is challenging, but is essential in satisfying the urgent and soaring demands of fully soft and comprehensive robotic systems that can perform tasks in spite of rigorous spatial constraints. Here, the mobility and adaptability of liquid droplets with the functionality of flexible electronics, and techniques to use droplets as carriers for flexible devices are combined. The resulting active droplets (ADs) with volumes ranging from 150 to 600 µL can conduct programmable functions, such as sensing, actuation, and energy harvesting defined by the carried flexible devices and move under the excitation of gravitational force or magnetic force. They work in both dry and wet environments, and adapt to the surrounding environment through reversible shape shifting. These ADs can achieve controllable motions at a maximum velocity of 226 cm min−1 on a dry surface and 32 cm min−1 in a liquid environment. The conceptual system may eventually lead to individually addressable ADs that offer sophisticated functions for high‐throughput molecule analysis, drug assessment, chemical synthesis, and information collection.

Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech. However, high-frequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction. In this paper, speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared. Four kinds of speech enhancement algorithms based on a fully connected neural network (FCNN), a long short-term memory (LSTM), a bidirectional long short-term memory (BLSTM), and a convolutional-recurrent neural network (CRNN) are adopted to enhance the sensor signals, and their performance after deployment on four kinds of edge and cloud platforms is also investigated. Experimental results show that the BLSTM performs best in improving speech quality, but is poorest with regard to hardware deployment. It improves short-time objective intelligibility (STOI) by 0.18 to nearly 0.80, which corresponds to a good intelligibility level, but it introduces latency as well as being a large model. The CRNN, which improves STOI to about 0.75, ranks second among the four neural networks. It is also the only model that is able to achieves real-time processing with all four hardware platforms, demonstrating its great potential for deployment on mobile platforms. To the best of our knowledge, this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors. The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.