Piezoelectric Sensor Array Research Articles

Vibro-Acoustic Signatures of Various Insects in Stored Products.

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 dB for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 dB to 16 dB, respectively.

Thin, flexible hybrid-structured piezoelectric sensor array with enhanced resolution and sensitivity

Wearable piezoelectric electronics have garnered significant interest in healthcare and human-machine interfaces due to their unique ability to convert mechanical stimuli into electrical signals. However, current wearable piezoelectric sensors struggle with low spatial resolution, limited sensitivity, and insufficient mechanical flexibility. Here, we report materials and designs of a thin, flexible piezoelectric sensor array with a balanced performance that could be used for spatiotemporal pressure mapping on human skin. The device with a soft-rigid hybrid structure integrates small-pixel, highly sensitive piezoceramic thick films into a conformally adaptable format that achieves satisfactory spatial resolution (6 mm), high sensitivity (15.08 mV/kPa) and optimal flexibility (bending radius of 4.23 mm). Theoretical analysis indicates that the thickness of substrate presents a trade-off between output performance and mechanical flexibility. To demonstrate the capabilities of the device, it has been employed to monitor joint bending, human gait, and throat sounds. Furthermore, it incorporates a convolutional neural network (CNN-2D) for voice recognition, achieving an impressive accuracy of 98.18 %. These capabilities underscore the potential of the device in advanced wearable applications, offering a robust platform for real-time, high-resolution physiological monitoring.

Using Flexible-Printed Piezoelectric Sensor Arrays to Measure Plantar Pressure during Walking for Sarcopenia Screening.

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly.

A combined technique of implantable sensors and probabilistic localization method for monitoring acoustic events on concrete slab

Continuous monitoring of acoustic events in concrete such as cracking and impacting, and providing their location information is a significant topic in structural health monitoring (SHM). This paper develops a combined technique of concrete implantable cube (CIC) and probabilistic localization method to monitor acoustic sources on concrete slabs. The CIC that integrates an octahedron-shaped piezoelectric sensor array can be implanted in concrete structures in long term and supply an omni-dimensional sensing capacity. Aiming at the designed sensor array and the impacts of randomness and heterogeneity of concrete material on the localization accuracy, a probabilistic localization method was proposed. To demonstrate the feasibility of the proposed technique, an experiment including 48 impact tests was conducted on a concrete slab specimen. The acoustic signals received by the implanted CIC were used to reconstruct localization maps to predict the impact areas. The results were quantified using three matrixes including accuracy, precision, and recall; consequently, the accuracy obtained the highest average score close to 0.98 and performed most steadily. In addition, to optimize the proposed technique furtherly, the sensitivity of two key parameters associated with this method were discussed. Overall, the experimental study showed a great potential of this developed technique for long-term monitoring acoustic events on concrete structures.

Skin-Attached Arrayed Piezoelectric Sensors for Continuous and Safe Monitoring of Oculomotor Movements.

Abnormal oculomotor movements are known to be linked to various types of brain disorders, physical/mental shocks to the brain, and other neurological disorders, hence its monitoring can be developed into a simple but effective diagnostic tool. To overcome the limitations in the current eye-tracking system and electrooculography, a piezoelectric arrayed sensor system is developed using single-crystalline III-N thin-film transducers, which offers advantages of mechanical flexibility, biocompatibility, and high electromechanical conversion, for continuous monitoring of oculomotor movements by skin-attachable, safe, and highly sensitive sensors. The flexible piezoelectric eye movement sensor array (F-PEMSA), consisting of three transducers, is attached to the face temple area where it can be comfortably wearable and can detect the muscles' activity associated with the eye motions. Output voltages from upper, mid, and lower sensors (transducers) on different temple areas generate discernable patterns of output voltage signals with different combinations of positive/negative signs and their relative magnitudes for the various movements of eyeballs including 8 directional (lateral, vertical, and diagonal) and two rotational movements, which enable various types of saccade and pursuit tests. The F-PEMSA can be used in clinical studies on the brain-eye relationship to evaluate the functional integrity of multiple brain systems and cognitive processes.

Hole-edge crack monitoring in attachment lug with large bolt hole based on guided wave and circular piezoelectric sensor array

The crack damage monitoring of aircraft structures is very significant for ensuring aircraft safety, reducing maintenance costs and extending service life. Due to the extreme service environment, the attachment lug is prone to initiate crack damage at the hole edge, which leads to crack propagation and fracture failure. Structural health monitoring technology based on piezoelectric guided wave has been widely studied, promoting the development of crack monitoring. However, at present, research on hole-edge crack damage monitoring of attachment lugs still needs to be further carried out. It is difficult to monitor small cracks at the initial stage of crack propagation, and the accuracy of crack monitoring needs to be improved. By focusing on the accuracy of the crack monitoring in the attachment lug, a crack damage monitoring method based on the circular piezoelectric sensor array is proposed in this paper. Combined with damage alarming and localization imaging, this method comprehensively evaluates the hole-edge crack damage monitoring situation and improves the monitoring effect. The method is verified by an experiment in attachment lug, and this verification includes small crack monitoring and crack propagation monitoring. The experimental results demonstrate that this method can achieve correct damage alarming results, and the maximum localization error of crack damage is only 3.02 mm, which provides a research idea for the accurate monitoring of crack damage at the hole edge.

A Fingertip-Mimicking 12×16 200μm-Resolution e-skin Taxel Readout Chip with per-Taxel Spiking Readout and Embedded Receptive Field Processing.

This paper presents an electronic skin (e-skin) taxel array readout chip in 0.18μm CMOS technology, achieving the highest reported spatial resolution of 200μm, comparable to human fingertips. A key innovation is the integration on chip of a 12×16 polyvinylidene fluoride (PVDF)-based piezoelectric sensor array with per-taxel signal conditioning frontend and spiking readout combined with local embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). Experimental results show that Spiking Neural Network (SNN)-based classification of the chip's spatiotemporal spiking output for input tactile stimuli such as texture and flutter frequency achieves excellent accuracies up to 97.1% and 99.2%, respectively. SNN-based classification of the indentation period applied to the on-chip PVDF sensors achieved 95.5% classification accuracy, despite using only a small 256-neuron SNN classifier, a low equivalent spike encoding resolution of 3-5 bits, and a sub-Nyquist 2.2kevent/s population spiking rate, a state-of-the-art power consumption of 12.33nW per-taxel, and 75μW-5mW for the entire chip is obtained. Finally, a comparison of the texture classification accuracies between two on-chip spike encoder outputs shows that the proposed neuromorphic level-crossing sampling (NLCS) architecture with a decaying threshold outperforms the conventional bipolar level-crossing sampling (LCS) architecture with fixed threshold.

Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors.

This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch-catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint's borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results.

AI-enabled crack-length estimation from acoustic emission signal signatures

Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.

Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure

Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.

Development and Evaluation of a Flexible PVDF-Based Balloon Sensor for Detecting Mechanical Forces at Key Esophageal Nodes in Esophageal Motility Disorders.

Prevailing methods for esophageal motility assessments, such as perfusion manometry and probe-based function imaging, frequently overlook the intricate stress fields acting on the liquid-filled balloons at the forefront of the probing device within the esophageal lumen. To bridge this knowledge gap, we innovatively devised an infusible flexible balloon catheter, equipped with a quartet of PVDF piezoelectric sensors. This design, working in concert with a bespoke local key-node analytical algorithm and a sensor array state analysis model, seeks to shed new light on the dynamic mechanical characteristics at pivotal esophageal locales. To further this endeavor, we pioneered a singular closed balloon system and a complementary signal acquisition and processing system that employs a homogeneously distributed PVDF piezoelectric sensor array for the real-time monitoring of dynamic mechanical nuances in the esophageal segment. An advanced analytical model was established to scrutinize the coupled physical fields under varying degrees of balloon inflation, thereby facilitating a thorough dynamic stress examination of local esophageal nodes. Our rigorous execution of static, dynamic, and simulated swallowing experiments robustly substantiated the viability of our design, the logical coherence of our esophageal key-point stress analytical algorithm, and the potential clinical utility of a flexible esophageal key-node stress detection balloon probe outfitted with a PVDF array. This study offers a fresh lens through which esophageal motility testing can be viewed and improved upon.

Display integrated flexible and transparent large-area pyroelectric gesture recognition /piezoelectric touch control sensor array based on in-situ polarized PVDF-TrFE films

Display integrated flexible and transparent large-area pyroelectric gesture recognition /piezoelectric touch control sensor array based on in-situ polarized PVDF-TrFE films

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Aircraft smart skin requires the integration of large-scale, ultrathin, and high-sensitive sensor network on the surface for structural health monitoring (SHM). However, it is fairly difficult to fabricate such large-area flexible sensor networks with low cost and high reliability. Although flexible and monolithic sensors are easy to be fabricated, their applications in large-area impact monitoring have yet to be developed and revealed. In this study, an impact monitoring and localization technology based on monolithic small-area sensors is proposed for large-area structures. The pivotal principle lies in the combination of the flexible piezoelectric sensor array and the Multiple Signal Classification (MUSIC)-assisted Artificial Intelligence Network (ANN) algorithm, where the key sorted feature matrix from the output signals to ANN can be captured by the MUSIC algorithm to achieve the spatial location estimation. The results show that the flexible piezoelectric sensors demonstrate excellent performance to monitor structural impacts in a region of more than 7500% of its area. Secondly, excellent conformation between the sensors and complex surfaces is achieved by the ultrathin thickness almost without affecting the surficial flow field. The overall recognition accuracy and robustness of impact location of machine learning is greatly increased by the integration of MUSIC algorithm, which is immune to the shape, material, thickness, or opening holes of the structure. Except for the impact location, impact energy, impact frequency, impact hardness and other structural health parameters can also be monitored. Therefore, a great prospect in the integrated monitoring of the aircraft’s structural and environmental parameters is expected.

High-Density Flexible Piezoelectric Sensor Array With Double Working Modes

Flexible and self-powered sensing device has aroused considerable interest in the field of the Internet of Things (IoT) and artificial intelligence (AI) in recent years. For more detailed and sensitive perception of the surroundings and external stimuli, sensors with high density and sensitivity are highly desirable. Here, this article reported a simple and low-cost strategy to realize a high-performance flexible piezoelectric sensor array composed of 30 sensing points with a high density of 11.11 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{2}}$ </tex-math></inline-formula> . The piezoelectric sensor array was analyzed by a finite-element model and characterized through sound pressure measurement and wrist bending for motion monitoring under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d}_{{33}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d}_{{31}}$ </tex-math></inline-formula> modes, respectively. The flexible sensor array with the total thickness of 125 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> exhibits a high sensitivity of 447.82 mV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula> kPa <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula> with an ultralow detection limit of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3.60$ </tex-math></inline-formula> Pa. Our work provides a simple strategy for the application potential in smart wearable electronics and human–machine interaction (HMI) areas.

Development of a retrofit layer with an embedded array of piezoelectric sensors for transient pressure measurement in maritime applications

Measurement of transient pressure distribution on maritime structures is important for the assessment of the hydrodynamic loads applied. The commonly used pressure sensors are mostly bulky, need to be bolted to the structure, and/or only provide point-wise measurements. In this paper, an elastic matrix layer with a network of embedded piezoelectric sensors is proposed to address these issues. For experimental validation, a 400 × 400 × 5 mm epoxy layer is fabricated embedding 25 piezoelectric sensors on a square grid in accordance with Gauss-Lobatto-Legendre points. A finite element based inverse procedure is developed to reconstruct the pressure field from the electric potentials measured by the piezoelectric transducers. Feasibility of the concept is evaluated by measuring and reconstructing the pressure field generated by a travelling wave in a water tank. Sensitivity of the layer is also investigated through the experiments. The results indicate that the retrofit layer is capable of pressure field reconstruction, and that the presence of disturbances on the sensing surface does not affect the measurements in a notable way, while non-ideal conditions of the mounting can have a significant impact on the accuracy of the measurements. The results highlight the potential of the concept in pressure distribution measurements.

Modal Decomposition of Acoustic Emissions from Pencil-Lead Breaks in an Isotropic Thin Plate.

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber-frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions.

Intelligent Wearable Wrist Pulse Detection System Based on Piezoelectric Sensor Array

The human radial artery pulse carries a rich array of biomedical information. Accurate detection of pulse signal waveform and the identification of the corresponding pulse condition are helpful in understanding the health status of the human body. In the process of pulse detection, there are some problems, such as inaccurate location of radial artery key points, poor signal noise reduction effect and low accuracy of pulse recognition. In this system, the pulse signal waveform is collected by the main control circuit and the new piezoelectric sensor array combined with the wearable wristband, creating the hardware circuit. The key points of radial artery are located by an adaptive pulse finding algorithm. The pulse signal is denoised by wavelet transform, iterative sliding window and prediction reconstruction algorithm. The slippery pulse and the normal pulse are recognized by feature extraction and classification algorithm, so as to analyze the health status of the human body. The system has accurate pulse positioning, good noise reduction effect, and the accuracy of intelligent analysis is up to 98.4%, which can meet the needs of family health care.

Knock sensing and imaging from structural bending waves

This paper presents a novel knock imaging technique for human-to-things interfaces. It allows any object to use its own surface as an imaging sensor. The methodology is to sense the structural bending waves caused by the knock and reconstruct the intensity image through the beamforming of the wave signals. To verify this, we prepare a thin piezoelectric sensor array on a square plate. The performances are evaluated by measuring the beam peak, beamwidth, and sidelobe level in the reconstructed image. Finally, a real finger knock test is demonstrated, showing an image reconstruction that the position error is only 3 mm.

Piezoelectric Gas Sensors with Polycomposite Coatings in Biomedical Application

When developing methods for diagnosing pathologies and diseases in humans and animals using electronic noses, one of the important trends is the miniaturization of devices, while maintaining significant information for diagnostic purposes. A combination of several sorbents that have unique sorption features of volatile organic compounds (VOCs) on one transducer is a possible option for the miniaturization of sensors for gas analysis. This paper considers the principles of creating polycomposite coatings on the electrodes of piezoelectric quartz resonators, including the choice of sorbents for the formation of sensitive layers, determining the mass and geometry of the formation of sensitive layers in a polycomposite coating, as well as an algorithm for processing the output data of sensors to obtain maximum information about the qualitative and quantitative composition of the gas phase. A comparative analysis of the efficiency and kinetics of VOC vapor sorption by sensors with polycomposite coatings and a set of sensors with relevant single coatings has been carried out. Regression equations have been obtained to predict the molar-specific sensitivity of the microbalance of VOC vapors by a sensor with a polycomposite coating of three sorbents with an error of 5-15% based on the results of the microbalance of VOC vapors on single coatings. A method for creating "visual prints" of sensor signals with polycomposite coatings is shown, with results comparable to those from an array of sensors. The parameters Aij∑ are proposed for obtaining information on the qualitative composition of the gas phase when processing the output data of sensors with polycomposite coatings. A biochemical study of exhaled breath condensate (EBC) samples, a microbiological investigation of calf tracheal washes, and a clinical examination were conducted to assess the presence of bovine respiratory disease (BRD). An analysis of the gas phase over EBC samples with an array of sensors with polycomposite coatings was also carried out. The "visual prints" of the responses of sensors with polycomposite coatings and the results of the identification of VOCs in the gas phase over EBC samples were compared to the results of bacteriological studies of tracheal washes of the studied calves. A connection was found between the parameters Aij∑ of a group of sensors with polycomposite coatings and the biochemical parameters of biosamples. The adequacy of replacing an array of piezoelectric sensors with single coatings by the sensors with polycomposite coatings is shown.

Matrix-addressed crosstalk-free self-powered pressure sensor array based on electrospun isolated PVDF-TrFE cells

Flexible pressure sensor array has attracted much attention in wearable electronics, but still lacks self-power, high resolution, large-area fabrication, and low crosstalk. Herein, we present a self-powered pressure sensor array via patterned piezoelectric polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) nanofibrous. The PVDF-TrFE nanofibers were collected by patterned metal ground electrodes that isolated each piezoelectric cell. The isolated sensing cells can effectively reduce the crosstalk caused by additional deformation. The potential from individual cell is detected through the row+column electrode configuration to determine the corresponding pressure point and trajectory information. The as-fabricated pressure sensor array showed an excellent linear piezoelectric response (2.5 mV/kPa), a fast response time (5 ms), and good repeatability (25,000 cycles). Furthermore, our piezoelectric devices with constructed PVDF-TrFE layer are feasible in scaling up to a large-area sensor array. Accurate recognitions of the letter "h" trajectory tracking and tactile sensation by our self-powered tactile sensor array show potential applications in electronic skin and artificial intelligence. • The piezoelectric sensor array with isolated PVDF-TrFE cell is constructed to cancel out the voltage crosstalk. • The electrospinning is a versatile and low-cost method to produce self-patterned and polarized nanofibers. • The accurate trajectory recognition based on our sensor array is successfully demonstrated.