Search Results (604)

This paper presents the design, modeling, and experimental validation of a capacitive tactile sensor specifically conceived to sense shear-driven contact dynamics in robotic manipulation. The proposed device is a layered flexible capacitive structure, in which controlled tangential interactions are induced. The electrode design maximizes sensitivity to shear motion and promotes an isotropic response with respect to slip direction, thereby addressing two key limitations that affect the majority of existing slip-sensing technologies. An analytical model was developed to describe the essential relationship between shear-induced displacements and the electrical response, providing insight into the design parameters and supporting the selection of geometry and materials. To test the sensor in real conditions, a dedicated capacitive readout circuit based on high-frequency excitation and synchronous demodulation was developed to robustly acquire capacitance variations while rejecting static offsets and parasitic effects. Several formulations for the interposed dielectric layer material were investigated, including viscous fluids and composite mixtures with high-permittivity nanoparticles, with the aim of improving electrical sensitivity while preserving mechanical stability. Experimental results obtained under controlled loading and sliding conditions demonstrate that the sensor is highly sensitive to changes in contact state and tangential interaction dynamics. The sensor responded consistently to both load-induced shear and slip-related phenomena, enabling the reliable monitoring of contact dynamics rather than binary slip detection. A proof-of-concept integration into a robotic finger confirms the suitability of the proposed approach for grasp monitoring. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

Current multi-axis force sensors often rely on complex mechanical structures or arrays of discrete transducers, resulting in larger footprints, higher complexity, and limited scalability for compact applications such as robotic fingertips or wearable tactile interfaces. To address these limitations, this paper introduces a novel optical sensing approach that uses a top-layer patterned color surface and an array of color sensors to decouple and measure normal, shear, and torsional forces within a highly compact 15 × 15 mm footprint. The patterned surface functions as a visual encoding layer, where applied forces induce measurable, direction-dependent shifts in reflected color distribution. By deploying multiple color sensors in an array, each sensor captures localized color variations, enabling spatial reconstruction of both magnitude and direction of applied loads through differential color analysis. The sensor’s performance was validated through robotic gripper integration, where it successfully provided multi-axis force feedback and enabled adaptive gripping force adjustment to achieve robust and stable object manipulation. The experimental results confirm the system’s ability to effectively sensing 3D forces and torsion forces, and support closed-loop control in adaptive robotic grasping. This design presents a scalable, low-profile alternative to conventional multi-axis force sensors, suitable for integration into space-constrained robotic and haptic systems. Full article

Decision-making relies on coordinated neural dynamics that integrate sensory evidence with top-down control. In this EEG study, we examined sensor (scalp)-level theta and alpha-band oscillations, as well as fronto-parietal network connectivity, during a tactile spatial discrimination task. Blindfolded participants judged the lateral offset of the central dot in a three-dot array delivered to the right index finger while an EEG was recorded. Time–frequency analyses revealed that both theta and alpha power were greater for correct than incorrect decision trials during pre-stimulus and post-stimulus intervals, suggesting enhanced preparatory and mnemonic engagement during accurate decisions. Directional connectivity assessed using block (multivariate) Granger causality demonstrated significantly stronger frontal-to-parietal influence in the theta band during both pre- and post-stimulus periods for correct decisions, supporting the role of long-range theta communication for top-down control in guiding tactile judgment. These findings highlight theta-band fronto-parietal communication as a key mechanism supporting successful tactile decision-making. Full article

Achieving precise sliding perception is crucial for enhancing human–machine interactions. Despite the extensive investigation of tactile sensors for static pressure detection, they still face challenges in detecting dynamic information such as sliding direction, speed, pressure and position in interactive touch scenarios. Herein, we propose a self-powered tactile interface that realizes motion-to-electricity generation by electrostatically regulating the carrier concentration and transport in the semiconductive layer with a top gate in sliding movement. This tactile sliding interface can distinguish various dynamic mechanical information by generating voltage signals related to the sliding direction, speed, pressure, and touch position without external bias voltage. By combining machine-learning algorithms, electrical signals of six representative sliding-touch interactions were accurately classified with a recognition accuracy of 98.33%. Furthermore, by integrating sensors into the smartphone’s side button, customizable functions such as volume control, screen unlocking, and music switching were achieved. This work provides an innovative mechanism for sliding sensing in interactive electronic and intelligent control systems. Full article

Robotic-assisted surgery (RAS) provides enhanced dexterity and visualisation but remains constrained by the absence of clinically meaningful palpation and haptic feedback. This perspective examines palpation sensing in RAS from an engineering and system-integration standpoint, identifying the lack of tactile information as a major contributor to increased cognitive load, prolonged training, and risk of tissue injury. Recent advances in force, tactile, vibroacoustic, audio, and optical sensor technologies enable quantitative assessment of tissue mechanical properties and often exceed human tactile sensitivity. However, clinical translation is limited by challenges in sensor miniaturisation, sterilisation, robustness and integration and the absence of standardised evaluation metrics. The integration of artificial intelligence and multimodal sensor fusion with intra-operative imaging and augmented visualisation is highlighted as a key strategy to compensate for sensor limitations and biological variability. Dedicated robotic palpation devices and wireless or magnetically coupled probes are discussed as promising transitional solutions. Overall, the restoration of palpation sensing is presented as a prerequisite for improving safety and efficiency and enabling higher levels of autonomy in future RAS platforms. Full article

This study aims to address the safety and mobility challenges faced by visually impaired individuals. To this end, a lightweight, high-precision semantic segmentation network is proposed for scenes containing tactile paving and zebra crossings. The network is successfully deployed on an intelligent guide robot equipped with a high-definition camera and a Huawei Atlas 310 embedded computing platform. To enhance both real-time performance and segmentation accuracy on resource-constrained devices, an improved G-GhostNet backbone is designed for feature extraction. Specifically, it is combined with a depthwise separable convolution-based Coordinate Attention module and a redesigned Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual features. A dedicated decoder efficiently fuses multi-level features to refine segmentation of tactile paving and zebra crossings. Experimental results demonstrate that the proposed model achieves mPA of 97% and 93%, mIoU of 94% and 86% for tactile paving and zebra crossing segmentation, respectively, with an inference speed of 59.2 fps. These results significantly outperform several mainstream semantic segmentation networks, validating the effectiveness and practical value of the proposed method in embedded systems for visually impaired travel assistance. Full article

With the advancement of tactile sensors, researchers increasingly integrate tactile perception into robotics, but only for tasks such as object reconstruction, classification, recognition, and grasp state assessment. In this paper, we rethink the relationship between visual and tactile perception and propose a novel robotic grasping detection method based on visual–tactile perception. Initially, we construct a visual–tactile dataset containing the grasp stability for each potential grasping position. Next, we introduce a novel Grasp Stability Prediction Module (GSPM) to generate a grasp stability probability map, providing prior knowledge regarding grasp stability to the grasp detection network for each possible grasp position. Finally, the map is multiplied element-wise with the corresponding colored image and inputted into the grasp detection network. Experimental results demonstrate that our novel visual–tactile fusion method significantly enhances robotic grasping detection accuracy. Full article

Reliable motion classification is essential for practical prosthetic-hand control. Unintended activations caused by ambiguous motions, unknown motions, or non-target body movements can degrade controllability and compromise user safety. Mechanical-sensing approaches are attracting attention as alternatives or complements to surface electromyography, and tactile-sensor-based methods represent one such direction. However, despite extensive studies on prosthetic control, systematic investigations of computationally lightweight motion-rejection strategies remain limited. This study investigates rejection mechanisms to improve the robustness of polyvinylidene fluoride (PVDF) tactile-sensor-based prosthetic control. The proposed approach selectively withholds outputs for misclassified and non-target inputs. We compare three mechanisms: (1) one-class support vector machine (OCSVM) outlier detection, (2) entropy-based rejection using a multilayer perceptron (BPNN-Entropy), and (3) a parameter-free decision-consistency check for one-vs-rest support vector machines (SVMs) that withholds classification when the output sign pattern is inconsistent (one-vs-rest reject option (OvR-RO); proposed). Performance is evaluated for three sources of unintended activation: ambiguous target trials (retrospectively defined), unknown motions excluded from training, and non-target body movements. The results show that OvR-RO achieves a favorable balance between rejection rate and rejection precision for ambiguous motions, while maintaining responsiveness. Overall, explicitly rejecting misclassified and non-target motions is effective for enhancing robustness in tactile-sensor-based prosthetic control. Full article

In this study, a high-performance MoS₂-based strain-gauge pressure was sensor fabricated entirely below 80 °C, enabling direct integration onto flexible polyethylene terephthalate (PET) substrates. The sensor comprised a three-layer MoS₂ channel (~2 nm) patterned via dry transfer and O₂/Ar plasma etching, interfaced with Cr/Au electrodes. This wafer-scale and cost-effective fabrication route preserves the crystallinity of the film and prevents substrate degradation. The sensor achieved a gauge factor of ~104 under compression, representing a fifty-fold improvement over conventional metal foil gauges (~2), with a linear response across both compressive and tensile regimes. Mechanical robustness was confirmed through repeated bending and tape adhesion tests, with no degradation in electrical performance. When configured as a Wheatstone bridge, this device exhibits normalized sensitivity suitable for real-time monitoring, with response and recovery times below 200 ms. These results establish O₂/Ar-plasma-patterned MoS₂ architectures as a scalable, cost-effective platform for next-generation flexible sensors, outperforming metal-foil technology in applications including seat-occupancy detection, wearable physiological monitoring, and tactile interfaces for soft robotics. Full article

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition design and an inverse mode auxiliary layering process, it ensures the uniform distribution of stress at different curvatures. The sensor adopts a 3 × 3 tactile array configuration, replicating the 3D curved surface distribution of human mechanoreceptors. By analyzing multi-point outputs, the sensor reconstructs contact pressure gradients and infers the softness or stiffness of touched objects, thereby realizing both structural and functional bionics. These sensors exhibit excellent linearity within 0–100 kPa (sensitivity ≈ 36.86 kPa⁻¹), fast response (2 ms), and outstanding durability (signal decay of only 1.94% after 30,000 cycles). It is worth noting that this conformal tactile fingertip integration method not only exhibits uniform responses at each unit, but also has the preload-free advantage, and then performs well in pulse detection and hardness discrimination. This work provides a novel bioinspired pathway for conformal integration of tactile sensors, enabling artificial skins and robotic fingertips with human-like tactile perception. Full article

The rising global demand for prosthetic limbs, driven by approximately 185,000 amputations annually in the United States, underscores the need for innovative and cost-efficient solutions. This study explores the integration of hybrid materials, advanced 3D printing techniques, and smart sensing technologies to enhance prosthetic finger production. A Taguchi-based design of experiments (DoE) approach using an L09 orthogonal array was employed to systematically evaluate the effects of infill density, infill pattern, and print speed on the tensile behavior of FDM-printed PLA components. Findings reveal that higher infill densities (90%) and hexagonal patterns significantly enhance yield strength, ultimate tensile strength, and stiffness. Additionally, the rheological properties of polydimethylsiloxane (PDMS) were optimized at various temperatures (30–70 °C), characterizing its viscosity, shear-thinning factors, and stress behaviors for 3D bioprinting of flexible sensors. Barium titanate (BaTiO₃) was incorporated into PDMS to fabricate a flexible tactile sensor, achieving reliable open-circuit voltage readings under applied forces. Structural and functional components of the finger prosthesis were fabricated using FDM, stereolithography (SLA), and extrusion-based bioprinting (EBP) and assembled into a functional prototype. This research demonstrates the feasibility of integrating hybrid materials and advanced printing methodologies to create cost-effective, high-performance prosthetic components with enhanced mechanical properties and embedded sensing capabilities. Full article

Preterm infants in neonatal intensive care units (NICUs) experience impaired neurodevelopment and dysregulated stress responses, partly due to a lack of tactile stimulation. Although massage therapy offers proven therapeutic benefits by stimulating C-tactile afferents through (gentle) dynamic touch, existing methods are limited by clinical staff variability and resource constraints. This work presents a compact soft-pneumatic actuator array (SPAA) utilizing four nylon–TPU actuators (modules) connected in series or in parallel to perform a sequential actuation; this array is designed to deliver safe, shear-free, and massage-like normal compression tailored for preterm infants. Actuator performance was characterized using a load-cell and a pressure sensor under different preloads (10–30 g), establishing operating internal pressures of 20–50 kPa, which produced target force ranges between 0.1 and 0.3 N. Two SPAA architectures were evaluated: (i) parallel manifold with branch resistances and (ii) series chain with graded outlet resistances, using passive fluidic sequencing for controlled activation. The series configuration achieved repeatable sequential actuation with programmable delays, essential for mimicking therapeutic massage patterns. These results demonstrate that passive soft-pneumatic sequencing can reliably deliver dynamic tactile stimuli within neurophysiological and safety constraints, laying the groundwork for standardized, automated neonatal massage therapy in NICUs. Full article

The center of pressure (CoP) is a key biomechanical indicator for assessing balance and fall risk; however, force plates, the gold standard for CoP measurement, are costly and impractical for widespread use. Low-cost alternatives such as inertial units or pressure sensors are limited by drift, sparse sensor coverage, and directional performance imbalances, with previous supervised learning approaches reporting ML-AP NRMSE differences of 3.2–4.7% using 1D time-series models on sparse sensor arrays. Therefore, we propose a tactile sensor-based CoP estimation system using deep learning models that can extract 2D spatial features from each pressure distribution image with CNN/ResNet encoders followed by a Bi-LSTM for temporal patterns. Using data from 23 healthy adults performing four balance protocols, we compared ResNet-Bi-LSTM and CNN-Bi-LSTM with baseline CNN-LSTM and Bi-LSTM models used in previous studies. Model performance was validated using leave-one-out cross-validation (LOOCV) and evaluated with RMSE, NRMSE, and R². The ResNet-Bi-LSTM with angular features achieved the best performance, with RMSE values of 18.63 ± 4.57 mm in the mediolateral (ML) direction and 17.65 ± 3.48 mm in the anteroposterior (AP) direction, while reducing the ML/AP NRMSE difference to 1.3% compared to 3.2–4.7% in previous studies. Under dynamic protocols, ResNet-Bi-LSTM maintained the lowest RMSE across models. These findings suggest that tactile sensor-based systems may provide a cost-effective alternative to force plates and hold potential for applications in gait analysis and real-time balance monitoring. Future work will validate clinical applicability in patient populations and explore real-time implementation. Full article

This paper investigates the effect of tactile feedback on the power efficiency and timing of controlling a remote robotic arm using a custom-built haptic glove. The glove integrates flex sensors to monitor finger movements and vibration motors to provide tactile feedback to the user. Communication with the robotic arm is established via the ESP-NOW protocol using an Arduino Nano ESP32 microcontroller (Arduino, Turin, Italy). This study examines the impact of tactile feedback on task performance by comparing precision, completion time, and power efficiency in object manipulation tasks with and without feedback. Experimental results demonstrate that tactile feedback significantly enhances the user’s control accuracy, reduces task execution time, and enables the user to control hand movement during object grasping scenarios precisely. It also highlights its importance in teleoperation systems. These findings have implications for improving human–robot interaction in remote manipulation scenarios, such as assistive robotics, remote surgery, and hazardous environment operations. Full article