Conductive hydrogels as promising candidates of wearable electronics have attracted considerable interest in health monitoring, multifunctional electronic skins, and human–machine interfaces. However, to simultaneously achieve excellent electrical properties, superior stretchability, and a low detection threshold of conductive hydrogels remains an extreme challenge. Herein, an ultrastretchable high-conductivity MXene-based organohydrogel (M-OH) is developed for human health monitoring and machine-learning-assisted object recognition, which is fabricated based on a Ti3C2T x MXene/lithium salt (LS)/poly­(acrylamide) (PAM)/poly­(vinyl alcohol) (PVA) hydrogel through a facile immersion strategy in a glycerol/water binary solvent. The fabricated M-OH demonstrates remarkable stretchability (2000%) and high conductivity (4.5 S/m) due to the strong interaction between MXene and the dual-network PVA/PAM hydrogel matrix and the incorporation between MXene and LS, respectively. Meanwhile, M-OH as a wearable sensor enables human health monitoring with high sensitivity and a low detection limit (12 Pa). Furthermore, based on pressure mapping image recognition technology, an 8 × 8 pixelated M-OH-based sensing array can accurately identify different objects with a high accuracy of 97.54% under the assistance of a deep learning neural network (DNN). This work demonstrates excellent comprehensive performances of the ultrastretchable high-conductive M-OH in health monitoring and object recognition, which would further explore extensive potential application prospects in personal healthcare, human–machine interfaces, and artificial intelligence.

Electronic skin (E‐skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E‐skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure–temperature bimodal sensing E‐skin based on all resistive output signals is developed for accurate object classification, which consists of laser‐induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure‐sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of −34.15 kPa−1. Meanwhile, a high temperature coefficient of resistance of −3.84%°C−1 is obtained in the range of 24–40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E‐skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E‐skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure–temperature bimodal sensing E‐skin demonstrates potential application in human‐machine interfaces, intelligent robots, and smart prosthetics.

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information‐processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3‐based memristor demonstrates a high ON/OFF ratio (>103), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum‐structured piezoresistive sensor array (sensitivity of 22.4 kPa−1, durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3‐based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital−analog bimodal memristor would demonstrate potential application in human–machine interaction, prosthetics, and artificial intelligence.

Since current reports demonstrated a higher prevalence of foot ulcers in diabetic patients who suffer from foot complication, the preventing occurrence of foot ulcers were the primary target in foot care. Clinical consensus introduced a variety of pressure-relieving products to diabetic patients and clinicians prescribed these products to their patients and recommended them used in daily life. However, available data were still controversial and whether these products could effectively reduce plantar pressure or not were uncertain. Thereby, this meta-analysis aimed first to summary all relevant findings in current database and secondly to explore whether pressure-relieving insoles/shoes can really relieve plantar pressure and what’s differences between customized products (shoes/insoles) and standard ones in reducing plantar pressure. We first searched published articles cited from Web of Science, Medline via OVID, CINAHL, SCOPUS, INFORMIT, Cochrane Central and EMBASE via OVID. Then we filtered observational studies reporting experimental effect of pressure-relieving insoles/shoes. Meanwhile, we set up primary outcome as overall mean peak plantar pressure (MPP) and secondary outcomes as MPP at various plantar regions and MPP at insoles/shoes with various structure designs. Our results show that pressure-relieving products (shoes/insoles) did lower the amplitude of pressure concentration; effect of custom-made and pre-fabricated products on pressure-relieving were similar. These findings suggested that no matter pressure-relieving products were custom-made or prefabricated standard one, if they were designed targeting to increase overall plantar contact areas, such as designed based on plantar model, or to provide extra arch supports or plug-in structures to transfer pressure concentration, they were all useful in diabetic foot care to prevent occurrence of ulceration. Overall, it is recommended that diabetic patients shall wear pressure-relieving insoles/shoes while walking.