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In this article, an ultrathin and flexible thermal perception (FTP) sensor with a fingerprint-like sensing structure is proposed. The developed FTP sensor features a sandwich structure formed by biocompatible polyimide (PI), gold thermistors, and Parylene C layers with a total thickness of $8~\mu \text{m}$. Meanwhile, multisensory functions including temperature and pressure sensing, as well as matter recognition are realized. Specifically, the FTP sensor shows an ultrafast response time of less than 0.12 s and 52 ms for matter and temperature detection, respectively, while various matters information such as PI, rubber, and steel, can be easily distinguished. Besides, the ultrathin design allows the sensor to gain signal stability of less than 0.3% even at a curvature up to 0.25 mm−1. Furthermore, the FTP sensor achieved a sensitivity of 812 mV/kPa for pressure sensing after being partially covered with a thermal conductive powder-doped silicone film. In short, the demonstrated functionality of this ultrathin FTP sensor combined with its simple fabrication holds the prospect of further advancing and deploying this device as a promising sensory system for mimicking human interaction. [ABSTRACT FROM AUTHOR]

The demand for flexible tactile sensors with both contact and contactless sensing capabilities is growing rapidly in the fields of smart robotics and prosthesis. However, it is still a challenge to integrate high performance proximity and pressure sensing functions in one sensor through simple fabrication process. Here, we propose a textile-based dual-mode sensor that can work on both magneto-straining mode (proximity) and piezoresistive mode (pressure). The sensor is simply fabricated by coating two functional materials in a concentric pattern, i.e., a magnetic layer followed by a conductive layer made of 2D MXene composite, on spandex substrate. As the dual-mode sensor approaches to a magnetic object, the attractive magnetic force will stretch the magnetic layer, along with the conductive layer. The strained conductive layer causes an increase of the resistance. Upon contact, the magnetic force reaches its maximum, while the pressure force works against the magnetic force and starts to reduce the strain of the conductive layer, leading to a decrease of the resistance. The turning point of the resistance can be used to identify the contact moment, and as an indicator of the switching of the two working modes of the sensor. Benefitting from the superior conductivity of the MXene material in the conductive layer, high sensitivity for both proximity sensing (13.76 mm−1) and pressure sensing (0.7 kPa−1) was achieved. The proposed dual-mode sensing methodology has great potential in human-machine interaction (HMI) applications. [ABSTRACT FROM AUTHOR]

A nanoengineered fabric capable of densely distributed pressure sensing is presented. Piezoresistive carbon nanotube-latex thin films were spray-coated and integrated with fabric to form robust, flexible, and deformable sensors. Spatial pressure sensing was achieved and validated experimentally using an electrical impedance tomography algorithm, which was able to estimate the resistivity distribution of the fabric and its changes due to applied pressure at different locations.

A power-efficient bridge-to-digital sensing interface is proposed, which also offers immunity against power supply noise. The interface utilizes duty-cycling to reduce the static power consumption of resistive bridge sensors, which are commonly used in implantable, wearable, and internet of things (IoT) applications, such as intracranial pressure (ICP) sensing and blood pressure (BP) monitoring. The proposed interface uses a revised version of the pseudo-pseudo differential (PPD) topology with the ping-pong technique to reduce the complexity of traditional fully-differential counterparts. A proof-of-concept prototype has been fabricated in 0.35- $\mu \text{m}$ CMOS and occupies an active area of 0.48 mm2. It achieves 9.13 effective number of bits (ENOB) at 3.72 kHz sampling rate and improvement of more than 50 dB in the power supply rejection ratio (PSRR) by employing the ping-pong technique. It reduces the power consumption of a 5- $\text{k}\Omega $ Wheatstone bridge by 99.6% compared to a traditional interface, down to $2.53~\mu \text{w}$ at 1.8 V supply. The functionality of the system has also been demonstrated in an experimental setup in conjunction with an embedded resistive bridge pressure sensor. [ABSTRACT FROM AUTHOR]

Velostat is a low-cost, low-profile electrical bagging material with piezoresistive properties, making it an attractive option for in-socket pressure sensing. The focus of this research was to explore the suitability of a Velostat-based system for providing real-time socket pressure profiles. The prototype system performance was explored through a series of bench tests to determine properties including accuracy, repeatability and hysteresis responses, and through participant testing with a single subject. The fabricated sensors demonstrated mean accuracy errors of 110 kPa and significant cyclical and thermal drift effects of up to 0.00715 V/cycle and leading to up to a 67% difference in voltage range respectively. Despite these errors the system was able to capture data within a prosthetic socket, aligning to expected contact and loading patterns for the socket and amputation type. Distinct pressure maps were obtained for standing and walking tasks displaying loading patterns indicative of posture and gait phase. The system demonstrated utility for assessing contact and movement patterns within a prosthetic socket, potentially useful for improvement of socket fit, in a low cost, low profile and adaptable format. However, Velostat requires significant improvement in its electrical properties before proving suitable for accurate pressure measurement tools in lower limb prosthetics. [ABSTRACT FROM AUTHOR]

Microelectromechanical systems (MEMS) using flexible and/or stretchable polymers can bring new functions of conformal integration of sensors on flat and curved surfaces. Here, we report flexible strain/pressure sensors using polydimethylsiloxane (PDMS) and polyimide-based MEMS. The MEMS device architecture enables the integration of three electrical sensors, outputting digital signals, onto an area of 50 mm2 for strain and pressure detection. We show that engineered sensors with various sensitivities in bending mode can be applied to control robotic arms. Another demonstration of pressure sensing using these sensors enables the potential of pressure control in handling physical objects. Our integrated wearable flexible sensors, compared to widely used rigid sensors, derive flexibility and stretchability from polymer materials and have huge potential applications in robotics, prosthetics, and other wearable technologies requiring flexible sensors. [ABSTRACT FROM AUTHOR]

Working inside the space suit causes injury and discomfort, but suit assessment techniques such as measuring joint torques and ranges of motion fail to evaluate injury because they fail to distinguish interactions between the human and the space suit. Contact pressure sensing would allow a quantitative assessment of the nature and location of suit-body contact where injuries occur. However, commercially available systems are not well suited for measurement inside the confined environment of the space suit during movement. We report on the design of a wearable pressure sensing system, the Polipo. The Polipo dynamically measures between 5 and 60 kPa of pressure with $\sim 1$ kPa sensitivity, is within 10% root mean square error from a known loading profile during dynamic movement, and is a standalone system able to accommodate a 50th percentile female to a 95th percentile male upper body dimensions with near shirt-sleeve mobility. This paper focuses on the upper body, but the methods may be extended to the full body as future work. It provides a pressure sensing system that could be applied beyond the field of aerospace to assess human–garment interactions, for example recommending armor protection for defense applications or to alleviate fall impacts for medical applications. [ABSTRACT FROM PUBLISHER]

A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior. The energy content at each frequency band of the acquire signals is well related to cavitation flow-field behavior. Moreover, the artificial neural network and the least squares support vector machine (LS-SVM) were implemented to identify the cavitation regime, using, as inputs, the power spectral density distributions of the pressure fluctuations, and some features of the decomposed signals, as the wavelet energy for each decomposition level and wavelet entropy. Results indicate the most accurate model to be used in the cavitation regime identification, underlining the enhanced capability of LS-SVM trained with the input data set based on the wavelet decomposition features. [ABSTRACT FROM PUBLISHER]

A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow-field. The fluctuations of the pressure measurements in the internal orifice and of the intensity level of the images’ pixels were decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior and to highlight the influence of temperature on the phenomena under investigation. It was shown that the energy content at each frequency band of the acquired signals is well related to cavitation flow-field behavior. [ABSTRACT FROM PUBLISHER]

We describe the design, fabrication, and calibration of a highly compliant artificial skin sensor. The sensor consists of multilayered mircochannels in an elastomer matrix filled with a conductive liquid, capable of detecting multiaxis strains and contact pressure. A novel manufacturing method comprised of layered molding and casting processes is demonstrated to fabricate the multilayered soft sensor circuit. Silicone rubber layers with channel patterns, cast with 3-D printed molds, are bonded to create embedded microchannels, and a conductive liquid is injected into the microchannels. The channel dimensions are 200 μm (width) × 300 μm (height). The size of the sensor is 25 mm × 25 mm, and the thickness is approximately 3.5 mm. The prototype is tested with a materials tester and showed linearity in strain sensing and nonlinearity in pressure sensing. The sensor signal is repeatable in both cases. The characteristic modulus of the skin prototype is approximately 63 kPa. The sensor is functional up to strains of approximately 250%. [ABSTRACT FROM PUBLISHER]