Traditional human emotion recognition is based on electroencephalogram (EEG) data collection technologies which rely on plenty of rigid electrodes and lack anti‐interference, wearing comfort, and portability. Moreover, a significant distribution difference in EEG data also results in low classification accuracy. Here, on‐skin biosensors with adhesive and hydrophobic bilayer hydrogel (AHBH) as interfaces for high accuracy emotion classification are proposed. The AHBH achieves remarkable adhesion (59.7 N m−1) by combining the adhesion mechanism of catechol groups and electrostatic attraction. Meanwhile, based on the synergistic effects of hydrophobic group rearrangements and surface energy reduction, the AHB‐hydrophobic layer exhibits 133.87° water contact angles through hydrophobic treatment of only 0.5 h. Hydrogen and electrostatic bonds are also introduced to form a seamless adhesive‐hydrophobic hydrogel interface and inhibit adhesion attenuation, respectively. With the AHBH as an ideal device/skin interface, the biosensor can reliably collect high‐quality electrophysiological signals even under vibration, sweating, and long‐lasting monitoring condition. Furthermore, the on‐skin electrodes, data processing, and wireless modules are integrated into a portable headband for EEG‐based emotion classification. A domain adaptive neural network based on the transfer learning technique is introduced to alleviate the effect of domain shift and achieve high classification accuracy.
Electronic-skin (E-skin) has been investigated extensively for robotic tactile sensing. However, E-skin sensors based on flexible metamaterials are still challenging to achieve. Moreover, the implementation of E-skin sensor arrays in the actual monitoring of robotic grasping and manipulation conditions are rather limited due to the difficulty in data processing. Herein, highperformance E-skin strain sensors based on flexible auxetic metamaterials are reported, which endow the sensors with the capability of measuring both compressive (40%) and tensile (>80%) strain in a wide range and superior sensitivity, as compared with sensors without the structure. With perception data collected by the sensors, a generic method for real-time detection of unstable robotic grasping is established. Through this method, the complicated problem of processing large-scale arrayed sensor signals is simplified into the calculation of two indices, which extract both time and frequency domain characteristics of the signals. The total detection time (including sensor measurement response and data processing) can be as short as 100 ms, in line with human skin response in slippage perception. Accurate detections in real-time during various grasping and manipulation tasks are presented, demonstrating the great value of the sensors and the detection approach in robotic perception and dexterous manipulation.
Ultrafast and self-powered photodetectors based on high-quality evaporated CsPbBr3 perovskites for applications in optical communication are demonstrated. The photodetectors achieve an ultrafast response time of 3.8 μs and on/off ratio of 3.5 × 104.
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