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A Capacitive Tactile Sensor Array for Surface Texture Discrimination
Abstract
This paper presents a silicon MEMS based capacitive sensing array, which has the ability to resolve forces in the sub mN range, provides directional response to applied loading and has the ability to differentiate between surface textures. Texture recognition is achieved by scanning surfaces over the sensing array and assessing the frequency spectrum of the sensor outputs.
... The early pressure mapping sensors were fabricated on rigid (non-flexible) substrates which are limited to the planar end-effector geometry and certain pressure/force. To overcome this limitation, research has been focused on the development of tactile sensors on various flexible substrates composed of polymers, metal foils and fabrics [6][7][8][9][10][11][12][13][14][15][16][17][18]. To achieve all the requirements of tactile sensors with high spatial resolution and pressure mapping, under low operating power conditions which conforms given geometry of the end-effector, microelectromechanical systems were shown to be a viable platform to develop advanced flexible tactile sensors. ...
... These issues require frequent calibration, and the life cycle of the sensor is limited by the operation and frequency of usage. To overcome the limitations and drawbacks of the piezoresistive sensors, the capacitive sensing modality was introduced [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The mechanism of the capacitive sensor includes the measurements of the change in capacitance (ΔC) with a change in distance (d) between two parallel plates (electrodes) with the application of a pressure/force. ...
... X-SENSORS AG developed a capacitive pressure measurement using a conductive fabric and foam dielectric material [6]. Muhammad et al incorporated a capacitive sensor in a CMOS using silicon substrates which is not conformable and limited by end-effector geometry [10,11]. Pritchard et al fabricated flexible capacitive pressure sensors on a polyimide film, with Parylene-C as an active sensing element which operated in a low-pressure regime [9]. ...
A method for high resolution tactile sensing for robotic end-effectors used in variable environmental conditions is required for practical robotic applications, such as heavy industry, construction, military and space applications. In this work, a robust, flexible tactile sensor based on a capacitive sensing mechanism with high sensitivity and stability that can operate between -60 and 120oC was developed. The active sensing thin film was composed of a 2:2 connective polymer-ceramic laminar composite. A stress-sensitive elastomer (Arathane 5753 A/B) was used as the primary compliant layer within the laminar architecture, and a HfO2 thin film was used as the dominating dielectric layer to improve the sensitivity of the sensor. The sensors were fabricated on a flexible polyimide film (Kapton) to conform to the end-effector geometry. The fabricated sensor showed good sensitivity and cycle stability (between 0 and 360 kPa). The capacitance change due to temperature variations were studied in detail. Three different capacitive sensor architectures were developed to study the influence of HfO2 layer on the sensitivity of the sensor.
Thermomechanical loading cycles were performed with in situ electrical acquisition to characterize the sensor. Chemical and structural characterization of the HfO2 layer deposited on a flexible substrate was implemented using conductive atomic force microscopy (c-AFM), Raman spectroscopy and x-ray photoelectron spectroscopy (XPS), and the optical properties were analyzed by ultra-violet visible spectrophotometer (UV-Vis).
... Their underlying technologies are varied and can be based on task-dependent designs. Some examples of existing tactile sensor technologies include capacitive sensors [2], piezoelectric sensors [3], piezoresistive sensors [4], quantum tunnelling composites [5], and optical sensors [1,6]. ...
... • w max : This parameter is an upper boundary for synaptic weights. We optimise it over a range of [1][2][3][4][5][6] We optimise the STDP parameters through Bayesian optimisation using the python hyperopt package. The final optimised parameter values of the network are displayed in Table 2. ...
Dexterous manipulation in robotic hands relies on an accurate sense of artificial touch. Here we investigate neuromorphic tactile sensation with an event-based optical tactile sensor combined with spiking neural networks for edge orientation detection. The sensor incorporates an event-based vision system (mini-eDVS) into a low-form factor artificial fingertip (the NeuroTac). The processing of tactile information is performed through a Spiking Neural Network with unsupervised Spike-Timing-Dependent Plasticity (STDP) learning, and the resultant output is classified with a 3-nearest neighbours classifier. Edge orientations were classified in 10-degree increments while tapping vertically downward and sliding horizontally across the edge. In both cases, we demonstrate that the sensor is able to reliably detect edge orientation, and could lead to accurate, bio-inspired, tactile processing in robotics and prosthetics applications.
... When actively touching, it could quantitatively evaluate the ridge texture with a spatial period difference as low as 40 µm (400, 440, 480 µm spatial periods, 1 mm ridge height). H.B. Muhammad et al. [12] proposed a capacitive tactile sensor array based on silicon MEMS, which resolved forces in the sub-mN range and successfully distinguished fine ridge textures with ridge heights of 200 µm and spatial periods as low as 200 µm. However, the research mentioned above on object surface information detection mainly focused on the estimation of the spatial period of ridge textures in the micron range and did not reach the capability to detect fine textures with ridge heights of several microns, such as human fingers [13,14]. ...
... In the existing research on fine texture detection using tactile technology, HB Muhammad et al. [12] successfully distinguished ridge texture samples with a ridge height of 200 µm and a spatial period as low as 200 µm. Yasutoshi Takekawa et al. [17] succeeded in obtaining the spatial period and ridge height information of ridge texture samples with ridge heights as low as 25 µm. ...
Surface texture information plays an important role in the cognition and manipulation of an object. Vision and touch are the two main methods for extracting an object’s surface texture information. However, vision is often limited since the viewing angle is uncertain during manipulation. In this article, we propose a fine surface texture detection method based on a stochastic resonance algorithm through a novel solid–liquid composite flexible tactile sensor array. A thin flexible layer and solid–liquid composite conduction structure on the sensor effectively reduce the attenuation of the contact force and enhance the sensitivity of the sensor. A series of ridge texture samples with different heights (0.9, 4, 10 μm), different widths (0.3, 0.5, 0.7, 1 mm), but the same spatial period (2 mm) of ridges were used in the experiment. The experimental results prove that the stochastic resonance algorithm can significantly improve the signal characteristic of the output signal of the sensor. The sensor has the capability to detect fine ridge texture information. The mean relative error of the estimation for the spatial period was 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. The sensing capability to sense a fine texture of tactile senor surpasses the limit of human fingers.
... A simple implementation of a capacitive sensor is presented in [28], wherein a capacitive sensor was mounted on a gripper to detect contact with the manipulated object and stop the movement. The advantages of capacitive sensors were further exploited in [51][52][53], wherein capacitive sensors were used either alone or together with other sensors to explore the texture properties of fabric materials. ...
... Capacitive tactile sensors were used in [51,53] to discriminate between different textures. Khan et al. [53] used an array of 16 capacitive tactile sensors, and by using the data from each sensor as inputs in an SVM, they managed to obtain close to perfect results. ...
While in most industries, most processes are automated and human workers have either been replaced by robots or work alongside them, fewer changes have occurred in industries that use limp materials, like fabrics, clothes, and garments, than might be expected with today’s technological evolution. Integration of robots in these industries is a relatively demanding and challenging task, mostly because of the natural and mechanical properties of limp materials. In this review, information on sensors that have been used in fabric-handling applications is gathered, analyzed, and organized based on criteria such as their working principle and the task they are designed to support. Categorization and related works are presented in tables and figures so someone who is interested in developing automated fabric-handling applications can easily get useful information and ideas, at least regarding the necessary sensors for the most common handling tasks. Finally, we hope this work will inspire researchers to design new sensor concepts that could promote automation in the industry and boost the robotization of domestic chores involving with flexible materials.
... Capacitive sensors have also been widely used for contactbased tactile sensing with use cases in texture recognition [31] and for creating tactile artificial skins [32], [33], which can serve as a robotic skin for collision monitoring [34]. Capacitive sensors can be designed and integrated into artificial robotic skins to measure forces applied to the robot's body [35], [36], [37]. ...
... . 30: SendToActuators(α t ). 31: C. Karen Liu is an associate professor in the Computer Science Department at Stanford University. She received her Ph.D. degree in Computer Science from the University of Washington. ...
Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. This study introduces a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a multi-electrode capacitive sensor array mounted on a robot's end effector to estimate the relative position and orientation (pose) of a nearby human limb. Capacitive servoing then uses these human pose estimates from a data-driven pose estimator within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. We provide a design overview of capacitive sensors for human-robot interaction and then investigate the performance and generalization of capacitive servoing through an experiment with 12 human participants. The results indicate that multidimensional capacitive servoing enables a robot's end effector to move proximally or distally along human limbs while adapting to human pose. Using a cross-validation experiment, results further show that capacitive servoing generalizes well across people with different body size.
... Existing micro-displacement sensors are mainly piezoresistive, capacitive, piezoelectric, etc. [1]. Among them, the piezoresistive micro-displacement sensor reflects fluctuation in external stress based on the piezoresistive effect [2][3][4][5][6][7]. The capacitive micro-displacement sensor changes the capacitance of the sensitive element by altering the capacitive pad spacing caused by external stress and then reflects the texture information of the surface of the measured object [8][9][10][11][12]. ...
Two-dimensional (2D) piezoelectric semiconductor materials are garnering significant attention in applications such as intelligent sensing and energy harvesting due to their exceptional physical and chemical properties. Among these, molybdenum disulfide (MoS2), a 2D wide-bandgap semiconductor, exhibits piezoelectricity in odd-layered structures due to the absence of an inversion symmetry center. In this study, we present a straightforward chemical vapor deposition (CVD) technique to synthesize monolayer MoS2 on a Si/SiO2 substrate, achieving a lateral size of approximately 50 µm. Second-harmonic generation (SHG) characterization confirms the non-centrosymmetric crystal structure of the wide-bandgap MoS2, indicative of its piezoelectric properties. We successfully transferred the triangular MoS2 to a polyethylene terephthalate (PET) flexible substrate using a wet-transfer method and developed a wide-bandgap MoS2-based micro-displacement sensor employing maskless lithography and hot evaporation techniques. Our testing revealed a piezoelectric response current of 5.12 nA in the sensor under a strain of 0.003% along the armchair direction of the monolayer MoS2. Furthermore, the sensor exhibited a near-linear relationship between the piezoelectric response current and the strain within a displacement range of 40–100 µm, with a calculated response sensitivity of 1.154 µA/%. This research introduces a novel micro-displacement sensor, offering potential for advanced surface texture sensing in various applications.
... The field of tactile sensor technologies includes capacitive sensors [53], piezoelectric sensors [54], piezoresistive sensors [55], quantum tunneling composites [56], and optical sensors [57,58]. These sensors come in all sizes and shapes, while some are commercially available and have been used for robotic manipulations. ...
Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation , development of prosthetics, and soft wearables to find engineering solutions for the human body. Fiber-optic-based sensors have recently become an indispensable part of bio-mechatronics systems, which are essential for position detection and control, monitoring measurements, compliance control, and various feedback applications. As a result, significant advancements have been introduced for designing and developing fiber-optic-based sensors in the past decade. This review discusses recent technological advancements in fiber-optical sensors, which have been potentially adapted for numerous bio-mechatronic applications. It also encompasses fundamental principles, different types of fiber-optical sensors based on recent development strategies, and characterizations of fiber Bragg gratings, optical fiber force myography, polymer optical fibers, optical tactile sensors, and Fabry-Perot interferometric applications. Hence, robust knowledge can be obtained regarding the technological enhancements in fiber-optical sensors for bio-mechatronics-based interdisciplinary developments. Therefore, this review offers a comprehensive exploration of recent technological advances in fiber-optical sensors for bio-mechatronics. It provides insights into their potential to revolutionize biomedical and bio-mechatronics applications, ultimately contributing to improved patient outcomes and healthcare innovation.
... Various types of tactile sensors, including capacitive, piezoresistive, optical sensors and even tactile features, like surface temperature and vibration, have been employed for texture recognition in robotic systems [60,[152][153][154]. These sensors can measure the physical interactions between the robot's end effector and the object's surface, allowing the robotic system to extract essential features for texture classification [155]. ...
Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.
... The integration of textiles with sensing technologies will facilitate the rapid development of nextgeneration wearable bioelectronics for widespread applications, such as power supply Chen et al., 2016;Zhang, N. et al., 2020), mobile healthcare Libanori et al., 2022;Meng et al., 2020), human-machine interfacing Zhou et al., 2020), artificial intelligence Liu et al., 2022;Su et al., 2021a), and personal thermoregulation Cai et al., 2017;Fang et al., 2021b;Peng et al., 2018). Until now, wearable technologies that detect and monitor biomechanical pressure have relied on the piezoresistive (Pan et al., 2014(Pan et al., , 2020, capacitive (Muhammad et al., 2011), piezoelectric (Su et al., 2021b), triboelectric (Yi et al., 2015) and magnetoelastic effects Chen et al., 2021c;Zhao et al., 2021;Zhou et al., 2021). Among these, piezoresistive sensors, which couple resistivity variation with geometric deformation upon external stress, have attracted considerable attention due to their facile architecture, high sensitivity, and easy signal-processing, as well as a dual function of static-dynamic-state monitoring (Rim et al., 2016;Wei et al., 2021). ...
Electronic textiles are fundamentally changing the way we live. However, the inability to effectively recycle them is a considerable burden to the environment. In this study, we developed a cotton fiber-based piezoresistive textile (CF p-textile) for biomonitoring which is biocompatible, biodegradable, and environmentally friendly. These CF p-textiles were fabricated using a scalable dip-coating method to adhere MXene flakes to porous cotton cellulose fibers. The adhesion is made stronger by strong hydrogen bonding between MXene flakes and hierarchically porous cotton cellulose fibers. This cotton-fiber system provides a high sensitivity of 17.73 kPa-1 in a wide pressure range (100 Pa-30 kPa), a 2 Pa subtle pressure detection limit, fast response/recovery time (80/40 ms), and good cycle stability (over 5, 000 cycles). With its compelling sensing performance, the CF p-textile can detect various human biomechanical activities, including pulsation, muscle movement, and swallowing, while still being comfortable to wear. Moreover, the cotton cellulose is decomposed into low-molecular weight cellulose or glucose as a result of the 1,4-glycosidic bond breakage when exposed to acid or during natural degradation, which allows the electronic textile to be biodegradable. This work offers an ecologically-benign, cost-effective and facile approach to fabricating high-performance wearable bioelectronics.
... An e-skin simultaneously mimicking SA-and RA-mechanoreceptors can analyze objects based on their physical properties, such as the surface texture, sense of shape, pressure, and dynamic or static strain 10 . To mimic these functionalities of the human skin, several sensing mechanisms have been employed for the development of an e-skin such as ion-channel systems 11,12 , transistors 13,14 , capacitive 15,16 , piezoresistive 17,18 , triboelectric 19,20 , and piezoelectric-based sensors 21,22 . ...
Human skin contains slowly adaptive (SA) and rapidly adaptive (RA) mechanoreceptors, which respond differently to external stimuli. Based on human tactile perception principles, the fabrication of a self-powered electronic skin (e-skin) that simultaneously mimics SA- and RA-mechanoreceptors is a prime need for robots and artificial prosthetics to interact with the surrounding environment. However, the complex process of merging multimode sensors to mimic SA- and RA-mechanoreceptors hinders their utilization in e-skins. We proposed SA- and RA-mechanoreceptors based on n-type and semi-insulating GaN nanowire arrays. The SA- and RA-mechanoreceptors demonstrated distinguished features such as grasping of objects and detection of their surface textures. Based on piezoelectric sensing principles, the proposed e-skin can simultaneously mimic static and dynamic pressure signals. Mechanoreceptors further detected several stimuli of various pressures with low and high frequencies. The response and reset times showed by SA-mechanoreceptors were 11 and 18 ms under 1-Hz frequency, which are rapid enough for practical e-skin applications.
... Flexible pressure sensors are indispensable in e-skin because they not only transform the external pressure stimuli into the corresponding electronic signals, but can also hold pressure-sensing ability under arbitrary deformation [7,8]. Pressure sensors can be divided into the following categories on the basis of their sensing mechanism: resistive [9][10][11][12][13][14], piezoelectric [15][16][17][18], triboelectric [19][20][21], optical [22], and capacitive [10,13,[23][24][25][26][27][28]. Among all these pressure sensors, capacitive pressure sensors could provide the advantages of relatively high sensitivity, fast response time, and low power consumption, so that capacitive pressure sensors have become a better choice in terms of e-skin and wearable devices [29,30]. ...
Electronic skin (E-skin) has attracted much attention in smart wearables, prosthetics, and robotics. The capacitive-type pressure sensor is generally regarded as one good option to design tactile sensing devices owing to its superior sensitivity in low-pressure region, fast response time and convenient manufacturing. Introducing microstructures on electrode surface is an effective approach to achieve highly sensitive capacitive pressure sensors. In this work, an electromechanical model is proposed to build the relationship between capacitance change and compressive force. The present model can predict the sensitivity of capacitive pressure sensor with microstructured electrodes, where each cellular microstructure is modeled using the contact mechanics theory. It is the first time in the literature that based on Hertz theory framework, one rigorous electromechanical theory framework is established to model flexible capacitive pressure sensor, and the model can be extended to other microstructures, such as micro-pyramid, micro-pillar, and micro-dome array. The validation indicates that the analytical results well agree with the experimental data from our previous work and other literatures. Moreover, the present model can well capture the sensitivity of pressure sensor on the beginning range of small pressure. The sensitivity on this range is the most significant for the E-skin due to its robust linearity for one pressure sensor. Besides, we analyzed the compressive force-displacement relationship, the compressive force-contact radius relationship and the influences of the geometrical and material parameters on the electromechanical coupling effect. The results show that the height and the Young’s modulus of the soft dielectric layer are regarded as the dominant influencing factors in the sensitivity of capacitive pressure sensors.
... Therefore, for decades, the development of flexible tactile sensors without sacrificing the performance has been attracting much attention from global researchers [5][6][7][8]. Various mechanisms of tactile sensors were developed and showed promising properties, including piezoelectric [9][10][11], piezoresistive [12][13][14][15][16], triboelectric [17], capacitive [18][19][20] and optical [21][22][23] mechanisms, etc Among these different mechanisms, the piezoelectric sensor exhibits high sensitivity and quick response to a wide range of dynamic mechanical 1 Author to whom any correspondence should be addressed stimulation despite that it can barely response to static force, which means the output of piezoelectrical material to a static force would evenly drop to zero in a short time. In addition, the self-power property can simplify the system of the piezoelectric tactile sensor, which is particularly important in the application of limit spaces, such as robotic finger tips. ...
... Such applications require both accurate measurement of applied force, and the possibility of direct attachment onto the surface of objects such as organs or skin for wearable and implantable devices. Multiple approaches to artificial tactile sensors were investigated, using methods based on piezoresistive [6], capacitive [7], inductive [8], and piezoelectric [9] effects. Each of the sensing methodologies has advantages and disadvantages in tactile sensing [10]; however, among the sensing approaches, piezoelectric-based tactile sensors have particularly high sensitivity, a high dynamic range, and a high frequency response [11]. ...
This research focuses on the development of a flexible tactile sensor array consisting of aluminum nitride (AlN) based on micro-electro-mechanical system (MEMS) technology. A total of 2304 tactile sensors were integrated into a small area of 2.5 × 2.5 cm2. Five hundred nm thick AlN film with strong c-axis texture was sputtered on Cr/Au/Cr (50/50/5 nm) layers as the sacrificial layer coated on a Si wafer. To achieve device flexibility, polydimethylsiloxane (PDMS) polymer and SU-8 photoresist layer were used as the supporting layers after etching away a release layer. Twenty-five mM (3-mercaptopropyl) trimethoxysilane (MPTMS) improves the adhesion between metal and polymers due to formation of a self-assembled monolayer (SAM) on the surface of the top electrode. The flexible tactile sensor has 8 × 8 channels and each channel has 36 sensor elements with nine SU-8 bump blocks. The tactile sensor array was demonstrated to be flexible by bending 90 degrees. The tactile sensor array was demonstrated to show clear spatial resolution through detecting the distinct electrical response of each channel under local mechanical stimulus.
... Recently, pressure sensors based on piezoresistivity, 12−15 capacitance, 16−18 or piezoelectricity, 19−21 or integrated with a field-effect transistor structure 22,23 have demonstrated high sensitivity and great flexibility. In particular, capacitive pressure sensors, which transduce mechanical strain into a capacitance change, have attracted much attention because of the advantages of the simple governing equation, 8,24 low power consumption, 8,25,26 and negligible temperature fluctuations. 27,28 Generally, elastomers are adopted as dielectric materials for their great elasticity and compressibility, which strongly affects the sensitivity and working range of a capacitive sensor. ...
Flexible pressure sensors play important roles in electronic skins (E-Skins), which mimic the mechanical forces sensing properties of human skin. A rational design for a pressure sensor with adjustable characteristics is in high demand for different application scenarios. Here, we present tunable, ultrasensitive, and flexible pressure sensors based on compressible wrinkled microstructures. Modifying the morphology of the polydimethylsiloxane (PDMS) microstructure enables the device to obtain different sensitivities and pressure ranges for different requirements. Further, by intentionally introducing hollow structures in the PDMS wrinkles, our pressure sensor exhibits an ultra-high sensitivity of 14.268 kPa⁻¹. The elastic microstructure-based capacitive sensor also possesses a very low detectable pressure limit (1.5 Pa), a fast response time (<50 ms), a wide pressure range and excellent cycling stability. Implementing respiratory monitoring and vocalization recognition is realized by attaching the flexible pressure sensor onto the chest and throat, respectively, showing its great application potential for disease diagnosis, monitoring, and other advanced clinical/biological wearable technologies.
... It is believed to be brought about by two mechanisms: 1) spatial coding of geometric properties for coarse textures and 2) vibrotactile coding for fine textures ([5]- [7]).To achieve texture recogniton in robotic systems, tactile sensors exploiting several different physical phenomenon have been proposed. These include capacitive ( [8]), piezoresistive ( [9], [10]), magnetic ( [11]), piezoelectric ( [12]) and optical ( [13]). All these sensors have their own share of advantages and disadvantages ( [14]). ...
In the era of ubiquitous computing with flourished visual displays in our surroundings, the application of haptic feedback technology still remains in its infancy. Bridging the gap between haptic technology and the real world to enable ambient haptic feedback on various physical surfaces is a grand challenge in the field of human-computer interaction. This paper presents the concept of an active electronic skin, characterized by three features: richness (multi-modal haptic stimuli), interactivity (bi-directional sensing and actuation capabilities), and invisibility (transparent, ultra-thin, flexible, and stretchable). By deploying this skin on physical surfaces, dynamic and versatile multi-modal haptic display, as well as tactile sensing, can be achieved. The potential applications of this skin include two categories: skin for the physical world (such as intelligent home, intelligent car, and intelligent museum), and skin for the digital world (such as haptic screen, wearable device, and bare-hand device). Furthermore, existing skin-based haptic display technologies including texture, thermal, and vibrotactile feedback are surveyed, as well as multidimensional tactile sensing techniques. By analyzing the gaps between current technologies and the goal of ambient haptics, future research topics are proposed, encompassing fundamental theoretical research on the physiological and psychological perception mechanisms of human skin, spatial-temporal registration among multimodal haptic stimuli, integration between sensing and actuation, and spatial-temporal registration between visual and haptic display. This concept of active electronic skin is promising for advancing the field of ambient haptics, enabling seamless integration of touch into our digital and physical surroundings.
With the rapid advancement of modern technology, flexible and wearable electronics, particularly electromechanical sensors, have gained considerable attention for motion‐based applications in medical health monitoring and artificial intelligence. Correspondingly, extensive efforts have been dedicated to enhancing their performance and practicality. Electromechanical sensors based on functional composites and structures accurately detect and monitor the body, generating the corresponding output signals. However, there are several limitations in manufacturing composite sensors, such as the selection and combination of functional materials, geometrical structures, constructed conductive pathways, stability of output signals, and operational lifetime. Thus, this review summarizes notable trends in the electromechanical sensors using functional composites and structures. This also provides an overview of different types of electromechanical pressure and strain sensors, exploring their operational mechanisms regarding triboelectricity, piezoelectricity, piezocapacitance, and piezoresistivity. The unique characteristics of functional materials, including conductive polymers, nanostructured metals, and carbon nanomaterials and composites, are analyzed alongside various design concepts for highly flexible and stretchable sensors. Furthermore, potential applications concerning human motion and human–machine interfaces are also recommended. Additionally, several future outlooks are reviewed for insights into future prospects and strategies. Thus, this review can assist readers in understanding current electromechanical devices more accurately.
Clamping is a necessary ability to perform operational tasks for flexible fingers. However, when flexible fingers clamp an object, an excessive contact force can damage it, and a small contact force can cause it to slide. Therefore, real-time monitoring of the contact state is the key to clamping the object stably and nondestructively. This paper develops a monitoring system for the contact state of flexible fingers based on fiber Bragg grating (FBG). The mapping relationship between the FBG wavelength shift and the normal contact force derived from the fingertip contact model can determine the critical slip point during the clamping process. Algorithms based on wavelet transform and maximum root mean square difference (MRD) are proposed to monitor and predict the critical slip point (MPCSP) in FBG wavelength signal.Experiments in flexible fingers clamping objects with different shapes, stiffness, and mass show that the critical and predicted slip points are about 0.4 s and 1.0 s earlier than the sliding. Moreover, feedback and adjustment of the contact force at the critical and predicted slip points can realize the stableclamping. The research results have important guiding significance on the feedback control of the nondestructive grasping for soft fingers.
Tactile sensing technology is crucial for soft grippers. Soft grippers equipped with intelligent tactile sensing systems based on various sensors can interact safely with the unstructured environments and obtain precise properties of objects (e.g., size and shape). It is essential to develop state‐of‐the‐art sensing technologies for soft grippers to handle different grasping tasks. In this review, the development of tactile sensing techniques for robotic hands is first introduced. Then, the principles and structures of different types of sensors normally adopted in soft grippers, including capacitive tactile sensors, piezoresistive tactile sensors, piezoelectric tactile sensors, fiber Bragg grating (FBG) sensors, vision‐based tactile sensors, triboelectric tactile sensors, and other advanced sensors developed recently are briefly presented. Furthermore, sensing modalities and methodologies for soft grippers are also described in aspects of force measurement, perception of object properties, slip detection, and fusion of perception. The application scenarios of soft grippers are also summarized based on these advanced sensing technologies. Finally, the challenges of tactile sensing technologies for soft grippers that need to be tackled are discussed and perspectives in addressing these challenges are pointed out.
Contact lens sensors provide a noninvasive approach for intraocular pressure (IOP) monitoring in patients with glaucoma. Accurate measurement of this imperceptible pressure variation requires highly sensitive sensors in the absence of simultaneously amplifying IOP signal and blinking-induced noise. However, current noise-reduction methods rely on external filter circuits, which thicken contact lenses and reduce signal quality. Here, we introduce a contact lens strain sensor with an anti-jamming ability by utilizing a self-lubricating layer to reduce the coefficient of friction (COF) to remove the interference from the tangential force. The sensor achieves exceptionally high sensitivity due to the strain concentration layout and the confined occurrence of sympatric microcracks. The animal tests prove our lens can accurately detect IOP safely and reliably.
Human-like tactile perception is critical for promoting robotic intelligence. However, reproducing tangential “sliding” perception of human skin is still struggling. Inspired by the lateral gating mechanosensing mechanism of mechanosensory cells, which perceives mechanical stimuli by lateral tension–induced opening-closing of ion channels, we report a robot skin (R-skin) with mechanically gated electron channels, achieving ultrasensitive and fast-response sliding tactile perception via pyramidal artificial fingerprint-triggered opening-closing of electron gates (E-gates, namely, customized V-shaped cracks within embedded mesh electron channels). By imitating cytomembrane to modulate membrane mechanics, local strain is enhanced at E-gates to effectively regulate electron pathways for high sensitivity while weakened at other positions to suppress random cracks for robust stability. The R-skin can directly recognize ultrafine surface microstructure (5 μm) at a response frequency (485 Hz) outshining humans and achieve human-like sliding perception functions, including dexterously distinguishing texture of complex-shaped objects and providing real-time feedback for grasping.
The hydrogel iontronic sensor (HIS) has attracted much attention in recent years due to its high sensitivity. The physical model to analyze the effects of various parameters on the sensitivity of the HIS is still lacking. In this work, we conduct experiments to study the effects of voltage frequency, sensor size, and ion concentration on the sensitivity of the HIS. The experimental results show that the sensitivity is highly dependent on frequency in the range of 20~1MHz. We establish a theoretical model consisting of Possion-Nerust-Planck equations to describe the ion migration and incompressible Neo-Hookean constitutive equation to describe the hydrogel deformation. The theoretical results divide the sensitivity into three regions of frequency: the region dominated by fully formed EDLs, the region dominated by ionic relaxation of the hydrogel, and the region dominated by the dielectric property of hydrogel. The model agrees well with the experiments on the frequency dependence of sensitivity as well as the effect of size and ion concentration. This work may provide a guidance for the design of highly sensitive hydrogel iontronic sensor.
Commonly encountered problems in the manipulation of objects with robotic hands are the contact force control and the setting of approaching motion. Microelectromechanical systems (MEMS) sensors on robots offer several solutions to these problems along with new capabilities. In this review, we analyze tactile, force and/or pressure sensors produced by MEMS technologies including off-the-shelf products such as MEMS barometric sensors. Alone or in conjunction with other sensors, MEMS platforms are considered very promising for robots to detect the contact forces, slippage and the distance to the objects for effective dexterous manipulation. We briefly reviewed several sensing mechanisms and principles, such as capacitive, resistive, piezoresistive and triboelectric, combined with new flexible materials technologies including polymers processing and MEMS-embedded textiles for flexible and snake robots. We demonstrated that without taking up extra space and at the same time remaining lightweight, several MEMS sensors can be integrated into robotic hands to simulate human fingers, gripping, hardness and stiffness sensations. MEMS have high potential of enabling new generation microactuators, microsensors, micro miniature motion-systems (e.g., microrobots) that will be indispensable for health, security, safety and environmental protection.
Toward the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. This study introduces a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a multielectrode capacitive sensor array mounted on a robot's end effector to estimate the relative position and orientation (pose) of a nearby human limb. Capacitive servoing then uses these human pose estimates from a data-driven pose estimator within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. We provide a design overview of capacitive sensors for human–robot interaction and then investigate the performance and generalization of capacitive servoing through an experiment with 12 human participants. The results indicate that multidimensional capacitive servoing enables a robot's end effector to move proximally or distally along human limbs while adapting to human pose. Using a cross-validation experiment, results further show that capacitive servoing generalizes well across people with different body size.
This article develops a novel approach for generalized predictive controller (GPC) tuning of multiple-input- multiple-output (MIMO) systems using an improved fuzzy logic and the event-triggered mechanism (ETM). First, the slope of the system output is considered as a new fuzzy target parameter to construct the fuzzy logic algorithm, based on which an improved fuzzy logic based GPC online tuning method is proposed. Second, in order to save the computation and communication resources for the online tuning of GPC parameters, ETM is further introduced to avoid unnecessary updates. Third, given the modified fuzzy algorithm and the aperiodic sampling framework caused by the ETM, the stability property of the closed-loop system under the proposed method is proved theoretically. Finally, the advantages of the developed technique are illustrated via a machine-furnace coordination system.
Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pulse pressure sensors have attracted keen research interest and have been widely used for long-term and real-time cardiovascular status monitoring. Owing to their compelling characteristics, including light weight, cost-effectiveness, wear comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pulse sensors for wearable health monitoring. In this review, we present an overview of wearable pressure sensors for human pulse wave monitoring, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. We first outline the conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. We then summarize the exemplary applications of pulse wave measurement based on microengineering structured devices. Finally, we conclude with a discussion of the opportunities and challenges that wearable pulse pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare. This article is protected by copyright. All rights reserved.
DESCRIPTION
Studying signals from natural sources and systems, and learning how Mother Nature conducts signal processing, can lead to the design of novel artificial devices. These sometime follow the naturally occurring designs at scales, which are extremely small (similar to naturally occurring designs). Devices that are built after learning from naturally occurring designs are better known to be “bioinspired” and they are outliers to some of the traditional sensor designs for engineering systems. This translation of naturally offered solutions to man-made processes/technologies have huge implications in solving very challenging problems. In this chapter, we have highlighted some of the recently developed sensors based on biomimetic strategies. Examples that are discussed are flow sensors, tactile sensors, and microphones based on the sensing mechanisms in animals such as cavefish, crickets, insects, etc. Apart from this, the various categories into which the biomimetic sensors can be classified are related to acoustic, chemical, electric, optical, magnetic, mechanical, radiation, and thermal domains. The article describes one sensor in each category in some level of detail to motivate the readers to understand the possibilities that may emerge by looking around and looking into various manifestations of Mother Nature.
Human-friendly robot designs are often inspired by human joints that exhibit lightweight, dexterity, and large compressive load capacity. However, there is a significant problem when attaching sensors to contact joints inspired by human joints. Attaching traditional sensors for obtaining information on a joint is substantially complicated by the skewed rotation axes. To solve this problem, we propose a novel contact joint sensor suitable for 3D curved surfaces. The proposed contact joint sensor is composed of a contact resistance force-sensor module for obtaining distributed pressure measurements utilized to estimate the joint information via a learning method. Each force-sensor array arranged in the desired shape on a 3D curved surface measures the surface pressure transmitted through a heterogeneous force-transmit layer. The learning-based model estimates the joint angle and torque values while maintaining the estimation performance even under varying load conditions. We validated the proposed contact joint sensor with experiments involving various load conditions. The average root-mean-squared error (RMSE) values of the flexion/extension and radial/ulnar rotation angles are
$2.2\,^{\circ }$
and
$1.7\,^{\circ }$
, respectively. In addition, estimations of the torque and tension at the contact joint show good agreement with the reference values despite changes in the load conditions.
Spiking neural network (SNN) utilizes spike trains for information processing among neurons, which is more biologically plausible and widely regarded as the third-generation artificial neural network (ANN). It has the potential for effectively processing spatial-temporal information and has the characteristics of lower power consumption and smaller calculation load compared with conventional ANNs. In this work, we demonstrate the feasibility of applying SNN to classify tactile signals collected by a bionic artificial fingertip that touches a group of real-world metal surfaces with different roughness levels. A two-layer SNN is adopted and trained using an unsupervised learning method with spike-timing-dependent plasticity (STDP). Experiments show that the trained SNN can categorize the input tactile signals into different surface roughness of metal textures with more than 80% accuracy. This work lays the foundation of applying SNNs to more complex tactile signal processing in robotics, manufacturing, and other engineering fields.
An electronic skin (ES) is developed by embedding a liquid-core poly(vinylidene fluoride) fiber into a silicone rubber. The experimental results show that the ES can detect the waveform, frequency, amplitude, and other parameters of the surface vibration pressure. The ES can sense the surface pressure amplitude over a range of 1.5–2.5 kPa and exhibits a sensitivity of 0.0472 fC/Pa when the pressure is less than 60 Pa. The resonant frequency of the ES is 0.4 Hz. The ES can also detect the elongation strain, and its sensitivity is 0.0058 fC/με. The ES has the characteristics of flexibility, high sensitivity, and a wide measuring range. Therefore, the ES can be used as a robot finger skin, which enables the robot to have touch perception capabilities.
This paper proposes a novel flexible tactile sensor array for dynamic triaxial force measurement. Piezoresistive nanofibers, which are fabricated via the electrospinning method and are composed of multi-wall carbon nanotubes (MWCNTs) and thermoplastic polyurethane (TPU), are selected as the sensing material. The sensitive piezoresistive layer, which has micron-scale thickness, is wrapped in polydimethylsiloxane (PDMS) with a bumped surface. This structure not only detects triaxial forces of different magnitudes and frequencies, but also can recognize the shape, size, and curvature of external objects using multiple measuring points of the sensor. When the sensor is subjected to normal forces at different frequencies, the measuring voltage shows good responses with variations of less than 1.21 %. When the sensor is subjected to shear forces, the coefficient of variation is less than 2.05%. In addition, when the sensor is stressed at multiple measuring points, the voltage response errors between the different points are less than 5.29 %. The sensor shows high sensitivity (with a resistance change that can reach four orders of magnitude) and high response-speeds (response within 62 ms) in validation experiments. The proposed flexible sensor is efficient in triaxial force detection and has the potential for application to prosthetic hands and wearable devices.
For the past two decades, the researches on stretchable physical sensors have made great technological advances, and have been applied to various applications such as electronic skin for robots, haptic devices, bionics, and wearable/implantable healthcare sensors, etc. The deformable physical sensors have been investigated in two approaches: the electronic sensors with the well-developed fabrication technologies and the iontronic sensors as a new technological alternative. There has been a wide spectrum of researches branching from the two approaches. They have evolved from simple-structured sensors with a single function into high-resolution array sensors with multiple functions. A variety of technological methods and principles have been explored depending on the target applications and the materials in use. The deformable sensors can be differentiated according to the specific methodology and principle; i) type of electrical signal (resistance or impedance, capacitance, induction, voltage or current, frequency), ii) power consumption (use of external power (passive) or self-signaling (active)), iii) data acquisition method (time-division multiple access or event-driven parallel access), and iv) multiple functionality (combination of different sensing units or multimodality). It is not clear at this stage which approach and method are suitable for which applications and materials. This review begins with a comparison of the technological methodologies and summarizes the evolution of deformable physical sensors. Focusing on key conceptual achievements according to the technological methodologies, this review introduces the advances of the electronic sensors in section 2 and the iontronic sensors in section 3. Section 4 discusses the challenges and directions for future researches, along with some possible technological solutions.
Because of high sensitivity, mechanical robustness, lightweight and wearability, flexible capacitive pressure transducer has been widely considered one of the most critical soft electronics in wearable consumables and e-skins. The enhancement of the pressure sensitivity of a flexible capacitive sensor relies on the introduction of interfacial microstructure to the dielectric layer. We demonstrate a new methodology to fabricate flexible capacitive sensors with copper-plated polyimide (PI) films as the electrodes and a porous polydimethylsiloxane (PDMS) layer 3D printed via the direct-ink-writing approach. Time-of-flight secondary ion mass spectrometry is developed to optimize the electroless copper plated PI films. What is further examined is the impact of the geometric complexity of the cellular PDMS structure, including filament width, spacing and alignment, on sensitivity, repeatability and reliability of the developed capacitive sensor. A robotic gripper equipped with our flexible pressure sensor showcases its competence to grip a soft target with well-posed force control. It is expected that our proposed sensor design and manufacturing methodology will advance the development of soft electronics and wearable sensors.
Biosensors worn on or implanted in eyes have been garnering substantial attention since being proven to be an effective means to acquire critical biomarkers for monitoring the states of ophthalmic disease, diabetes. Among these disorders, glaucoma, the second leading cause of blindness globally, usually results in irreversible blindness. Continuous intraocular pressure (IOP) monitoring is considered as an effective measure, which provides a comprehensive view of IOP changes that is beyond reach for the “snapshots” measurements by clinical tonometry. However, to satisfy the applications in ophthalmology, the development of IOP sensors are required to be prepared with biocompatible, miniature, transparent, wireless and battery‐free features, which are still challenging with many current fabrication processes. In this work, the recent advances in this field are reviewed by categorizing these devices into wearable and implantable IOP sensors. The materials and structures exploited for engineering these IOP devices are presented. Additionally, their working principle, performance, and the potential risk that materials and device architectures may pose to ocular tissue are discussed. This review should be valuable for preferable structure design, device fabrication, performance optimization, and reducing potential risk of these devices. It is significant for the development of future practical IOP sensors.
Rationally designed pressure sensors for target applications have been in increasing demand. Capacitive pressure sensors with microstructured dielectrics demonstrate a high capability of meeting this demand due to their wide versatility and high tunability by manipulating dielectric layer material and microstructure geometry. However, to streamline the design and fabrication of desirable sensors, a better understanding of how material microstructure and properties of the dielectric layer affect performance is vital. The ability to predict trends in sensor design and performance simplifies the process of designing and fabricating sensors for various applications. A series of equations are presented that can be used to predict trends in initial capacitance, capacitance change, and sensitivity based on dielectric constant and compressive modulus of the dielectric material and base length, interstructural separation, and height of the dielectric layer microstructures. The efficacy of this model has been experimentally and computationally confirmed. The model was then used to illuminate, qualitatively and quantitatively, the relationships between these key material properties and microstructure geometries. Finally, this model demonstrates high tunability and simple implementation for predictive sensor performance for a wide range of designs to help meet the growing demand for highly specialized sensors.
Rapid growth of flexible/wearable electronic devices has encouraged the swift development in the direction of fabricating high performance flexible super-
capacitors with superior electrochemical performance. Recently, graphene based
electrodes have received a great deal of attention for flexible supercapacitors on
account of their outstanding properties; including excellent mechanical flexibility,
enormous surface area and high electrical conductivity. This chapter summarizes
the recent research progress on flexible supercapacitors and identifies the existing challenges related to preparation of electrodes and their device fabrication with regulated electrical and electrochemical properties. Furthermore, it draws attention towards the recent flexible prototype supercapacitors, in plane supercapacitors and fiber-type supercapacitors development. Moreover, it aims for insightful under-standing on opportunities and challenges, endowing stimulation of further research progress in this fascinating field.
Current developments being made in upper limb prostheses are focused on replacing lost sensory information to the amputees. Providing sensory stimulation from the prosthesis can directly improve control over the prosthetic and provide a sense of body ownership. The focus of this review article is on recent developments while including foundational knowledge for some of the critical concepts in neural prostheses. Reported concepts follow the flow of information from sensors to signal processing, with emphasis on texture recognition, and then to sensory stimulation strategies that reestablish the lost sensory feedback loop. Prosthetic sensors are used to detect the physical environment, converting pressure, force, and position into electrical signals. The electrical signals can then be processed in an effort to identify the surrounding environment using distinctive characteristics such as stiffness and texture. In order for the amputee to use this information in a natural manner, there must be real-time sensory stimulation, perception, and motor control of the prosthesis. Although truly complete sensory replacement has not yet been realized, some basic percepts can be partially restored, allowing progress towards a more realistic prosthesis with natural sensations.
The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings.
Pressure sensors play an integral role in a wide range of applications, such as soft robotics and health monitoring. In order to meet this demand, many groups microengineer the active layer—the layer that deforms under pressure and dictates changes in the output signal—of capacitive, resistive/piezoresistive, piezoelectric, and triboelectric pressure sensors in order to improve sensor performance. Geometric microengineering of the active layer has been shown to improve performance parameters such as sensitivity, dynamic range, limit of detection, and response and relaxation times. There are a wide range of implemented designs, including microdomes, micropyramids, lines or microridges, papillae, microspheres, micropores, and microcylinders, each offering different advantages for a particular application. It is important to compare the techniques by which the microengineered active layers are designed and fabricated as they may provide additional insights on compatibility and sensing range limits. To evaluate each fabrication method, it is critical to take into account the active layer uniformity, ease of fabrication, shape and size versatility and tunability, and scalability of both the device and the fabrication process. By better understanding how microengineering techniques and design compares, pressure sensors can be targetedly designed and implemented. Geometrically microengineering the active layer of pressure sensors is an increasingly popular approach to tuning and improving performance. This work evaluates and compares designs and their effects on performance parameters, including sensitivity, dynamic range, limit of detection, and response and relaxation times along with the advantages of a wide range of fabrication techniques to achieve these designs.
There is an increasing demand on the fabrication of robust, flexible, cost-effective, and eco-friendly self-supporting materials, such as yarns, fibers, papers-like, films, and monoliths, etc., due to their promising applications in flexible sensing fields. With the unique structure and outstanding properties, various functional materials with zero-dimensional, one-dimensional, and two-dimensional structures have been employed as promising building blocks for the assembly of self-supporting materials. In this chapter, the type, key parameter, and working principles of sensors are presented. Meanwhile, we summarize the sensing properties of different self-supporting materials through detailed cases. In addition, the challenges and opportunities of current sensors based on self-supporting materials are briefly discussed.
Recent advancements in nanotechnology largely enabled fabrication of plasmonic nanostructures of desired structural features and substantially improved the sensitivity and selectivity of the conventional optical sensing techniques. The plasmonic nanostructure mitigates the limitation of weak scattering cross-section in Raman spectroscopy via electromagnetic as well as chemical enhancement mechanism. The plasmonic nanostructure combined with the Raman spectroscopy technique, popularly known surface-enhanced Raman scattering spectroscopy, has been now established as an effective tool for molecular finger printing of analyte molecule and find applications diverse areas, ranging from biosensors to art. This chapter explains the mechanism behind the surface-enhanced Raman scattering spectroscopy with an emphasis on the factors contributing towards the enhancement in the Raman signal. Further, an account of the difference between conventional and surface enhanced Raman spectroscopy is presented. The role of hot spots and the rationale behind the choice of metal nanoparticles for surface-enhanced Raman scattering substrates is described. In addition, various approaches adopted for the fabrication of substrates in 1D, 2D, and 3D is explained in detail. A detailed account of a few emerging areas wherein this technique finds applications is also given in the chapter.
There is an increasing demand for specialized pressure sensors in various applications. Previously, capacitive pressure sensors have been shown to have wide versatility in use, with a high degree of potential tuning possible through manipulating the geometry or material selection of the dielectric layer. However, in order to make sensors that are tunable and predictable, the design and fabrication method first needs to be developed. Presented here is an improved fabrication method to achieve tunable, consistent, and reproducible pressure sensors by using a pyramid microstructured dielectric layer along with a lamination layer. The as‐produced sensor performance is able to match predicted trends. Further, a simple model based on this system is developed and its efficacy is experimentally confirmed. Then, the model to predict a wide range of material and microstructure geometric properties prior to device fabrication is used to provide trends on sensor performance. Finally, it is demonstrated that the new method can be used to targetedly design a pressure sensor for a specific application—in vitro pulse sensing. Tunable pressure sensors are achievable by geometric manipulation or material selection. To achieve this, tunable and reproducible pressure sensors are fabricated and modeled using a simple model based on microstructure properties. The model can be used to predict the effects of a wide range of material properties on sensor performance. Using this, targeted sensors for specific applications can be achieved.
This paper presents a power-generating sensor array in a flexible and stretchable form. The proposed device is composed of a woven structure that provides various features, including a capacitive tactile sensor, piezoresistive strain sensor, triboelectric energy harvester, and piezoelectric energy harvester. The device is implemented in a textile structure using functional threads implemented with lead zirconate, carbon nanotube, polydimethylsiloxane, and silver nanowire (Ag NW). A stretching force can be detected by measuring the resistance change in the Ag NW composite layer on each thread. Further, the magnitude and location of the vertical force can be detected by measuring the capacitance variation on each capacitive cell that is formed by the gap between two Ag NW layers at the crossing points of each weft and warp thread. For the energy harvesters, the maximum power was measured as 108 μW at 3 MΩ from the triboelectric energy harvesting when the device was pushed in the vertical direction. When a stretching force was applied, a maximum of 60.3 μW at 1 MΩ was measured from the piezoelectric energy harvester.
Sensitivity and linearity are important performance metrics of flexible sensors in application. An effective approach towards improving the performance of capacitive pressure sensors (CPS) is the appropriate design of the microstructure of the dielectric layer. Using the finite element modeling in an integration of Abaqus and COMSOL Multiphysics, we propose a methodology to simulate the deformation and capacitance responses of CPS upon external pressure; the numerical results agree well with experimental data. With the attempt to improve the performance of widely used pyramidal and cylindrical microstructure-based CPS, the effects of microstructure geometric parameters and mechanical property of materials such as the elastic modulus, length of hemline, sidewall angle, height and size on the pressure response were investigated, and the sensitivity and nonlinear error were also analyzed. It has been found that the sensitivity and linearity are more sensitive to elastic modulus, and less sensitive to height. With the same sensitivity, the cylinders-based CPS have higher linearity, while the pyramids-based CPS have larger pressure measuring range. The obtained results could provide reference information for the design of CPS with improved application characteristics.
Functional prosthetics hands which have the ability to help amputees perform tasks in daily life have been developed over many years. These hands need a control system which is fed information from sensors mounted on a prosthetic hand and human–machine interface. A variety of sensors therefore been developed for the prosthetic hand to measure fingertip force, joint angle (position), object slip, texture and temperature. However, most of the strain/stress sensors are attached to the fingertip. In this paper, the potential positions for strain sensors on the side of the finger link of the prosthetic hand are investigated that, in the future, will allow for force control in a lateral or key grip. With modified links of a Southampton Hand, some promising positions for strain sensors have been determined. On some of the links, the strain sensor can be used as an indicator to show the angle of the finger during a curling operation.
Inspired by the epidermal–dermal and outer microstructures of the human fingerprint, a novel flexible sensor device is designed to improve haptic perception and surface texture recognition, which is consisted of single-walled carbon nanotubes, polyethylene, and polydimethylsiloxane with interlocked and outer micropyramid arrays. The sensor shows high pressure sensitivity (−3.26 kPa−1 in the pressure range of 0−300 Pa), and it can detect the shear force changes induced by the dynamic interaction between the outer micropyramid structure on the sensor and the tested material surface, and the minimum dimension of the microstripe that can be discerned is as low as 15 µm × 15 µm (interval × width). To demonstrate the texture discrimination capability, the sensors are tested for accurately discerning various surface textures, such as the textures of different fabrics, Braille characters, the inverted pyramid patterns, which will have great potential in robot skins and haptic perception, etc.
Three experiments are reported bearing on Katz’s hypothesis that tactile texture perception is mediated by vibrational cues
in the case of fine textures and by spatial cues in the case of coarse textures. Psychophysical responses when abrasive surfaces
moved across the skin were compared with those obtained during static touch, which does not provide vibrational cues. Experiment
1 used two-interval forced-choice procedures to measure discrimination of surfaces. Fine surfaces that were readily discriminated
when moved across the skin became indistinguishable in the absence of movement; coarse surfaces, however, were equally discriminable
in moving and stationary conditions. This was shown not to result from any inherently greater difficulty of fine-texture discrimination.
Experiments 2 and 3 used free magnitude estimation to obtain a more comprehensive picture of the effect of movement on texture
(roughness) perception. Without movement, perception was seriously degraded (the psychophysical magnitude function was flattened)
for textures with element sizes below 100 p,m; above this point, however, the elimination of movement produced an overall
decrease in roughness, but not in the slope of the magnitude function. Thus, two components of stimulation (presumably vibrational
and spatial) contribute to texture perception, as Katz maintained; mechanisms for responding to the latter appear to be engaged
at texture element sizes down to 100 ^m, a surprisingly small value.
A compliant 2×2 tactile sensor array was developed and investigated for roughness encoding. State of the art cross shape 3D MEMS sensors were integrated with polymeric packaging providing in total 16 sensitive elements to external mechanical stimuli in an area of about 20 mm(2), similarly to the SA1 innervation density in humans. Experimental analysis of the bio-inspired tactile sensor array was performed by using ridged surfaces, with spatial periods from 2.6 mm to 4.1 mm, which were indented with regulated 1N normal force and stroked at constant sliding velocity from 15 mm/s to 48 mm/s. A repeatable and expected frequency shift of the sensor outputs depending on the applied stimulus and on its scanning velocity was observed between 3.66 Hz and 18.46 Hz with an overall maximum error of 1.7%. The tactile sensor could also perform contact imaging during static stimulus indentation. The experiments demonstrated the suitability of this approach for the design of a roughness encoding tactile sensor for an artificial fingerpad.
The performance of robotic and prosthetic hands in unstructured environments is severely limited by their having little or no tactile information compared to the rich tactile feedback of the human hand. We are developing a novel, robust tactile sensor array that mimics the mechanical properties and distributed touch receptors of the human fingertip. It consists of a rigid core surrounded by a weakly conductive fluid contained within an elastomeric skin. The sensor uses the deformable properties of the finger pad as part of the transduction process. Multiple electrodes are mounted on the surface of the rigid core and connected to impedance-measuring circuitry safely embedded within the core. External forces deform the fluid path around the electrodes, resulting in a distributed pattern of impedance changes containing information about those forces and the objects that applied them. Here we describe means to optimize the dynamic range of individual electrode sensors by texturing the inner surface of the silicone skin. Forces ranging from 0.1 to 30N produced impedances ranging from 5 to 1000kΩ. Spatial resolution (50Hz) appeared to be limited only by the viscoelastic properties of the silicone elastomeric skin.
Human subjects scaled gratings of alternating grooves and ridges for perceived roughness. Roughness increased with an increase in groove width and decreased with an increase in ridge width, but the effect of groove width was much greater than the effect of ridge width. In corresponding neurophysiological experiments, the gratings were moved sinusoidally across the receptive fields of single mechano-receptive afferents innervating the fingerpads of anesthetized monkeys. The measure of response used was the mean cyclic discharge rate (averaged over one cycle of the sinusoid). Slowly adapting afferents (SAs), rapidly adapting afferents (RAs), and Pacinian afferents (PCs) all showed a marked increase in response when groove width increased. An increase in ridge width had no consistent effect on the responses of SAs or RAs but resulted in a small decrease in the response of PCs. The response to a smooth surface differed significantly from the responses to the finer gratings only for the RAs. An alternative measure of response (the number of impulses elicited by each spatial cycle of the grating) increased with an increase in ridge width for all 3 fiber types. Thus, the large effect of groove width on perceived roughness can be accounted for by the mean cyclic discharge rate in the active afferent fibers. The smaller effect of ridge width can be accounted for by the number of impulses per spatial cycle of the grating.
Tactile pattern recognition depends on form and texture perception. A principal dimension of texture perception is roughness, the neural coding of which was the focus of this study. Previous studies have shown that perceived roughness is not based on neural activity in the Pacinian or cutaneous slowly adapting type II (SAII) neural responses or on mean impulse rate or temporal patterning in the cutaneous slowly adapting type I (SAI) or rapidly adapting (RA) discharge evoked by a textured surface. However, those studies found very high correlations between roughness scaling by humans and measures of spatial variation in SAI and RA firing rates. The present study used textured surfaces composed of dots of varying height (280-620 micron) and diameter (0.25-2.5 mm) in psychophysical and neurophysiological experiments. RA responses were affected least by the range of dot diameters and heights that produced the widest variation in perceived roughness, and these responses could not account for the psychophysical data. In contrast, spatial variation in SAI impulse rate was correlated closely with perceived roughness over the whole stimulus range, and a single measure of SAI spatial variation accounts for the psychophysical data in this (0.974 correlation) and two previous studies. Analyses based on the possibility that perceived roughness depends on both afferent types suggest that if the RA response plays a role in roughness perception, it is one of mild inhibition. These data reinforce the hypothesis that SAI afferents are mainly responsible for information about form and texture whereas RA afferents are mainly responsible for information about flutter, slip, and motion across the skin surface.
The importance of movement in texture perception by touch has long been appreciated, but problematic issues related to the utilization of information during active touch persist. How is the repeated demonstration of active–passive equivalence in perceptual sensitivity to felt texture to be interpreted? What does such equivalence imply about the source(s) of information used to make perceptual judgements of texture? What sort of perceptual subsystem is organized to recover texture by touch? What is the role played by the action system in this recovery? The seminal observations of David Katz and J.J. Gibson are recounted and contemporary research is described. Several unresolved issues are outlined, as are several approaches by which the availability and utilization of information within this particular perceptual-action subsystem can be empirically studied.
This paper presents the development of a MEMS based capacitive tactile sensor intended to be incorporated into a tactile array as the core element of a biomimetic fingerpad. The use of standard microfabrication technologies in realising the device allowed a cost efficient fabrication involving only a few process steps. A low noise readout electronics system was developed for measuring the sensor response. The performance of both bare and packaged sensors was evaluated by direct probing of individual capacitive sensor units and characterising their response to load–unload indentation cycles.
This paper reviews the various technologies available for rapid prototyping including stereolithography, selective laser sintering, laminated object manufacturing, fused deposition modelling, multi-jet modelling, three-dimensional printing. It also covers surface roughness considerations and mechanical properties including dimensional accuracy and compares costs of various systems and general trends in equipment performance.
Combined psychophysical and neurophysiological studies have shown that the perceived roughness of surfaces with element spacings of >1 mm is based on spatial variation in the firing rates of slowly adapting type 1 (SA1) afferents (mean absolute difference in firing rates between SA1 afferents with receptive fields separated by approximately 2 mm). The question addressed here is whether this mechanism accounts for the perceived roughness of surfaces with element spacings of <1 mm. Twenty triangular and trapezoidal gratings plus a smooth surface were used as stimulus patterns [spatial periods, 0.1-2.0 mm; groove widths (GWs), 0.1-2.0 mm; and ridge widths (RWs), 0-1.0 mm]. In the human psychophysical studies, we found that the following equation described the mean roughness magnitude estimates of the subjects accurately (0.99 correlation): 0.2 + 1.6GW - 0.5RW - 0.25GW(2). In the neurophysiological studies, these surfaces were scanned across the receptive fields of SA1, rapidly adapting, and Pacinian (PC) afferents, innervating the glabrous skin of anesthetized macaque monkeys. SA1 spatial variation was highly correlated (0.97) with human roughness judgments. There was no consistent relationship between PC responses and roughness judgments; PC afferents responded strongly and almost equally to all of the patterns. Spatial variation in SA1 firing rates is the only neural code that accounts for the perceived roughness of surfaces with finely and coarsely spaced elements. When surface elements are widely spaced, the spatial variation in firing rates is determined primarily by the surface pattern; when the elements are finely spaced, the variation in firing rates between SA1 afferents is determined by stochastic variation in spike rates.
A batch-fabricated solid-state capacitive pressure transducer has been developed using silicon integrated-circuit technology. The fabricated devices exhibit a dynamic range of 350 mmHg and a pressure sensitivity of about 1100 ppm/mmHg. The temperature coefficient of zero-pressure offset is about +50 ppm/°C (less than 0.05 mmHg/°C) and the temperature coefficient of pressure sensitivity over the -20 to +50°C temperature span is about +275 ppm/°C (less than 0.04 mmHg/°C) when the device is used with an open or vacuum-sealed reference cavity. These temperature coefficients are substantially lower than those of previously reported monolithic devices and are low enough that expensive temperature trims can be eliminated for many applications.
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