Piezoresistive Sensor Array Research Articles

Artificial Tactile Sensory Finger for Contact Pattern Identification Based on High Spatiotemporal Piezoresistive Sensor Array.

Human fingertip tactile perception relies on the activation of densely distributed tactile receptors to identify contact patterns in the brain. Despite significant efforts to integrate tactile sensors with machine learning algorithms for recognizing physical patterns on object surfaces, developing a tactile sensing system that emulates human fingertip capabilities for identifying contact patterns with a high spatiotemporal resolution remains a formidable challenge. In this study, we present the development of an artificial tactile finger for accurate contact pattern identification, achieved through the integration of a high spatiotemporal piezoresistive sensor array (PRSA) and a convolutional neural network (CNN) model. Spatiotemporal characterization tests reveal that the artificial finger exhibits a fast temporal resolution of approximately 7 ms and achieves a two-point threshold of 1.5 mm, surpassing that of the human fingertip. To compare the performance of the artificial finger with the human finger in recognizing different patterns, we acquired pressure images by pressing the artificial finger, coated with a flexible PRSA film, onto both simple embossed and complex curved patterns while also recording human recognition results of perceiving these patterns. Experimental findings demonstrate that the artificial finger achieves higher classification accuracy in recognizing both simple and complex patterns (99.0 and 96.1%, respectively) compared to the human fingertip (69.1 and 22.7%). This artificial finger serves as a promising platform with great potential for various robotic tactile sensing applications including prosthetics, skin electronics, and robotic surgery.

A Flexible Double-Sided Curvature Sensor Array for Use in Soft Robotics.

This paper describes the design, fabrication, integration, characterization, and demonstration of a novel flexible double-sided curvature sensor array for use in soft robotics. The paper explores the performance and potential applications of a piezoresistive sensor array consisting of four gold strain gauges on a flexible polyimide (PI) substrate arranged in a Wheatstone bridge configuration. Multiple sensor strips were arranged like the fingers of a hand. Integrating Shape Memory Alloy (SMA) foils alongside the fingers was explored to mimic a human hand-gripping motion controlled with temperature, while curvature sensor array strips measure the resulting finger shapes. Moreover, object sensing in a flexible granular material gripper was demonstrated. The sensors were embedded within Polydimethylsiloxane (PDMS) to enhance their tactile feel and adhesive properties. The findings of this study are promising for future applications, particularly in robotics and prosthetics, as the ability to accurately mimic human hand movements and reconstruct sensor surfaces paves the way for robotic hand functionality.

Heater-Integrated Flexible Piezoresistive Pressure Sensor Array for Smart-Car Seats

Heater-Integrated Flexible Piezoresistive Pressure Sensor Array for Smart-Car Seats

Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array

Flexible pressure sensor arrays have been playing important roles in various applications of human–machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human–machine interaction. However, it remains challenging to simultaneously achieve high spatial and temporal resolution in developing pressure sensor arrays for tactile sensing with robust function to achieve precise signal recognition. This work presents the development of a flexible high spatiotemporal piezoresistive sensor array (PRSA) by coupling with machine learning algorithms to enhance tactile recognition. The sensor employs cross-striped nanocarbon-polymer composite as an active layer, though screen printing manufacture processes. A miniaturized signal readout circuit and transmission board is developed to achieve high-speed acquisition of distributed pressure signals from the PRSA. Test results indicate that the developed PRSA platform simultaneously possesses the characteristics of high spatial resolution up to 1.5 mm, fast temporal resolution of about 5 ms, and long-term durability with a variation of less than 2%. The PRSA platform also exhibits excellent performance in real-time visualization of multi-point touch, mapping embossed shapes, and tracking motion trajectory. To test the performance of PRSA in recognizing different shapes, we acquired pressure images by pressing the finger-type device coated with PRSA film on different embossed shapes and implementing the T-distributed Stochastic Neighbor Embedding model to visualize the distinction between images of different shapes. Then we adopted a one-layer neural network to quantify the discernibility between images of different shapes. The analysis results show that the PRSA could capture the embossed shapes clearly by one contact with high discernibility up to 98.9%. Collectively, the PRSA as a promising platform demonstrates its promising potential for robotic tactile sensing.

Reliable and Scalable Piezoresistive Sensors with an MXene/MoS2 Hierarchical Nanostructure for Health Signals Monitoring.

The increased popularity of wearable electronic devices has led to a greater need for advanced sensors. However, fabricating pressure sensors that are flexible, highly sensitive, robust, and compatible with large-scale fabrication technology is challenging. This work investigates a piezoresistive sensor constructed from an MXene/MoS2 hierarchical nanostructure, which is obtained through an easy and inexpensive fabrication process. The sensor exhibits a high sensitivity of 0.42 kPa-1 (0-1.5 kPa), rapid response (∼36 ms), and remarkable mechanical durability (∼10,000 cycles at 13 kPa). The sensor has been demonstrated to be successful in detecting human motion, speech recognition, and physiological signals, particularly in analyzing human pulse. These data can be used to alert and identify irregularities in human health. Additionally, the sensing units are able to construct sensor arrays of various sizes and configurations, enabling pressure distribution imaging in a variety of application scenarios. This research proposes a cost-effective and scalable approach to fabricating piezoresistive sensors and sensor arrays, which can be utilized for monitoring human health and for use in human-machine interfaces.

Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.

Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by 'feeling' the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing.

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5× 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5×104 cycles, and fast response time of 2.43ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5×5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

Ultralight Smart Patch with Reduced Sensing Array Based on Reduced Graphene Oxide for Hand Gesture Recognition

Flexible sensors for hand gesture recognition and human–machine interface (HMI) applications have witnessed tremendous advancements during the last decades. Current state‐of‐the‐art sensors placed on fingers or embedded into gloves are incapable of fully capturing all hand gestures and are often uncomfortable for the wearer. Herein, a flake‐sphere hybrid structure of reduced graphene oxide (rGO) doped with polystyrene (PS) spheres is fabricated to construct the highly sensitive, fast response, and flexible piezoresistive sensor array, which is ultralight in the weight of only 2.8 g and possesses the remarkable curved‐surface conformability. The flexible wrist‐worn device with a five‐sensing array is used to measure pressure distribution around the wrist for accurate and comfortable hand gesture recognition. The intelligent wristband is able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. To showcase our wristband, a real‐time system is developed to control a robotic hand via the classification results, which further demonstrates the potential of this work for HMI applications.

Flexible Electronic Skin for Monitoring of Grasping State During Robotic Manipulation.

Electronic skin for robotic tactile sensing has been studied extensively over the past years, yet practical applications of electronic skin for the grasping state monitoring during robotic manipulation are still limited. In this study, we present the fabrication and implementation of electronic skin sensor arrays for the detection of unstable grasping. The piezoresistive sensor arrays have the advantages of facile fabrication, fast response, and high reliability. With the tactile data from the sensor array, we propose two quantitative indicators, correlation coefficient and wavelet coefficient, to identify grasping with variable forces and slippage. Those two indicators reflect both time and frequency domain characteristics in the contact forces from the sensor array and can be obtained without large amount of calculation. We demonstrate the utility of this method under various conditions, the results indicate grasping with variable forces, and slippage can be distinguished by this method. The flexible sensor arrays are adopted for tactile sensing on a bionic hand, and the effectiveness of this method in detecting various grasping states has been verified. The electronic skin sensor array and the grasping state monitoring method are promising for applications in robotic dexterous manipulation.

Facile fabrication of flexible piezo-resistive pressure sensor array using reduced graphene oxide foam and silicone elastomer

Electronic skins have attracted significant interest due to their broad applications in our daily lives. Flexible pressure sensors form one of the essential parts of any electronic skin. Achieving high-performance electronic skins requires ways to make pressure sensor arrays with high sensitivity and good flexibility. We report on the facile fabrication of flexible piezo-resistive pressure sensor arrays with excellent mechanical stability, high sensitivity, and a wide working range (0–30 kPa) of pressure using reduced graphene oxide (rGO) on flexible PCB (FPCB). It shows a remarkable sensitivity of 0.13 kPa−1 in the pressure range < 10 kPa and 0.05 kPa−1 in the range of > 10 kPa, covering a broad spectrum of testing conditions. We demonstrated that it could provide a pressure map and intensity, giving tactile information of the pressure exerted on our prototype with a 4 × 4 array.

Submillimeter-Scaled PEDOT:PSS/PPy Piezoresistive Pressure Sensor Array and Its Applications in Biomedicine

Developing a small, smart, and sensitive pressure sensor array is necessary for establishing a comfortable and safe environment in daily life. Herein, the miniaturization effects of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula> poly (3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) piezoresistive pressure sensors with a cross-point electrode (CPE) structure have been investigated and found to suffer from response delays owing to the serious current crowding effect. To eliminate the adverse effects caused by the device scaling, polypyrrole (PPy) was blended into PEDOT:PSS to obtain a high piezoresistive sensitivity of 0.58 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> and a fast relaxation time of 0.74 s for the miniaturized PEDOT:PSS/PPy piezoresistive pressure sensors with a PPy concentration of 60 ppm. Subsequently, the optimized PEDOT:PSS/PPy composites were implemented to fabricate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> CPE piezoresistive sensor arrays with an electrode size of 0.2 mm, and a readout circuit was designed to instantaneously measure the resistance of each pixel of the sensor arrays under the application of pressure. Using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> PEDOT:PSS/PPy piezoresistive sensor arrays with a readout circuit, the pressure distribution of the finger pressing and an occlusal pressure monitoring of a molar before and after the dental filling process were successfully demonstrated, suitable for prospective applications in biomedicine.

A Capacitive and Piezoresistive Hybrid Sensor for Long‐Distance Proximity and Wide‐Range Force Detection in Human–Robot Collaboration

Electronic Skins Safety is a core concern in human-robot collaboration (HRC), and the active safety control has attracted widespread attention. A capacitive and piezoresistive hybrid sensor array (e-skins) for longdistance proximity and wide-range force detection is presented in article 2100213 by Zhenlong Wang, Jie Zhao, PingAn Hu, Jia Zhang and co-workers. The performance for controlling commercial robots in HRC shows the potential usages in complex conditions.

Wireless Rehabilitation Training Sensor Arrays Made with Hot Screen-Imprinted Conductive Hydrogels with a Low Percolation Threshold.

Herein, we propose a highly sensitive wireless rehabilitation training ball with a piezoresistive sensor array for patients with Parkinson's disease (PD). The piezoresistive material is a low percolation threshold conductive hydrogel which is formed with polypyrrole (PPy) nanofibers (NFs) as a conductive filler derived from a polydopamine (PDA) template. The proton acid doping effect and molecular template of PDA are essential for endowing PPy NFs with a high aspect ratio, leading to a low percolation threshold (∼0.78 vol %) and a low Young's 004Dodulus of 37.69 kPa and hence easy deformation. The piezoresistive sensor exhibited a static and dynamic stability of 10,000 s and 15,000 cycle times, respectively. This stability could be attributed to the increased hydrophilicity of conductive fillers, enhancing the interfacial strength between the conductive filler and the matrix. The interaction between the PDA-PPy NFs and the hydrogel matrix endows the hydrogel with toughness and ensures the stability of the device. Additionally, the microdome structure of the conductive hydrogel, produced by hot screen-imprinting, dramatically improves the sensitivity of the piezoresistive sensor (∼856.14 kPa-1). The microdome conductive hydrogel can distinguish a subtle pressure of 15.40 Pa compared to the control hydrogel without a microstructure. The highly sensitive piezoresistive sensor has the potential to monitor the hand-grip force, which is not well controlled by patients with PD. The rehabilitation training ball assembled with a sensor array on the surface and a wireless chip for communication inside is built and used to monitor the pressure in real time through the WeChat applet. Thus, this work has significantly broadened the application of hydrogel-based flexible piezoresistive sensors for human activity monitoring, which provides a promising strategy to realize next-generation electronics.

Design of an array of piezoresistive airflow sensors based on pressure loading mode for simultaneous detection of airflow velocity and direction.

As an irreplaceable element for obtaining airflow information in many engineering scenarios, airflow sensors have gained increasing attention across the fields of aerospace engineering, environmental engineering, sustainable energy exploitation, meteorology research, and so on. As one of the mainstream airflow sensing principles, piezoresistive airflow velocity sensors have experienced rapid growth over the years, while effective vector airflow sensors with the ability of detecting both airflow velocity and direction based on the piezoresistive principle are scarce. Here, on the basis of our developed piezoresistive airflow velocity sensors based on pressure loading mode, we design an array of these sensors and propose a corresponding explicit algorithm for simultaneous detection of airflow velocity and direction. This sensor array configuration enables an automatic recognition function of the quadrant of incoming airflow, which can significantly simplify the reverse calculation of airflow information compared with conventional vector airflow sensors. The experimental results demonstrate the decent performance of this sensor array for identifying both airflow velocity and direction. This study not only fills the gap between our developed airflow velocity sensor and the ability of detecting airflow direction but also presents a simple and universal array-based strategy for vector airflow sensing, which could be widely applied in airflow sensors based on other principles.

Velostat Sensor Array for Object Recognition

This paper presents a cost-effective pressure sensing system for object detection and identification. The pressure sensing system consists of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27\times 27$ </tex-math></inline-formula> piezoresistive sensor array made of carbon composite Velostat, a signal processing subsystem for signal scanning, amplification, registration, and enhancement. A convolutional neural network is used to classify various objects through the pressure signals produced and processed by the sensing array. Based on systematic characterizations and calibrations of sensing materials and system sensitivity, three experiment setups are established to recognize 10 objects to be detected. In series of experiments, a pressure image data set consisting of 32264 frames of images is first assembled to represent the 10 objects. Contrast enhancement algorithm was used to process the pressure image data set and combined with a convolutional neural network ResNet-PI to classify the 10 objects. For pressure images collected with the preestablished three experiment setups, an overall accuracy of 0.9854 is achieved. Compared with other systems based on Velostat sensor array, the system demonstrated in this study features improvements in structural robustness, detection repeatability and system reliability, suggesting its potential applications in emerging areas including human-computer interaction and smart health monitoring.

The Axial Impact Response and Plantar Load Distribution of the Hybrid III and Military Lower Extremity Under Altered Ankle Postures.

Foot injuries as a result of automotive collisions are frequent and impactful. Anthropomorphic test devices (ATDs), used to assess injury risk during impact scenarios such as motor vehicle collisions, typically assess risk of foot/ankle injuries by analyzing data in tibia load cells. The peak axial force and the tibia index are metrics commonly used to evaluate risk of injury to the lower extremity but do not directly account for injury risk to the foot, or the risk of injury associated with out-of-position loading. Two ATDs, the Hybrid III lower leg and the Military Lower Extremity, were exposed to axial impacts at seven different ankle postures. An array of piezoresistive sensors located on the insole of a boot was employed during these tests to assess the load distribution variations among postures and between ATD models on the plantar surface of the foot. Both posture and ATD model affected the load distribution on the foot, highlighting the need for regional injury risk assessments in this vulnerable anatomical region. The increase in forefoot loading during plantarflexion was not reflected in the standard industry metrics of peak axial force or tibia index, suggesting that increased fracture risk to the forefoot would not be detected. The variations in load distribution between the models could also alter injury risk assessment in frontal collisions based on differences in attenuation. These data could be used for regional foot injury assessment and to inform the design of an improved ATD foot.

A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion.

Recent achievements in the field of computer vision, reinforcement learning, and locomotion control have largely extended legged robots’ maneuverability in complex natural environments. However, little research focuses on sensing and analyzing the physical properties of the ground, which is crucial to robots’ locomotion during their interaction with highly irregular profiles, deformable terrains, and slippery surfaces. A biomimetic, flexible, multimodal sole sensor (FMSS) designed for legged robots to identify the ontological status and ground information, such as reaction force mapping, contact situation, terrain, and texture information, to achieve agile maneuvers was innovatively presented in this paper. The FMSS is flexible and large-loaded (20 Pa–800 kPa), designed by integrating a triboelectric sensing coat, embedded piezoelectric sensor, and piezoresistive sensor array. To evaluate the effectiveness and adaptability in different environments, the multimodal sensor was mounted on one of the quadruped robot’s feet and one of the human feet then traversed through different environments in real-world tests. The experiment’s results demonstrated that the FMSS could recognize terrain, texture, hardness, and contact conditions during locomotion effectively and retrain its sensitivity (0.66 kPa−1), robustness, and compliance. The presented work indicates the FMSS’s potential to extend the feasibility and dexterity of tactile perception for state estimation and complex scenario detection.

A Readout Circuit Based on Zero Potential Crosstalk Suppression for a Large Piezoresistive Sensor Array: Case Study Based on a Resistor Model

Although large piezoresistive sensor arrays are vital in obtaining 2D pressure distributions in many applications, they suffer from crosstalk forming parasitic parallel paths as a result of their generic shared row-column architecture. Existing software based algorithms and post processing is the industrially utilized crosstalk suppression mechanism which is below par in meeting the desired measurement accuracy and shape accuracy requirements. However, several hardware based implementations based on the zero potential method have been proposed in literature to mitigate crosstalk and deliver reasonable measurements with repressed crosstalk. Nevertheless, they are often confined to smaller sensor array sizes meeting the requirements of low density applications like robotics where power consumption, design complexity, magnitude of external dimensions, cost of implementation and higher scanning rates are not the governing constraints during the design process. Adopting existing mechanisms directly into scanning large sensor arrays violate the aforementioned design constraints, thus, a novel approach is required. Here, we have proposed and implemented a readout circuit based on zero potential crosstalk suppression, with an improved decoder-transistor based scanning architecture, for a large piezoresistive sensor array containing 30000 sensels meeting the design constraints. The developed circuit was validated using a miniaturized resistor model in conjunction with circuit simulations in PSPICE to determine the propensity of the readout circuit to deliver a measurement accuracy (error less than 1%) while maintaining shape accuracy at the hardware level.

Fabrication of piezoresistive Si nanorod-based pressure sensor arrays: A promising candidate for portable breath monitoring devices

This paper reports on the controlled fabrication of a highly sensitive piezoresistive sensor by using Si nanorod (NR) arrays. An efficient, large-area, scalable strategy was adopted to fabricate the pressure sensors by incorporating chemically etched, high-aspect-ratio, vertical Si NR arrays between two thin Au layers. The piezoresistive properties corresponding to dimension- and position-controlled and randomly etched, closely packed, and thin Si NR arrays were exploited to fabricate the small, portable, and device-compatible pressure sensors. The Si-NR-based piezoresistive sensors exhibited a high sensitivity of 0.49 MPa −1 , thereby demonstrating its superiority over other unconventional piezoresistive nanomaterials such as Si with different configurations of nanostructures. Furthermore, the sensors exhibited a large variation (~45%) in the current at a constant bias voltage of 2 V under a weak applied pressure corresponding to an inert gas flow of 5 sccm. The excellent pressure sensing performance of the piezoresistive Si NRs enabled the efficient detection of changes corresponding to the human breathing pattern. In particular, the key advantages of such pressure sensors is the simple, inexpensive, and scalable fabrication process; high sensitivity with ultra-low-pressure detection; and excellent ambient stability (>several months) with a high durability pertaining to more than 1,000 cycles of pressure loading/unloading. Furthermore, we demonstrated the ability of the pressure sensor to act as a portable human breath sensor to monitor respiratory parameters in a noninvasive and personalized manner. The results can provide direction for the realization of next-generation breath-sensing gadgets and other leading-edge applications in the domain of electronic and healthcare devices. • Chemically etched, dimension- and position-controlled Si nanorod arrays are utilized as pressure sensors. • Piezoresistivity in mesoporous Si nanorod is revealed to achieve a high-pressure sensitivity. • The pressure sensor detects a tiny flow of inert gas efficiently. • Demonstrated the pressure sensor’s ability to act as a portable human breath sensor.

Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity

Moving a finger around the boundary edge is one of the important strategies followed by humans for ascertaining the shape and size of an object by touch. Tactile response from thousands of mechanoreceptors in the human hand offers a high spatiotemporal resolution to perceive the edge orientation quickly (<50 ms) and accurately (acuity of <3°). Inspired by the computational efficiency of biological tactile system, we present a neuromorphic approach to artificial tactile sensing that mimics the spike-based spatiotemporal tactile response of Fast Adapting type I (FA-I) mechanoreceptors. We propose a novel, model-based spatiotemporal correlation matching method to estimate the orientation of the boundary edge while a piezoresistive tactile sensor array attached to robotic arm palpates over the object. Results highlight the ability of the proposed method to efficiently leverage spatial and temporal information, by obtaining very precise orientation estimates (±1.67° error for edges oriented from 10° to 90°, with a step of 5°) in spite of a low-resolution sensor (169 mm2, 4×4 resolution). A comparison with both spatial and spatiotemporal based classifiers indicates that the proposed method achieves 20% lower mean absolute error (MAE) than its closest counterparts, all of which required supervised training. Furthermore, we show that even with a ten-fold loss of spike time precision (1 ms–10 ms), MAE is maintained at 2°. This work highlights that even with a modest sensor size and resolution, spatiotemporal similarity metrics can be used to obtain very precise estimates of orientation. Such an approach has potential applications towards improving the tactile sensing capability in robotic/prosthetic hands where knowledge of spatial edge orientation information is paramount for object manipulation, and sensor contact area is often sparse and small.