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Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

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We introduce a novel in-home hand rehabilitation system for monitoring hand motions and assessing grip forces of stroke patients. The overall system is composed of a sensing device and a computer vision system. The sensing device is a lightweight cylindrical object for easy grip and manipulation, which is covered by a passive sensing layer called "Smart Skin." The Smart Skin is fabricated using soft silicone elastomer, which contains embedded microchannels partially filled with colored fluid. When the Smart Skin is compressed by grip forces, the colored fluid rises and fills in the top surface display area. Then, the computer vision system captures the image of the display area through a red-green-blue camera, detects the length change of the liquid through image processing, and eventually maps the liquid length to the calibrated force for estimating the gripping force. The passive sensing mechanism of the proposed Smart Skin device works in conjunction with a single camera setup, making the system simple and easy to use, while also requiring minimum maintenance effort. Our system, on one hand, aims to support home-based rehabilitation therapy with minimal or no supervision by recording the training process and the force data, which can be automatically conveyed to physical therapists. In contrast, the therapists can also remotely instruct the patients with their training prescriptions through online videos. This study first describes the design, fabrication, and calibration of the Smart Skin, and the algorithm for image processing, and then presents experimental results from the integrated system. The Smart Skin prototype shows a relatively linear relationship between the applied force and the length change of the liquid in the range of 0-35 N. The computer vision system shows the estimation error <4% and a relatively high stability in estimation under different hand motions.

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This paper presents a control technique for reducing the reflection of acoustic signals for the plane array of multilayer acoustic absorbers underwater. In order to achieve this, a plane array of multilayer acoustic absorbers is proposed to attenuate low-frequency noise, with each unit consisting of a piezoelectric transducer, two layers of polyvinylidene fluorides and three layers of the acoustic window. Time-delay separation is used to find the incident and reflected acoustic signals to achieve reflected sound reduction. Experimental comparison of the attenuation rate of the reflected acoustic signal when performing passive and active controls is considered to verify the effectiveness of the time-delay separation technique applied plane array absorbers. Experiments on the plane array of smart skin absorbers confirmed that the reduction of reflected acoustic signals makes it suitable for a wide range of underwater applications.

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Programmable mechanically active materials (MAMs) are defined as materials that can sense and transduce external stimuli into mechanical outputs or conversely that can detect mechanical stimuli and respond through an optical change or other change in the appearance of the material. Programmable MAMs are a subset of responsive materials and offer potential in next generation robotics and smart systems. This review specifically focuses on hydrogel-based MAMs because of their mechanical compliance, programmability, biocompatibility, and cost-efficiency. First, the composition of hydrogel MAMs along with the top-down and bottom-up approaches used for programming these materials are discussed. Next, the fundamental principles for engineering responsivity in MAMS, which includes optical, thermal, magnetic, electrical, chemical, and mechanical stimuli, are considered. Some advantages and disadvantages of different responsivities are compared. Then, to conclude, the emerging applications of hydrogel-based MAMs from recently published literature, as well as the future outlook of MAM studies, are summarized.

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Sleep disturbances are prevalent in neurodegenerative diseases in general, and in Parkinson's disease (PD) in particular. Recent evidence points to the clinical value of sleep in disease progression and improving quality of life. Therefore, monitoring sleep quality in an ongoing manner at the convenience of one's home has the potential to improve clinical research and to contribute to significantly better personalized treatment. Further, precise mapping of sleep patterns of each patient can contribute to a better understanding of the disease, its progression and the appropriate medical treatment. Here we review selective, state-of-the-art, home-based devices for assessing sleep and sleep related disorders. We highlight the large potential as well as the main challenges. In particular, we discuss medical validity, standardization and regulatory concerns that currently impede widespread clinical adoption of existing devices. Finally, we propose a roadmap with the technological and scientific steps that are required to impact PD research and treatment.

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With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 "Optimising Design for Inspection" (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

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Tiles are commonly used to cover the exteriors of buildings in Taiwan. However, older buildings in Taiwan encounter the problem of tiles falling off due to natural deterioration, which is unsightly, and more importantly, a threat to public safety. Nevertheless, no current method exists that can effectively detect flaws in building tiles in real time. This study combined the fields of civil engineering and automatic control to reduce risks caused by falling tiles by improving real-time detection of at-risk areas. Micro-resistance was combined with fuzzy theory as the logical foundation for evaluating tile status. String-type strain gauges were adopted as sensors to design a smart skin sensory system that could measure signs of deterioration in tile surface lesions. The design was found to be feasible. In the future, it can be further developed for facile real-time assessment of tile status.

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Due to the growing use of composite materials in aircraft structures, Aircraft Smart Composite Skins (ASCSs) which have the capability of impact monitoring for large-scale composite structures need to be developed. However, the impact of an aircraft composite structure is a random transient event that needs to be monitored on-line continuously. Therefore, the sensor network of an ASCS and the corresponding impact monitoring system which needs to be installed on the aircraft as an on-board device must meet the requirements of light weight, low power consumption and high reliability. To achieve this point, an Impact Region Monitor (IRM) based on piezoelectric sensors and guided wave has been proposed and developed. It converts the impact response signals output from piezoelectric sensors into Characteristic Digital Sequences (CDSs), and then uses a simple but efficient impact region localization algorithm to achieve impact monitoring with light weight and low power consumption. However, due to the large number of sensors of ASCS, the realization of lightweight sensor network is still a key problem to realize an applicable ASCS for on-line and continuous impact monitoring. In this paper, three kinds of lightweight piezoelectric sensor networks including continuous series sensor network, continuous parallel sensor network and continuous heterogeneous sensor network are proposed. They can greatly reduce the lead wires of the piezoelectric sensors of ASCS and they can also greatly reduce the monitoring channels of the IRM. Furthermore, the impact region localization methods, which are based on the CDSs and the lightweight sensor networks, are proposed as well so that the lightweight sensor networks can be applied to on-line and continuous impact monitoring of ASCS with a large number of piezoelectric sensors. The lightweight piezoelectric sensor networks and the corresponding impact region localization methods are validated on the composite wing box of an unmanned aerial vehicle. The accuracy rate of impact region localization is higher than 92%.

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Smart skin is expected to be stretchable and tactile for bionic robots as the medium with the ambient environment. Here, a stretchable triboelectric-photonic smart skin (STPS) is reported that enables multidimensional tactile and gesture sensing for a robotic hand. With a grating-structured metal film as the bioinspired skin stripe, the STPS exhibits a tunable aggregation-induced emission in a lateral tensile range of 0-160%. Moreover, the STPS can be used as a triboelectric nanogenerator for vertical pressure sensing with a maximum sensitivity of 34 mV Pa-1 . The pressure sensing characteristics can remain stable in different stretching conditions, which demonstrates a synchronous and independent sensing property for external stimuli with great durability. By integrating on a robotic hand as a conformal covering, the STPS shows multidimensional mechanical sensing abilities for external touch and different gestures with joints bending. This work has first demonstrated a triboelectric-photonic coupled multifunctional sensing terminal, which may have great applications in human-machine interaction, soft robots, and artificial intelligence.

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The progress of smart skin technology presents unprecedented opportunities for artificial intelligence. Resolution enhancement and energy conservation are critical to improve the perception and standby time of robots. Here, we present a self-powered analogue smart skin for detecting contact location and velocity of the object, based on a single-electrode contact electrification effect and planar electrostatic induction. Using an analogue localizing method, the resolution of this two-dimensional smart skin can be achieved at 1.9 mm with only four terminals, which notably decreases the terminal number of smart skins. The sensitivity of this smart skin is remarkable, which can even perceive the perturbation of a honey bee. Meanwhile, benefiting from the triboelectric mechanism, extra power supply is unnecessary for this smart skin. Therefore, it solves the problems of batteries and connecting wires for smart skins. With microstructured poly(dimethylsiloxane) films and silver nanowire electrodes, it can be covered on the skin with transparency, flexibility, and high sensitivity.

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A bio-inspired absolute pressure sensor network has been developed. Absolute pressure sensors, distributed on multiple silicon islands, are connected as a network by stretchable polyimide wires. This sensor network, made on a 4'' wafer, has 77 nodes and can be mounted on various curved surfaces to cover an area up to 0.64 m × 0.64 m, which is 100 times larger than its original size. Due to Micro Electro-Mechanical system (MEMS) surface micromachining technology, ultrathin sensing nodes can be realized with thicknesses of less than 100 µm. Additionally, good linearity and high sensitivity (~14 mV/V/bar) have been achieved. Since the MEMS sensor process has also been well integrated with a flexible polymer substrate process, the entire sensor network can be fabricated in a time-efficient and cost-effective manner. Moreover, an accurate pressure contour can be obtained from the sensor network. Therefore, this absolute pressure sensor network holds significant promise for smart vehicle applications, especially for unmanned aerial vehicles.

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A high-resolution dynamic tactile/pressure display is indispensable to the comprehensive perception of force/mechanical stimulations such as electronic skin, biomechanical imaging/analysis, or personalized signatures. Here, we present a dynamic pressure sensor array based on pressure/strain tuned photoluminescence imaging without the need for electricity. Each sensor is a nanopillar that consists of InGaN/GaN multiple quantum wells. Its photoluminescence intensity can be modulated dramatically and linearly by small strain (0-0.15%) owing to the piezo-phototronic effect. The sensor array has a high pixel density of 6350 dpi and exceptional small standard deviation of photoluminescence. High-quality tactile/pressure sensing distribution can be real-time recorded by parallel photoluminescence imaging without any cross-talk. The sensor array can be inexpensively fabricated over large areas by semiconductor product lines. The proposed dynamic all-optical pressure imaging with excellent resolution, high sensitivity, good uniformity, and ultrafast response time offers a suitable way for smart sensing, micro/nano-opto-electromechanical systems.

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Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

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