During tactile perception, the electrical system in the brain will respond to the changes in vibration, friction, and surface properties between the perceived surface and human skin. To establish the relationship between tactile perception and friction, 64-channel Neuroscan event-related potential (ERP) system and a three-axis force sensor were used to obtain electroencephalograph (EEG) and friction signals during fingers exploring surfaces. An oddball variation mode was applied to produce the late positive component (LPC) of ERP. Three fabric samples with different friction coefficient were chosen. The results indicate that LPC amplitude is affected by friction. High friction leads to high LPC amplitude. LPC latency is related to difference of friction. Large difference of friction between samples induces small LPC latency. During tactile perception, the applied normal load of male is larger than that of female. Tactile perception is effected by gender. Female has larger LPC latency than male, indicating female are more sensitive to recognize fabrics. This fundamental study could provide theory support to the related researches.

This paper provides an overview of the development of haptic texture reproduction technology, focusing on methods such as vibration, ultrasound, and electrostatic systems. It also explores how artificial intelligence (AI) and deep learning contribute to enhancing the adaptability and personalization of tactile feedback. The paper emphasizes the importance of understanding tactile perception mechanisms, particularly the role of Piezo proteins and the interaction between receptors and their microenvironment, in improving feedback system accuracy. Despite technological advancements, the accurate reproduction of fine textures and high-frequency vibrations remains a challenge. The review underscores that interdisciplinary research, including neuroscience, materials science, and AI, is crucial for future advancements in haptic systems.

Meissner’s corpuscles are essential mechanoreceptors that detect low-frequency vibrations. However, the internal fluid dynamic processes that convert directional mechanical stimuli into neural signals are not yet fully understood. This study aims to clarify the direction-specific sensing mechanism by analyzing internal fluid flow and shear stress distribution under different vibration modes. A biomimetic microfluidic platform was developed and coupled with a dynamic mesh computational fluid dynamics (CFD) model to simulate the response of the corpuscle to 20 Hz normal and tangential vibrations. The simulation results showed clear differences in fluid behavior. Normal vibration produced localized vortices and peak wall shear stress greater than 0.0054 Pa along the short axis. In contrast, tangential vibration generated stable laminar flow with a lower average shear stress of about 0.0012 Pa along the long axis. These results suggest that the internal structure of the Meissner corpuscle is important for converting mechanical inputs from different directions into specific fluid patterns. This study provides a physical foundation for understanding mechanotransduction and supports the design of biomimetic sensors with improved directional sensitivity for use in smart skin and soft robotic systems.

Cancer metastasis is the leading cause of cancer-related deaths, with the ability of cancer cells to invade blood vessels or lymphatic systems, determining their metastatic potential. Therefore, the rapid and accurate assessment of cell invasiveness is crucial. Current methods, such as the Transwell assay and fluorescent labeling, are complex, invasive, and may disrupt the physiological state of live cells. In this study, we introduce an electrochemical impedance-based method for evaluating cancer cell invasiveness, combining Transwell and microfluidic technologies to monitor the invasion process in a dynamic environment. A stable microfluidic chip with 30 μm interdigital electrodes was developed, optimized for HeLa cell detection. We identified 1 kHz as the optimal frequency for achieving the maximum impedance resolution of cancer cell invasiveness. By correlating the impedance response of Z_cells/Z_no-cells with invasiveness, we established a reliable electrochemical model. This model was validated with a hydrogen peroxide cytotoxicity assay, showing a high correlation with optical staining and a minimal error of 1.89%, underscoring its potential for drug efficacy prediction. The proposed method offers rapid detection, low cost, and requires no manual intervention, making it an efficient and reliable tool for assessing cancer cell invasiveness in therapeutic research.

(1) Background: Multimodal tactile cognition is paramount for robotic dexterity, yet its advancement is constrained by the limited realism of existing texture datasets and the difficulty of effectively fusing heterogeneous signals. This study introduces a comprehensive framework to overcome these limitations by integrating a parametrically designed dataset with a novel fusion architecture. (2) Methods: To address the challenge of limited dataset realism, we developed a universal texture dataset that leverages information entropy and Perlin noise to simulate a wide spectrum of surfaces. To tackle the difficulty of signal fusion, we designed the Multimodal Fusion Attention Transformer Network (MFT-Net). This architecture strategically combines a Convolutional Neural Network (CNN) for local feature extraction with a Transformer for capturing global dependencies, and it utilizes a Squeeze-and-Excitation attention module for adaptive cross-modal weighting. (3) Results: Evaluated on our custom-designed dataset, MFT-Net achieved a classification accuracy of 86.66%, surpassing traditional baselines by a significant margin of over 21.99%. Furthermore, an information-theoretic analysis confirmed the dataset’s efficacy by revealing a strong positive correlation between the textures’ physical information content and the model’s recognition performance. (4) Conclusions: Our work establishes a novel design-verification paradigm that directly links physical information with machine perception. This approach provides a quantifiable methodology to enhance the generalization of tactile models, paving the way for improved robotic dexterity in complex, real-world environments.