Recently, flexible pressure sensors (FPSs) have attracted intensive attention owing to their ability to mimic and function as electronic skin. Some sensors are exploited with a biological structure dielectric layer for high sensitivity and detection. However, traditional sensors with bionic structures usually suffer from a limited range for high‐pressure scenes due to their high sensitivity and high hysteresis in the medium pressure range. Here, a reconfigurable flea bionic structure FPS based on 3D printing technology, which can meet the needs of different scenes via tailoring of the dedicated structural parameters, is proposed. FPS exhibits high sensitivity (1.005 kPa−1 in 0–1 kPa), wide detection range (200 kPa), high repeatability (6000 cycles in 10 kPa), low hysteresis (1.3%), fast response time (40 ms), and very low detection limit (0.5 Pa). Aiming at practical application implementation, FPS has been correspondingly placed on a finger, elbow, arm, neck, cheek, and manipulators to detect the actions of various body parts, suggestive of excellent applicability. It is also integrated to make a flexible 3 × 3 sensor array for detecting spatial pressure distribution. The results indicate that FPS exhibits a significant application potential in advanced biological wearable technologies, such as human motion monitoring.
Objective. Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising. Approach. The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method. Main results. The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance. Significance. This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.
Stretchable strain sensors with high stability, high responsiveness, and low detection limit provide the broad potential for intelligent robots and electronic skin. However, developing low‐cost strain sensors with contact and non‐contact sensing modes remains a significant challenge. In this study, a flexible magnetic strain sensor based on a sandwich structure is proposed to address this challenge. The proposed structure utilizes the coordination between carbon black and Fe3O4 microparticles in the silicone rubber matrix to enhance the sensor's sensitivity to external strain and magnetic stimuli. The sensor exhibits excellent tensile properties with a strain range of up to 180%, fast response/recovery time (78 ms/65 ms), high stability, and durability after 9000 cycles. Moreover, the flexible magnetic strain sensors can detect micro‐vibration and micro‐strain signals. It can also be performed as electronic skin to precisely sense human movements. Furthermore, the newly developed sensor can accurately sense oncoming objects and bicycle riding speed/distance, and a flexible magnetic keyboard is conceived. Consequently, the dual‐modal magnetic strain sensor exhibits an excellent ability to identify contact and non‐contact states and has broad application prospects in next‐generation intelligent products.
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