Texture Sensor Research Articles

Manufacturing strategies for highly sensitive and self-powered piezoelectric and triboelectric tactile sensors

Abstract Piezoelectric and triboelectric effects are of growing interest for facilitating high-sensitivity and self-powered tactile sensor applications. The working principles of piezoelectric and triboelectric nanogenerators provide strategies for enhancing output voltage signals to achieve high sensitivity. Increasing the piezoelectric constant and surface triboelectric charge density are key factors in this enhancement. Methods such as annealing processes, doping techniques, grain orientation controls, crystallinity controls, and composite structures can effectively enhance the piezoelectric constant. For increasing triboelectric output, surface plasma treatment, charge injection, microstructuring, control of dielectric constant, and structural modification are effective methods. The fabrication methods present significant opportunities in tactile sensor applications. This review article summarizes the overall piezoelectric and triboelectric fabrication processes from materials to device aspects. It highlights applications in pressure, touch, bending, texture, distance, and material recognition sensors. The conclusion section addresses challenges and research opportunities, such as limited flexibility, stretchability, decoupling from multi-stimuli, multifunctional sensors, and data processing.

A soft robotic system imitating the multimodal sensory mechanism of human fingers for intelligent grasping and recognition

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, we have innovatively designed a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing has achieved a recognition accuracy of 98.43 % for different objects. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety.

Healing Function for Abraded Fingerprint Ridges in Tactile Texture Sensors.

Tactile texture sensors are designed to evaluate the sensations felt when a human touches an object. Prior studies have demonstrated the necessity for these sensors to have compliant ridges on their surfaces that mimic human fingerprints. These features enable the simulation of contact phenomena, especially friction and vibration, between human fingertips and objects, enhancing the tactile sensation evaluation. However, the ridges on tactile sensors are susceptible to abrasion damage from repeated use. To date, the healing function of abraded ridges has not been proposed, and its effectiveness needs to be demonstrated. In this study, we investigated whether the signal detection capabilities of a sensor with abraded epidermal ridges could be restored by healing the ridges using polyvinyl chloride plastisol as the sensor material. We developed a prototype tactile sensor with an embedded strain gauge, which was used to repeatedly scan roughness specimens. After more than 1000 measurements, we observed significant deterioration in the sensor's output signal level. The ridges were then reshaped using a mold with a heating function, allowing the sensor to partially regain its original signal levels. This method shows potential for extending the operational lifespan of tactile texture sensors with compliant ridges.

Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM ) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R 2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors.

Food Texture Measurement System Using Rod Type Actuator for Imitation of Human Mastication

Food texture is one of the most important factors in determining the personal palatability of foods, so food companies require food texture measurement and evaluation in developing novel food products. However, instruments are not fast enough or strong enough to imitate human mastication. To measure the textures of different foods, this study proposes a sensor stand that uses a rod-type actuator. The target speed and force of the sensor stand are 100 mm/s and 100 N, respectively. A food texture sensor that imitates the structure of a human tooth is attached to the sensor stand. The sensor stand and a desktop computer make up the measurement system. Using the system, the fundamental characteristics of the sensor stand with the texture sensor are demonstrated. Verification experiments confirm that the sensor stand satisfies the target values of speed and force. Experiments on actual food items also demonstrate the effectiveness of the measurement system in evaluating food textures.

PROXIMATE COMPOSITION AND ORGANOLEPTIC PROPERTIES OF PUMPKIN (Cucurbita moschata)-BASED COMPLEMENTARY FOODS WITH SEVERAL TYPES OF NUTS

Complementary Foods is food given to infants aged 6-24 months. Complementary foods must fulfill the criteria for nutritional needs that have been regulated by the government such as adequacy of macro and micro nutrients. One of the local food ingredients that can be used as complementary foods is pumpkin.Pumpkin is known to contain nutritional components that are quite complete but have a small protein content. Therefore, it is necessary to add nuts as a source of protein. The purpose of this study was to determine the comparison effect of pumpkin puree with various types of nuts on the proximate composition and organoleptic properties of complementary foods. This study used a completely randomized design with one factor, namely the differences in the type of bean puree with five levels of treatments, A1 (35% pumpkin: 35% Bambara bean), A2 (35% pumpkin: 35% kidney bean), A3 (35% pumpkin: 35% mung bean), A4 (35% pumpkin: 35% soybean), and A5 (35% pumpkin: 35% edamame bean). Data analysis using analysis of variance and Kruskall Wallis test. The result showed that the Complementary Foods formulations made with 35% pumpkin puree and 35% soybean puree had the highest nutritional content. The Complementary Foods had 57.25% water content, 2.83% ash content, 20.62% protein content, and 10.49 fat content which had fulfill the Complementary Foods standard except the carbohydrate content. The addition of nuts puree to pumpkin complementary foods had an effect on color and texture sensors and all parameters in the hedonic test.

Smart fingertip sensor for food quality control: Fruit maturity assessment with a magnetic device

Automated technologies for quality inspection of fruits have attracted great interest in the food industry. The development of nondestructive mechanisms to assess the quality of individual fruit prior to sale may lead to an increase in overall product quality, value, and consequently, producer competitiveness. However, the existing methods have limitations. Herein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed. The texture sensor consists of up to 100 magnetized nanocomposite cilia attached to a chip with magnetoresistive sensors in a full Wheatstone bridge architecture. In this paper we demonstrate the use of ciliary sensors in scanning fruits (blueberries and strawberries) in different maturation stages. The contact of the cilia with the fruit skin provided qualitative information about its texture in terms of ripeness stage. Less mature fruits exhibited, on average, a highest peak voltage of 0.14 mV for blueberries and 0.12 mV for strawberries, while overripe fruits exhibited 0.58 mV and 0.56 mV, respectively. The results were confirmed by sensorial assessment of the fruit freshness, and therefore attesting the application potential of the sensing technology for fruit quality control.

A Magnetic Food Texture Sensor and Comparison of the Measurement Data of Chicken Nuggets.

Food texture is one of the important quality indicators in foodstuffs, along with appearance and flavor, contributing to taste and odor. This study proposes a novel magnetic food texture sensor that corresponds to the tactile sensory capacity of the human tooth. The sensor primarily consists of a probe, linear slider, spring, and circuit board. The probe has a cylindrical shape and includes a permanent magnet. Both sides of the spring are fixed to the probe and circuit board. The linear slider enables the smooth, single-axis motion of the probe during food compression. Two magnetoresistive elements and one inductor on the circuit board measured the probe’s motion. A measurement system then translates the measurement data collected by the magnetoresistive elements into compression force by means of a calibration equation. Fundamental experiments were performed to evaluate the range, resolution, repetitive durability of force, and differences in the frequency responses. Furthermore, the sensor was used to measure seven types of chicken nuggets with different coatings. The difference between the force and vibration measurement data is revealed on the basis of the discrimination rate of the nuggets.

Spatio-chromatic information available from different neural layers via Gaussianization

How much visual information about the retinal images can be extracted from the different layers of the visual pathway? This question depends on the complexity of the visual input, the set of transforms applied to this multivariate input, and the noise of the sensors in the considered layer. Separate subsystems (e.g. opponent channels, spatial filters, nonlinearities of the texture sensors) have been suggested to be organized for optimal information transmission. However, the efficiency of these different layers has not been measured when they operate together on colorimetrically calibrated natural images and using multivariate information-theoretic units over the joint spatio-chromatic array of responses.In this work, we present a statistical tool to address this question in an appropriate (multivariate) way. Specifically, we propose an empirical estimate of the information transmitted by the system based on a recent Gaussianization technique. The total correlation measured using the proposed estimator is consistent with predictions based on the analytical Jacobian of a standard spatio-chromatic model of the retina–cortex pathway. If the noise at certain representation is proportional to the dynamic range of the response, and one assumes sensors of equivalent noise level, then transmitted information shows the following trends: (1) progressively deeper representations are better in terms of the amount of captured information, (2) the transmitted information up to the cortical representation follows the probability of natural scenes over the chromatic and achromatic dimensions of the stimulus space, (3) the contribution of spatial transforms to capture visual information is substantially greater than the contribution of chromatic transforms, and (4) nonlinearities of the responses contribute substantially to the transmitted information but less than the linear transforms.

Flexible Multimodal Sensors for Electronic Skin: Principle, Materials, Device, Array Architecture, and Data Acquisition Method

Electronic skin (e-skin) is designed to mimic the comprehensive nature of human skin. Various advances in e-skin continue to drive the development of the multimodal tactile sensor technology on flexible and stretchable platforms. e-skin incorporates pressure, temperature, texture, photographic imaging, and other sensors as well as data acquisition and signal processing units formed on a soft substrate for humanoid robots, wearable devices, and health monitoring electronics that are the most critical applications of soft electronics. This artificial skin has developed very rapidly toward becoming real technology. However, the complex nature of e-skin technology presents significant challenges in terms of materials, devices, sophisticated integration methods, and interference-free data acquisition. These challenges range from functional materials, device architecture, pixel design, array structure, and data acquisition method to multimodal sensing performance with negligible interference. In this article, we present recent research trends and approaches in the field of flexible and stretchable multimodal sensors for e-skin focusing on the following aspects: 1) flexible and stretchable platforms; 2) operating principles and materials suitable for pressure, temperature, strain, photograph, and hairy sensor devices; 3) device and integration architectures, including multimodal single cells, three-axis tactile sensors, vertical-stacked sensor arrays, active matrix sensor arrays, and integration electronics; 4) reliable acquisition methods for various texture sensing and machine-learning algorithms for processing tactile sensing data; and 5) future outlook.

Food Texture Quantification of Tempura Using Magnetic Food Texture Sensor and Time-series Data

Food Texture Quantification of Tempura Using Magnetic Food Texture Sensor and Time-series Data

The perceptually-inspired model of tactile texture sensor based on the inverse-magnetostrictive effect

PurposeThe purpose of this study is to develop an output model to extract surface microstructure characteristics of different objects, so as to predict the response of the output voltage obtained from tactile texture sensor.Design/methodology/approachThe model is based on the consideration of the inverse-magnetostrictive effect, the flexure mode, the linear constitutive equations and the strain principle.FindingsThis research predicts and investigates the effect of the texture properties on the tactile texture sensor output characteristics.Originality/valueThe surface texture characteristic is regarded to be important information to evaluate and recognize the object.

Food texture evaluation using logistic regression model and magnetic food texture sensor

A food texture evaluation method using a magnetic food texture sensor is proposed for the visualization of food texture. The food texture sensor measures two time-series waves, one of force and one of vibration, during fracture of a food sample. Twenty profiles were extracted from the two waves. The evaluation method selected the profiles to use in the logistic model and determined the coefficients of the model based on the results of sensory tests. Laboratory experiments confirmed that the logistic model evaluated the food textures as radar charts. In addition, the model can potentially evaluate the food textures of unknown foods.

Stylus type MEMS texture sensor covered with corrugated diaphragm

In this paper, a stylus type MEMS texture sensor covered with a corrugated palylene diaphragm, which prevent debris from jamming into the sensor without significant degradation of sensitivity and bandwidth, was reported. A new fabrication process using a lost-foil method to make the corrugated diaphragm on a 3-axis piezoresistive force sensor at wafer level has been developed. The texture sensor could detect the surface microstructure as small as about 10 in lateral direction and 1.7 in vertical direction. The output signals from the sensor were repeatable, and the Z-axis signal was almost the same regardless of the moving direction, indicating that the hysteresis was small enough. In addiiton, the bandwidth as wide as 1.1 kHz and 500 Hz in lateral and vertical directions, respectively, were confirmed. The surface roughness and friction coefficient of various materials were successfully detected by the fabricated texture sensor.

A Novel Texture Sensor for Fabric Texture Measurement and Classification

Surface texture is one of the important cues for human beings to identify different fabrics. This paper presents a novel design of a surface texture sensor by imitating human active texture perception by touch. A thin polyvinylidene fluoride (PVDF) film is used as the sensitive element to fabricate a high-accuracy, high-speed-response fabric surface texture sensor, and a mechanism is designed to produce the relative motion at a certain speed between the texture sensor and the surface of the perceived fabric with constant contact force. Thus, the surface texture property can be measured as the output charge of the PVDF film of the sensor induced by the small height/depth variation of the moving fabric surface. A texture feature extraction method by compressing the zero value spectral lines in frequency domain is proposed. In addition, a radial basis function (RBF) neural networks based on unsupervised K-means clustering algorithm is used as classifier for texture recognition. The experiments show that the proposed texture sensor is effective in detecting the feature signals of fabric surface textures, which are suitable for the RBF networks to classify the different fabrics.

A MULTIFUNCTIONAL AND PORTABLE OPTICAL SENSOR FOR QUANTITATIVE CHARACTERIZATION OF 3D SURFACE TEXTURE PROPERTIES

Surface characterization technologies are generally sorted into two categories: noncontact and contact-based technologies. Among these technologies, no one can stand out to simultaneously and rapidly measure both surface patterns/textures and mechanical properties such as softness, friction, and mechanical impedance. In this paper, we have addressed this problem and developed a multifunctional and portable surface texture sensor through combination of both contact and noncontact optical surface profiling mechanisms. The developed sensor relying on an optomechanical principle can be efficiently used for quantitative characterization of surface texture properties including 3D texture pattern, roughness, and even mechanical properties like softness, etc. As one of the important applications, we have used the sensor to measure and analyze texture properties of extensive automotive interior leather sample surfaces. The results demonstrate that the sensor can effectively assist the interior designer to quantify and classify essential texture features of automobile interior surfaces.

Profile-based roughness discrimination with pen-type texture sensor

Tactile discriminations of surface roughness using artificial sensors have been challenging. The modeling methods and parameters that have been using to describe the mechanical properties of rough surface are insufficient for haptic roughness. This paper proposes a method to characterize surface roughness based on the profiles of the surface. A compact handheld pen-type texture sensor with a right probe is developed for the measurement of surface profiles. Based on the contact force and the motion of the senor, profiles in the paths of scanning are estimated. The height variations of a profile are converted to a series of tactile stimuli to represent the contact stimulations in haptic explorations. The mean and the standard deviation of the amplitudes of stimuli are identified as haptic features that indicate the required tangential force to slide on the rough surface and how rough the surface is, respectively. Experiments show that the roughness on four kinds of sandpapers can be clearly distinguished by the proposed discrimination method.

Reconstruction of surface texture based on spatial information measured with a pen-type texture sensor

Surface texture is one of the important properties for the human to identify objects by touch. Effective reconstructions of textures are necessary for realistic interactions between the human and environment via human–computer interfaces. This paper presents a systematic approach for sensing and reconstructing periodic surface textures. Three significant issues are discussed: a pen-type texture sensor that measures the spatial information based on the measurements of contact forces; an algorithm for the reconstruction of periodic textures based on the obtained spatial information; and the method of incremental scanning to identify the polar spectrum of a surface by limited number of scans. The concept of polar spectrum is introduced to describe the spatial properties of the surface, that is, the relation between spatial frequencies and the direction of measurement. The pattern of polar spectrum is used to facilitate surface reconstructions. Experimental results based on the spatial information obtained with a laser displacement sensor and the pen-type texture sensor demonstrate the effectiveness of the proposed methods for the measurement and reconstruction of periodic textures.

Texture sensor for a prosthetic hand

This research has shown for the first time that the physical dimensions of small surface patterns in an object surface are detectable from sensors integrated into the fingertip and mechanical links of a prosthetic hand. A further novel aspect of this work is the use of the standard deviation of data used in the analysis.Charge amplifiers are used to extract the signal from the piezoelectric sensors when an object that has two grating surfaces moves past a fingertip.Similar signals have been observed from all the sensors. An analysis of the data has shown that the repeating pattern from the gratings is detectable from a calculation of the mean standard deviation. An estimate of the grating widths can also be made from this analysis. Approximately 32 and 58 grooves are in contact with fingertip (width 15 mm) representing a resolution of 2 grooves and 4 grooves mm−1 respectively.

1P2-N-105 ヒト指の構造と知覚機構を模倣した触感センサ(触覚と力覚3,生活を支援するロボメカ技術のメガインテグレーション)

1P2-N-105 ヒト指の構造と知覚機構を模倣した触感センサ(触覚と力覚3,生活を支援するロボメカ技術のメガインテグレーション)