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Abstract A built-in capacitive proximity sensing method for a charge-induction electrostatic film actuator is proposed. This actuator consists of two thin sheets that function as a stator and a slider. A stator is an insulating sheet with many strips of electrodes in it, whereas a slider is a dielectric sheet that has slight conductivity on its surface. By applying actuation voltage on stator electrodes, the slider that is placed on the stator is driven by electrostatic force. This research realized the simultaneous actuation and proximity sensing using the same electrodes by integrating a resonance-based capacitance measurement circuit into a driving circuit. The study investigated the impact of having a slider on sensing performance, confirming the feasibility of simultaneous sensing and driving. The implemented system achieved an interactive actuation that changed driving velocity according to the proximity distance of the human hand.

The performance of the capacitive gap-sensing system plays a critical role in a satellite-based gravity gradiometer that is developed using an electrostatic accelerometer. The capacitive sensing gain mainly depends on the stabilized injection bias amplitude, the gain of the transformer bridge, and the trans-impedance amplifier. Previous studies have indicated that amplitude noise is the main factor influencing the noise of capacitive displacement detection. Analyzing the capacitive gap-sensing system indicates that the amplitude, frequency, phase, and broadband noises of the stabilized injection bias have varying levels of influence on the performance of the detection system. This paper establishes a model to clarify the mentioned effects. The validation of the sub-tests demonstrates that the analysis and evaluation results of various noise coefficients are highly consistent with the model's predicted outcomes. [ABSTRACT FROM AUTHOR]

In the space gravitational wave detection mission, inertial sensors play the role of providing an inertial reference for the laser interferometric measurement system. Among them, the capacitance sensor serves as the core key technology of the inertial sensor, used to measure the relative position of the test mass (TM) in the electrode cage. The capacitance sensor utilizes synchronous demodulation technology to extract signals from the AC induction signal. When the phase of the demodulation switch signal is aligned, the synchronous demodulator can most effectively filter out noise, thus directly influencing the performance of the capacitance sensor. However, since the TM is in a suspended state, the information read by the capacitance sensor is dynamic, which increases the difficulty of demodulation phase alignment. In light of this, a method is proposed for achieving the phase alignment of the demodulation switch signal in a dynamic environment. This is accomplished by adjusting the phase of the demodulation switch signal, and subsequently computing the phase difference between the AC induction signal and the demodulation switch signal. At the same time, a measurement and evaluation method for phase deviation is also proposed. Ultimately, an automatic phase alignment system for the demodulation switch signal in dynamic environments is successfully implemented on an FPGA platform, and tests are conducted on a hexapod PI console platform to simulate dynamic environments. The experimental results demonstrate that the system accurately achieves phase alignment in the static environment, with a phase deviation of 0.1394 rad. In the simulated dynamic environment, the phase deviation is 0.1395 rad. [ABSTRACT FROM AUTHOR]

Carbon nanotube–glue composite gel-based surface-type elastic sensors with a cylindrical shape deformable (flexible) metallic body were fabricated for tactile pressure and compressive displacement sensing. The fabrication of the sensors was performed using the rubbing-in technique. The effect of the pressure and the compressive displacement on the capacitance and the impedance of the sensors were investigated at various frequencies (in the range of 1 kHz to 200 kHz). It was found that under the effect of pressure from 0 to 9 g/cm2, the capacitance increased by 1.86 and 1.78 times, while the impedance decreased by 1.84 and 1.71 times at the frequencies of 1 kHz to 200 kHz, respectively. The effect of displacement on the impedance and the capacitance of the device was also investigated at various frequencies from 1 kHz to 200 kHz. The results showed that under the effect of compressive displacement up to 25 µm, the impedance of the sensors decreased on average by 1.19 times, while the capacitance increased by 1.09 times, accordingly. The frequency response of the displacement sensor showed that it matched with the low-pass filter. The obtained results are explained based on changes in the shape and geometrical parameters of the cylindrical-shaped conductive body. These results have also been explained on the basis of the distance between the conductive plates of the capacitive sensors during compression, which takes place under the effect of applied pressure or displacement. Moreover, the design of the sensors is simple and easy to fabricate, and their use is also earthy. The fabricated sensors have great potential for commercialization. [ABSTRACT FROM AUTHOR]

Abstract Rollers and wheels are widely used in industry and transportation, but there is seldom direct information about contact forces. A smart roller is introduced which provides real‐time pressure measurements from a soft, elastomer‐coated cylinder. The roller is designed for automated fiber placement (AFP) machines, which are widely used in the aerospace industry to manufacture complex composite parts. For optimum process performance, real‐time feedback is highly desirable for detecting flaws during manufacturing. The sensor replaces the elastomer outer layer of a standard roller with 4 by 13 tactile pixels (taxels) of soft capacitive sensors, which provide more than 1 pF of change in capacitance per taxel over a pressure range of 1 MPa. Sensors are made of silicone and mounted on a flexible printed circuit board on which a microcontroller with Bluetooth‐Low‐Energy collects and transmits capacitance data. The sensor dielectric layer is composed of pillars that increase layer compliance and sensitivity while also providing the stiffness of typical industrial rollers. The ability of the roller to measure real‐time local compaction pressure at typical manufacturing speeds enables the monitoring of spatially‐resolved pressure‐time curves, which can be used to predict and control adhesion.

In all ultrasonic material evaluation methods, transducers and sensors play a key role of mechanoelectrical conversion. Their transduction characteristics must be known quantitatively in designing and implementing successful structural health monitoring (SHM) systems. Yet, their calibration and verification have lagged behind most other aspects of SHM system development. This study aims to extend recent advances in quantifying the transmission and receiving sensitivities to normally incident longitudinal waves of ultrasonic transducers and acoustic emission sensors. This paper covers extending the range of detection to lower frequencies, expanding to areal and multiple sensing methods and examining transducer loading effects. Using the refined transmission characteristics, the receiving sensitivities of transducers and sensors were reexamined under the conditions representing their actual usage. Results confirm that the interfacial wave transmission is governed by wave propagation theory and that the receiving sensitivity of resonant acoustic emission sensors peaks at antiresonance.

The performance of the capacitive gap-sensing system plays a critical role in a satellite-based gravity gradiometer that is developed using an electrostatic accelerometer. The capacitive sensing gain mainly depends on the stabilized injection bias amplitude, the gain of the transformer bridge, and the trans-impedance amplifier. Previous studies have indicated that amplitude noise is the main factor influencing the noise of capacitive displacement detection. Analyzing the capacitive gap-sensing system indicates that the amplitude, frequency, phase, and broadband noises of the stabilized injection bias have varying levels of influence on the performance of the detection system. This paper establishes a model to clarify the mentioned effects. The validation of the sub-tests demonstrates that the analysis and evaluation results of various noise coefficients are highly consistent with the model’s predicted outcomes.

Carbon nanotube–glue composite gel-based surface-type elastic sensors with a cylindrical shape deformable (flexible) metallic body were fabricated for tactile pressure and compressive displacement sensing. The fabrication of the sensors was performed using the rubbing-in technique. The effect of the pressure and the compressive displacement on the capacitance and the impedance of the sensors were investigated at various frequencies (in the range of 1 kHz to 200 kHz). It was found that under the effect of pressure from 0 to 9 g/cm2, the capacitance increased by 1.86 and 1.78 times, while the impedance decreased by 1.84 and 1.71 times at the frequencies of 1 kHz to 200 kHz, respectively. The effect of displacement on the impedance and the capacitance of the device was also investigated at various frequencies from 1 kHz to 200 kHz. The results showed that under the effect of compressive displacement up to 25 µm, the impedance of the sensors decreased on average by 1.19 times, while the capacitance increased by 1.09 times, accordingly. The frequency response of the displacement sensor showed that it matched with the low-pass filter. The obtained results are explained based on changes in the shape and geometrical parameters of the cylindrical-shaped conductive body. These results have also been explained on the basis of the distance between the conductive plates of the capacitive sensors during compression, which takes place under the effect of applied pressure or displacement. Moreover, the design of the sensors is simple and easy to fabricate, and their use is also earthy. The fabricated sensors have great potential for commercialization.

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors' response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis's applied forces, corroborating its potential for clinical practice. [ABSTRACT FROM AUTHOR]

Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a  6 × 18  array of capacitive sensors that captured five gestures—Palm, Fist, Middle, OK, and Index—of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average  F 1  score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system. [ABSTRACT FROM AUTHOR]

Stable grasps are essential for robots handling objects. This is especially true for "robotized" large industrial machines as heavy and bulky objects that are unintentionally dropped by the machine can lead to substantial damages and pose a significant safety risk. Consequently, adding a proximity and tactile sensing to such large industrial machinery can help to mitigate this problem. In this paper, we present a sensing system for proximity/tactile sensing in gripper claws of a forestry crane. In order to avoid difficulties with respect to the installation of cables (in particular in retrofitting of existing machinery), the sensors are truly wireless and can be powered using energy harvesting, leading to autarkic, i.e., self-contained, sensors. The sensing elements are connected to a measurement system which transmits the measurement data to the crane automation computer via Bluetooth low energy (BLE) compliant to IEEE 1451.0 (TEDs) specification for eased logical system integration. We demonstrate that the sensor system can be fully integrated in the grasper and that it can withstand the challenging environmental conditions. We present experimental evaluation of detection in various grasping scenarios such as grasping at an angle, corner grasping, improper closure of the gripper and proper grasp for logs of three different sizes. Results indicate the ability to detect and differentiate between good and poor grasping configurations. [ABSTRACT FROM AUTHOR]

In the space gravitational wave detection mission, inertial sensors play the role of providing an inertial reference for the laser interferometric measurement system. Among them, the capacitance sensor serves as the core key technology of the inertial sensor, used to measure the relative position of the test mass (TM) in the electrode cage. The capacitance sensor utilizes synchronous demodulation technology to extract signals from the AC induction signal. When the phase of the demodulation switch signal is aligned, the synchronous demodulator can most effectively filter out noise, thus directly influencing the performance of the capacitance sensor. However, since the TM is in a suspended state, the information read by the capacitance sensor is dynamic, which increases the difficulty of demodulation phase alignment. In light of this, a method is proposed for achieving the phase alignment of the demodulation switch signal in a dynamic environment. This is accomplished by adjusting the phase of the demodulation switch signal, and subsequently computing the phase difference between the AC induction signal and the demodulation switch signal. At the same time, a measurement and evaluation method for phase deviation is also proposed. Ultimately, an automatic phase alignment system for the demodulation switch signal in dynamic environments is successfully implemented on an FPGA platform, and tests are conducted on a hexapod PI console platform to simulate dynamic environments. The experimental results demonstrate that the system accurately achieves phase alignment in the static environment, with a phase deviation of 0.1394 rad. In the simulated dynamic environment, the phase deviation is 0.1395 rad.

Nanometer-sized carbon particulates generated by incomplete combustion in heavy-duty vehicles are harmful to human health. A high-resolution technique is needed to detect and measure these pollutants. This study aims to optimize a capacitive sensor design for detecting and measuring particulates. Firstly, the effect of design parameters on particulate detection and sensor compliance sensitivity is investigated by using the finite element method. By comparing the simulation results with literature findings for performance validation, the sensor structure is optimized to detect lower particulate concentrations. The simulation result shows that particulate detection sensitivity has linear variations with changes in particulate mass. With optimum electrode spacing and top insulation layer thickness of 5 µm, the sensor can detect a particulate deposition of 0.033 mg/min and generate a maximum capacitance of 581 pF. Since the optimized design can measure particulate deposition at a lower range and with higher sensitivity, it is suitable to be applied to detect nanometer-sized carbon particulates.

A graphene-based capacitive NO2 sensing device was developed by utilizing the quantum capacitance effect. We have used a graphene field-effect transistor (G-FET) device whose geometrical capacitance is enhanced by incorporating an aluminum back-gate electrode with a naturally oxidized aluminum surface as an insulating layer. When the graphene, the top-side of the device, is exposed to NO2, the quantum capacitance of graphene and, thus, the measured capacitance of the device, changed in accordance with NO2 concentrations ranging from 1–100 parts per million (ppm). The operational principle of the proposed system is also explained with the changes in gate voltage-dependent capacitance of the G-FET exposed to various concentrations of NO2. Further analyses regarding carrier density changes and potential variances under various concentrations of NO2 are also presented to strengthen the argument. The results demonstrate the feasibility of capacitive NO2 sensing using graphene and the operational principle of capacitive NO2 sensing. [ABSTRACT FROM AUTHOR]

Soft robotic grippers achieve increased versatility and reduced complexity through intelligence embodied in their flexible and conformal structures. The most widely used soft grippers are pneumatically driven; they are simple and effective but require bulky air compressors that limit their application space and external sensors or computationally expensive vision systems for pick verification. In this study, a multi‐material architecture for self‐sensing electrohydraulic bending actuators is presented that enables a new class of highly versatile and reconfigurable soft grippers that are electrically driven and feature capacitive pick verification and object size detection. These electrohydraulic grippers are fast (step input results in finger closure in 50 ms), draw low power (6.5 mW per finger to hold grasp), and can pick a wide variety of objects with simple binary electrical control. Integrated high‐voltage driving electronics are presented that greatly increase the application space of the grippers and make them readily compatible with commercially available robotic arms. [ABSTRACT FROM AUTHOR]

In space gravitational-wave-detection missions, inertial sensors are used as the core loads, and their acceleration noise needs to reach 3 × 10 − 15   ms − 2 / Hz at a frequency of 0.1   mHz , which corresponds to the capacitive sensing system; the capacitive sensing noise on the sensitive axis needs to reach 1   aF / Hz . Unlike traditional circuit noise evaluation, the noise in the mHz frequency band is dominated by the thermal noise and the 1 / f noise of the device, which is a challenging technical goal. In this paper, a low-frequency, high-precision resonant capacitor bridge method based on a planar transformer is used. Compared with the traditional winding transformer, the developed planar transformer has the advantages of low temperature drift and low 1 / f noise. For closed-loop measurements of capacitive sensing circuits and sensitive structures, the minimum capacitive resolution in the time domain is about 3   aF , which is far lower than the scientific measurement resolution requirement of 5.8   fF for gravitational wave detection. The capacitive sensing noise is converted to 1.095   aF / Hz in the frequency band of 10   mHz – 1   Hz . Although there is a gap between the closed-loop measurement results and the final index, the measurement environment is an experimental condition without temperature control on the ground; additionally, in China, the measurement integrity and actual measurement results of the capacitive sensing function have reached a domestic leading level. This is the realization of China's future space gravitational wave exploration. [ABSTRACT FROM AUTHOR]

Cysticercosis, caused by Taenia solium infection, is a leading cause of acquired epilepsy in many developing countries. Several types of immunoassays have been developed for the detection of Taenia solium infection in both infected humans and livestock animals. However, these methods require central laboratory facilities and are both time- and labor-consuming with longer than desired turnaround time. In this work, we demonstrated that AC electrokinetics (ACEK) capacitive sensing can be used to realize point-of-care immunosensor in general, with the on-site screening of Taenia solium infection as an example here. The sensor employs interdigitated microelectrodes (IDME) functionalized with a recombinant Taenia solium antigen, rT24H, to detect anti-rT24H antibodies in clinical serum samples. ACEK capacitive sensing method interrogates the IDME sensors with a special AC signal, which serves the dual purposes of enriching target antibodies by ACEK effects and directly measuring the capacitance change induced by specific binding. First, to characterize the ACEK biosensor as an immunosensor in general, IgG in phosphate-buffered saline buffer was tested against IDME sensors functionalized with anti-IgG. The limit of detection of the sensor was 24.1 fg/mL, and the linear dynamic range was 0.1–100 pg/mL. To test the clinical usage of this sensor, ACEK capacitive sensors with rT24H probe were used to test clinical serum samples from patients with or without Taenia solium infection. The diagnostic sensitivity of the ACEK capacitive sensor for Taenia solium infection was found to be 88.24%. ACEK capacitive immunosensors have shown good potential for point-of-care diagnostics. [ABSTRACT FROM AUTHOR]

Harvesting energy for IoT nodes in places that are permanently poorly lit is important, as many such places exist in buildings and other locations. The need for energy-autonomous devices working in such environments has so far received little attention. This work reports the design and test results of an energy-autonomous sensor node powered solely by solar cells. The system can cold-start and run in low light conditions (in this case 20 lux and below, using white LEDs as light sources). Four solar cells of 1 cm2 each are used, yielding a total active surface of 4 cm2. The system includes a capacitive sensor that acts as a touch detector, a crystal-accurate real-time clock (RTC), and a Cortex-M3-compatible microcontroller integrating a Bluetooth Low Energy radio (BLE) and the necessary stack for communication. A capacitor of 100 μF is used as energy storage. A low-power comparator monitors the level of the energy storage and powers up the system. The combination of the RTC and touch sensor enables the MCU load to be powered up periodically or using an asynchronous user touch activity. First tests have shown that the system can perform the basic work of cold-starting, sensing, and transmitting frames at +0 dBm, at illuminances as low as 5 lux. Harvesting starts earlier, meaning that the potential for full function below 5 lux is present. The system has also been tested with other light sources. The comparator is a test chip developed for energy harvesting. Other elements are off-the-shelf components. The use of commercially available devices, the reduced number of parts, and the absence of complex storage elements enable a small node to be built in the future, for use in constantly or intermittently poorly lit places. [ABSTRACT FROM AUTHOR]

In order to improve the performance (linearity and sensitivity) of capacitive-based diaphragm pressure sensors, a new design is proposed. The non-linearity is improved by eliminating the capacitance of a small area in the central region of the sensor. In contrast, the sensitivity is improved by enhancing the contribution of the edges of the sensor, which leads to a structure where the distance between the fixed and movable diaphragms is non-uniform. Unlike the other research works, we focus on the case of large deflections. The mathematical model allowing the determination of the capacitance as a function of pressure for this new design is presented. Three prototypes with different sized non-metalized central areas are successfully fabricated using 3D printing technology and machining process.Simulation results show that this form allows a non-linearity inferior to 1% if the reference capacitor is variable.

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors’ response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis’s applied forces, corroborating its potential for clinical practice.

Stable grasps are essential for robots handling objects. This is especially true for “robotized” large industrial machines as heavy and bulky objects that are unintentionally dropped by the machine can lead to substantial damages and pose a significant safety risk. Consequently, adding a proximity and tactile sensing to such large industrial machinery can help to mitigate this problem. In this paper, we present a sensing system for proximity/tactile sensing in gripper claws of a forestry crane. In order to avoid difficulties with respect to the installation of cables (in particular in retrofitting of existing machinery), the sensors are truly wireless and can be powered using energy harvesting, leading to autarkic, i.e., self-contained, sensors. The sensing elements are connected to a measurement system which transmits the measurement data to the crane automation computer via Bluetooth low energy (BLE) compliant to IEEE 1451.0 (TEDs) specification for eased logical system integration. We demonstrate that the sensor system can be fully integrated in the grasper and that it can withstand the challenging environmental conditions. We present experimental evaluation of detection in various grasping scenarios such as grasping at an angle, corner grasping, improper closure of the gripper and proper grasp for logs of three different sizes. Results indicate the ability to detect and differentiate between good and poor grasping configurations.

Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures—Palm, Fist, Middle, OK, and Index—of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants’ gestures and tested with one different participant’s gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.

We present a novel intelligent garment design approach for body posture/gesture detection in the form of a loose-fitting blazer prototype, “the MoCaBlazer.” The design is realized by leveraging conductive textile antennas with the capacitive sensing modality, supported by an open-source electronic theremin system (OpenTheremin). The use of soft textile antennas as the sensing element allows flexible garment design and seamless tech-garment integration for the specific structure of different clothes. Our novel approach is evaluated through two experiments involving defined movements (20 arm/torso gestures and eight dance movements). In cross-validation, the classification model yields up to 97.18% average accuracy and 92% f1-score, respectively. We have also explored real-time inference enabled by a radio frequency identification (RFID) synchronization method, yielding an f1-score of 82%. Our approach opens a new paradigm for designing motion-aware smart garments with soft conductive textiles beyond traditional approaches that rely on tight-fitting flexible sensors or rigid motion sensor accessories.

Passive electric field sensing can be utilized in a wide variety of application areas, although it has certain limitations. In order to better understand what these limitations are and how countervailing measures to these limitations could be implemented, this paper contributes an in-depth discussion of problems with passive electric field sensing and how to bypass or solve them. The focus lies on the explanation of how commonly known signal processing techniques and hardware build-up schemes can be used to improve passive electric field sensors and the corresponding data processing. [ABSTRACT FROM AUTHOR]

This paper presents a novel design of a high resolution and low-cost tactile force sensor, based on the concept of mode-localization in two weakly coupled resonators (WCRs). The sensor is fabricated at mesoscale by utilizing rapid prototyping techniques. The two WCRs in the sensor are operated at resonance by using an electrostatic actuation. Change in the oscillation amplitude ratios and resonant frequency shift, corresponding to an input force is utilized as an output metric for the measurement of force. The application of an applied force on the WCRs induced electrostatic strain, which acted as a negative stiffness perturbation. The outer body of sensor is manufactured using a soft silicone elastomer and shaped using molds based on laser cutting technique. The proposed tactile force sensor is analyzed numerically through finite-element-method (FEM) based simulations. For the testing of tactile force sensor, an actuation and sensing electronics scheme is developed. The experimental results revealed that the sensor is capable of measuring input force up to 20 mN with a relative amplitude ratio (AR) and resonant frequency shift based sensitivity of 27040 ppm/mN and 3553 ppm/mN respectively. The experimentally evaluated resolution for the sensor is 7.3 µN. The sensor shows the stability in response to the thermal variations and low-frequency vibrational environments. [Display omitted] • A novel resonant tactile force sensor, using mode-localization Phenomena in two weakly coupled resonators (WCRs). • The sensing and the actuation are capacitive and electrostatic. • The drive circuitry and signal readout electronics for the sensor have been presented for the tactile force sensor. • The tactile force sensor is fabricated using the conventional machining processes. [ABSTRACT FROM AUTHOR]

This study presents the design and implementation of a capacitive type pressure sensor with vertically integrated deformable hermetic chambers based on the standard commercial processes (the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- $\mu \text{m}$ 1P6M complementary metal-oxide-semiconductor (CMOS) process) and the in-house post CMOS processes. The proposed pressure sensor features the realization of two vertically integrated hermetic chambers consisting of two deformable diaphragms with embedded sensing electrodes and one diaphragm with reference electrodes. Thus, the sensitivity of the pressure sensor can be improved for near twofold within the same footprint. Moreover, the stepped structure is designed to reduce the stiffness of the deformable diaphragms to further enhance the sensitivity of the proposed sensor. The feasibility of the proposed design has been evaluated through both finite element method (FEM) simulations and experiments. Measurement results indicate, as compared with the reference design with only one deformable hermetic chamber, the proposed pressure sensor with vertically integrated deformable hermetic chambers has a near twofold enhancement in sensitivity within the pressure range of 20–120 kPa. Additionally, the proposed design with the deformable stepped-diaphragm can enhance the sensitivity by about 5.1-fold within the same pressure range. [ABSTRACT FROM AUTHOR]

A flexible fiber-coupled confocal laser endomicroscope has been developed using an electrostatic micro-electro-mechanical system (MEMS) scanner located in at distal optics to collect in vivo images in human subjects. Long transmission lines are required that deliver drive and sense signals with limited bandwidth. Phase shifts have been observed between orthogonal ${X}$ - and ${Y}$ -scanner axes from environmental perturbations, which impede image reconstruction. Image-processing algorithms used for correction depend on image content and quality, while scanner calibration in the clinic can be limited by potential patient exposure to lasers. We demonstrate a capacitive sensing method to track the motion of the electrostatically driven 2-D MEMS scanner and to extract phase information needed for image reconstruction. This circuit uses an amplitude modulation (AM) envelope detection method on shared drive and sensing electrodes of the scanner. Circuit parameters were optimized for performance given high scan frequencies, transmission line effects, and substantial parasitic coupling of drive signal to circuit output. Extraction of phase information further leverages nonlinear dynamics of the MEMS scanner. The sensing circuit was verified by comparing with data from a position sensing detector (PSD) measurement. The phase estimation showed an accuracy of 2.18° and 0.79° in ${X}$ - and ${Y}$ -axes for motion sensing, respectively. The results indicate that the sensing circuit can be implemented with feedback control for precalibration of the scanner in clinical MEMS-based imaging systems. [ABSTRACT FROM AUTHOR]

Harvesting energy for IoT nodes in places that are permanently poorly lit is important, as many such places exist in buildings and other locations. The need for energy-autonomous devices working in such environments has so far received little attention. This work reports the design and test results of an energy-autonomous sensor node powered solely by solar cells. The system can cold-start and run in low light conditions (in this case 20 lux and below, using white LEDs as light sources). Four solar cells of 1 cm2 each are used, yielding a total active surface of 4 cm2. The system includes a capacitive sensor that acts as a touch detector, a crystal-accurate real-time clock (RTC), and a Cortex-M3-compatible microcontroller integrating a Bluetooth Low Energy radio (BLE) and the necessary stack for communication. A capacitor of 100 μF is used as energy storage. A low-power comparator monitors the level of the energy storage and powers up the system. The combination of the RTC and touch sensor enables the MCU load to be powered up periodically or using an asynchronous user touch activity. First tests have shown that the system can perform the basic work of cold-starting, sensing, and transmitting frames at +0 dBm, at illuminances as low as 5 lux. Harvesting starts earlier, meaning that the potential for full function below 5 lux is present. The system has also been tested with other light sources. The comparator is a test chip developed for energy harvesting. Other elements are off-the-shelf components. The use of commercially available devices, the reduced number of parts, and the absence of complex storage elements enable a small node to be built in the future, for use in constantly or intermittently poorly lit places.

A graphene-based capacitive NO2 sensing device was developed by utilizing the quantum capacitance effect. We have used a graphene field-effect transistor (G-FET) device whose geometrical capacitance is enhanced by incorporating an aluminum back-gate electrode with a naturally oxidized aluminum surface as an insulating layer. When the graphene, the top-side of the device, is exposed to NO2, the quantum capacitance of graphene and, thus, the measured capacitance of the device, changed in accordance with NO2 concentrations ranging from 1–100 parts per million (ppm). The operational principle of the proposed system is also explained with the changes in gate voltage-dependent capacitance of the G-FET exposed to various concentrations of NO2. Further analyses regarding carrier density changes and potential variances under various concentrations of NO2 are also presented to strengthen the argument. The results demonstrate the feasibility of capacitive NO2 sensing using graphene and the operational principle of capacitive NO2 sensing.

Gesture recognition has become pervasive in many interactive environments. Recognition based on Neural Networks often reaches higher recognition rates than competing methods at a cost of a higher computational complexity that becomes very challenging in low resource computing platforms such as microcontrollers. New optimization methodologies, such as quantization and Neural Architecture Search are steps forward for the development of embeddable networks. In addition, as neural networks are commonly used in a supervised fashion, labeling tends to include bias in the model. Unsupervised methods allow for performing tasks as classification without depending on labeling. In this work, we present an embedded and unsupervised gesture recognition system, composed of a neural network autoencoder and K-Means clustering algorithm and optimized through a state-of-the-art multi- objective NAS. The present method allows for a method to develop, deploy and perform unsupervised classification in low resource embedded devices.

The world population growth and average life expectancy rise have increased the number of people suffering from non-communicable diseases, namely osteoarthritis, a disorder that causes a significant increase in the years lived with disability. Many people who suffer from osteoarthritis undergo replacement surgery. Despite the relatively high success rate, around 10% of patients require revision surgeries, mostly because existing implant technologies lack sensing devices capable of monitoring the bone–implant interface. Among the several monitoring methodologies already proposed as substitutes for traditional imaging methods, cosurface capacitive sensing systems hold the potential to monitor the bone–implant fixation states, a mandatory capability for long-term implant survival. A multifaceted study is offered here, which covers research on the following points: (1) the ability of a cosurface capacitor network to effectively monitor bone loosening in extended peri-implant regions and according to different stimulation frequencies; (2) the ability of these capacitive architectures to provide effective sensing in interfaces with hydroxyapatite-based layers; (3) the ability to control the operation of cosurface capacitive networks using extracorporeal informatic systems. In vitro tests were performed using a web-based network sensor composed of striped and interdigitated capacitive sensors. Hydroxyapatite-based layers have a minor effect on determining the fixation states; the effective operation of a sensor network-based solution communicating through a web server hosted on Raspberry Pi was shown. Previous studies highlight the inability of current bone–implant fixation monitoring methods to significantly reduce the number of revision surgeries, as well as promising results of capacitive sensing systems to monitor micro-scale and macro-scale bone–interface states. In this study, we found that extracorporeal informatic systems enable continuous patient monitoring using cosurface capacitive networks with or without hydroxyapatite-based layers. Findings presented here represent significant advancements toward the design of future multifunctional smart implants. [ABSTRACT FROM AUTHOR]

This research aims at contributing to a seamless, integrated technology intelligent living system for solitary older adults at home. The capacitive intimate sensing module, that can be easily pasted to the existing home space element surfaces, daily objects, or home furniture, such as a wall, door, stairs, a chair, cabinet, table, sofa, etc, is developed in this research. This 30 × 30 cm sensing module can actively sense people's physical behaviors and body movements in spaces. The signals acquired from the sensing modules in indoor spaces will then integrate into the controller system through the IoT application and logically define the behavior classification. From the preliminary analysis of observing the 80-year-old elderly subject's daily activities, the movement trajectory of the 'Move–Stop' pattern is found. There will be a touch (T) and a touchless (TL) relationship between the body and the space elements or objects. The touchless or non-contact intimate relationship also can be divided into two types: 1. the body 'Passes by' (P) the spatial elements or objects, and 2. the body 'Stays' (S) in front of the object and performs activities. This research pasted eight sensing modules on nine objects in six spaces. Finally, the specific actions and life pattern can be recognized and analyzed through the developed IoT spatial behavior smart system and provide the customized intelligent application function for the elderly. [ABSTRACT FROM AUTHOR]

The problems of the traditional type grating angular displacement encoder make it difficult to balance the measurement performance of miniaturization, high precision and high resolution, and the ability to resist vibration and shock is poor. In this article, a multivector variable-area capacitive angular displacement encoder based on a rotating electric field is proposed. Based on the principle of constructing a rotating electric field, a three-layer transmissive sensing structure with upper and lower layers of electrode plates and a modulation rotor as the middle layer is proposed and designed. A single-channel signal is used as the input excitation signal of the capacitive sensor to avoid the problem of mismatching multiple input signals. A pair of quadrature signals reflecting the spatial angle information is obtained through electric field demodulation and differential processing. The phase demodulation method based on the time scale is further adopted to improve the precision of demodulation. Simulations and experiments show that the sensor has a diameter of 50 mm, a thickness of 5 mm, a resolution of $0.81''$ , an accuracy of $2.5''$ , and a maximum nonlinear error of $40''$. The design is verified to be feasible, which provides a new solution for capacitive angular position sensor design. [ABSTRACT FROM AUTHOR]

In space gravitational-wave-detection missions, inertial sensors are used as the core loads, and their acceleration noise needs to reach 3×10−15 ms−2/Hz at a frequency of 0.1 mHz, which corresponds to the capacitive sensing system; the capacitive sensing noise on the sensitive axis needs to reach 1 aF/Hz. Unlike traditional circuit noise evaluation, the noise in the mHz frequency band is dominated by the thermal noise and the 1/f noise of the device, which is a challenging technical goal. In this paper, a low-frequency, high-precision resonant capacitor bridge method based on a planar transformer is used. Compared with the traditional winding transformer, the developed planar transformer has the advantages of low temperature drift and low 1/f noise. For closed-loop measurements of capacitive sensing circuits and sensitive structures, the minimum capacitive resolution in the time domain is about 3 aF, which is far lower than the scientific measurement resolution requirement of 5.8 fF for gravitational wave detection. The capacitive sensing noise is converted to 1.095 aF/Hz in the frequency band of 10 mHz–1 Hz. Although there is a gap between the closed-loop measurement results and the final index, the measurement environment is an experimental condition without temperature control on the ground; additionally, in China, the measurement integrity and actual measurement results of the capacitive sensing function have reached a domestic leading level. This is the realization of China’s future space gravitational wave exploration.