3D printed swelling-driven shape-morphing pH-responsive hydrogel gripper.

Stimuli-responsive soft robots have provided new directions for obtaining advanced biomedical healthcare systems, such as targeted drug delivery capsules, less-invasive biopsy tools, and untethered microsurgical robots. We designed, 3D printed, and tested diverse time-dependent shape changeable 3D pH-responsive soft grippers consisting of N-isopropylacrylamide (NIPAM) and N-isopropylacrylamide-co-acrylic acid (NIPAM-AAc) bilayer. We found that the swelling/deswelling-driven actuation of the pH-responsive NIPAM/NIPAM-AAc gripper is primarily affected by the volume percent (% v/v) of the acrylic acid (AAc) and intensity of UV light. We expect that this study can be applied to untethered pH-responsive soft grippers as smart drug delivery capsules or biopsy tools in biomedical healthcare systems.

DSE-NN: Deeply Supervised Efficient Neural Network for Real-Time Remote Photoplethysmography.

Non-contact remote photoplethysmography can be used in a variety of medical and healthcare fields by measuring vital signs continuously and unobtrusively. Recently, end-to-end deep learning methods have been proposed to replace the existing handcrafted features. However, since the existing deep learning methods are known as black box models, the problem of interpretability has been raised, and the same problem exists in the remote photoplethysmography (rPPG) network. In this study, we propose a method to visualize temporal and spectral representations for hidden layers, deeply supervise the spectral representation of intermediate layers through the depth of networks and optimize it for a lightweight model. The optimized network improves performance and enables fast training and inference times. The proposed spectral deep supervision helps to achieve not only high performance but also fast convergence speed through the regularization of the intermediate layers. The effect of the proposed methods was confirmed through a thorough ablation study on public datasets. As a result, similar or outperforming results were obtained in comparison to state-of-the-art models. In particular, our model achieved an RMSE of 1 bpm on the PURE dataset, demonstrating its high accuracy. Moreover, it excelled on the V4V dataset with an impressive RMSE of 6.65 bpm, outperforming other methods. We observe that our model began converging from the very first epoch, a significant improvement over other models in terms of learning efficiency. Our approach is expected to be generally applicable to models that learn spectral domain information as well as to the applications of regression that require the representations of periodicity.

Wireless, AI-enabled wearable thermal comfort sensor for energy-efficient, human-in-the-loop control of indoor temperature.

The conventional heating, ventilation, and air conditioning (HVAC) systems are based on a set-point control approach that only considers the temperature of the environment without reflecting the thermophysiological status of the occupant. This approach not only fails to fully satisfy individual thermal preferences, but it also makes an HVAC operation energy-inefficient. One possible solution is to control the indoor thermal condition based on an accurate prediction of the occupant's thermal comfort to prevent any unnecessary energy consumption. Here, we present an artificial intelligence (AI) wearable sensor-based human-in-the-loop HVAC control system that is operated on a real-time basis reflecting the thermophysiological condition of the occupant to automatically improve their thermal comfort while reducing the energy consumption of the building. The wristband-type, AI-based, three-point wearable temperature sensor offers excellent thermal comfort prediction accuracy (93.9%), enabling a human-centric HVAC control operation. A proof-of-concept demonstration of closed human-in-the-loop HVAC control using the AI-enabled wearable sensor system confirms both the accuracy of the thermal comfort prediction and the energy-efficiency of this approach, demonstrating its potential as a new solution that improves the occupant's thermal comfort and provides building energy savings.

Beyond Human Touch Perception: An Adaptive Robotic Skin Based on Gallium Microgranules for Pressure Sensory Augmentation.

Robotic skin with human-skin-like sensing ability holds immense potential in various fields such as robotics, prosthetics, healthcare, and industries. To catch up with human skin, numerous studies are underway on pressure sensors integrated on robotic skin to improve the sensitivity and detection range. However, due to the trade-off between them, existing pressure sensors have achieved only a single aspect, either high sensitivity or wide bandwidth. Here, an adaptive robotic skin is proposed that has both high sensitivity and broad bandwidth with an augmented pressure sensing ability beyond the human skin. A key for the adaptive robotic skin is a tunable pressure sensor built with uniform gallium microgranules embedded in an elastomer, which provides large tuning of the sensitivity and the bandwidth, excellent sensor-to-sensor uniformity, and high reliability. Through the mode conversion based on the solid-liquid phase transition of gallium microgranules, the sensor provides 97% higher sensitivity (16.97 kPa-1 ) in the soft mode and 262.5% wider bandwidth (≈1.45 MPa) in the rigid mode compared to the human skin. Successful demonstration of the adaptive robotic skin verifies its capabilities in sensing a wide spectrum of pressures ranging from subtle blood pulsation to body weight, suggesting broad use for various applications.

Multichannel Acoustic Spectroscopy of the Human Body for Inviolable Biometric Authentication.

Specific features of the human body, such as fingerprint, iris, and face, are extensively used in biometric authentication. Conversely, the internal structure and material features of the body have not been explored extensively in biometrics. Bioacoustics technology is suitable for extracting information about the internal structure and biological and material characteristics of the human body. Herein, we report a biometric authentication method that enables multichannel bioacoustic signal acquisition with a systematic approach to study the effects of selectively distilled frequency features, increasing the number of sensing channels with respect to multiple fingers. The accuracy of identity recognition according to the number of sensing channels and the number of selectively chosen frequency features was evaluated using exhaustive combination searches and forward-feature selection. The technique was applied to test the accuracy of machine learning classification using 5,232 datasets from 54 subjects. By optimizing the scanning frequency and sensing channels, our method achieved an accuracy of 99.62%, which is comparable to existing biometric methods. Overall, the proposed biometric method not only provides an unbreakable, inviolable biometric but also can be applied anywhere in the body and can substantially broaden the use of biometrics by enabling continuous identity recognition on various body parts for biometric identity authentication.

Bending Sensor Based on Controlled Microcracking Regions for Application toward Wearable Electronics and Robotics.

A soft bending sensor based on the inverse pyramid structure is demonstrated, revealing that it can effectively suppress microcrack formation in designated regions, thus allowing the cracks to open gradually with bending in a controlled manner. Such a feature enabled the bending sensor to simultaneously have a wide dynamic range of bending strain (0.025-5.4%), high gauge factor (∼74), and high linearity (R2 ∼ 0.99). Furthermore, the bending sensor can capture repeated instantaneous changes in strain and various types of vibrations, owing to its fast response time. Moreover, the bending direction can be differentiated with a single layer of the sensor, and using an array of sensors integrated on a glove, object recognition was demonstrated via machine learning. Finally, a self-monitoring proprioceptive ionic electroactive polymer (IEAP) actuator capable of operating in liquid was demonstrated. Such features of our bending sensor will enable a simple and effective way of detecting sophisticated motion, thus potentially advancing wearable healthcare monitoring electronics and enabling proprioceptive soft robotics.

Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus.

The recent global spread of COVID-19 stresses the importance of developing diagnostic testing that is rapid and does not require specialized laboratories. In this regard, nanomaterial thin-film-based immunosensors fabricated via solution processing are promising, potentially due to their mass manufacturability, on-site detection, and high sensitivity that enable direct detection of virus without the need for molecular amplification. However, thus far, thin-film-based biosensors have been fabricated without properly analyzing how the thin-film properties are correlated with the biosensor performance, limiting the understanding of property-performance relationships and the optimization process. Herein, the correlations between various thin-film properties and the sensitivity of carbon nanotube thin-film-based immunosensors are systematically analyzed, through which optimal sensitivity is attained. Sensitivities toward SARS-CoV-2 nucleocapsid protein in buffer solution and in the lysed virus are 0.024 [fg/mL]-1 and 0.048 [copies/mL]-1, respectively, which are sufficient for diagnosing patients in the early stages of COVID-19. The technique, therefore, can potentially elucidate complex relationships between properties and performance of biosensors, thereby enabling systematic optimization to further advance the applicability of biosensors for accurate and rapid point-of-care (POC) diagnosis.

Microfluidic Screening-Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend.

Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models are trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization shows the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. The technique can serve as a guideline for elucidating the underlying complex parameter-property-performance correlations in solution-based thin-film processing, thereby accelerating the optimization of various thin-film devices in the future.

Correction to: An Easy-to-Fabricate Cell Stretcher Reveals Density-Dependent Mechanical Regulation of Collective Cell Movements in Epithelia.

[This corrects the article DOI: 10.1007/s12195-021-000689-6.].

An Easy-to-Fabricate Cell Stretcher Reveals Density-Dependent Mechanical Regulation of Collective Cell Movements in Epithelia.

INTRODUCTION: Mechanical forces regulate many facets of cell and tissue biology. Studying the effects of forces on cells requires real-time observations of single- and multi-cell dynamics in tissue models during controlled external mechanical input. Many of the existing devices used to conduct these studies are costly and complicated to fabricate, which reduces the availability of these devices to many laboratories. METHODS: We show how to fabricate a simple, low-cost, uniaxial stretching device, with readily available materials and instruments that is compatible with high-resolution time-lapse microscopy of adherent cell monolayers. In addition, we show how to construct a pressure controller that induces a repeatable degree of stretch in monolayers, as well as a custom MATLAB code to quantify individual cell strains. RESULTS: As an application note using this device, we show that uniaxial stretch slows down cellular movements in a mammalian epithelial monolayer in a cell density-dependent manner. We demonstrate that the effect on cell movement involves the relocalization of myosin downstream of Rho-associated protein kinase (ROCK). CONCLUSIONS: This mechanical device provides a platform for broader involvement of engineers and biologists in this important area of cell and tissue biology. We used this device to demonstrate the mechanical regulation of collective cell movements in epithelia. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12195-021-00689-6.

Electrical Impedance of Upper Limb Enables Robust Wearable Identity Recognition against Variation in Finger Placement and Environmental Factors.

Most biometric authentication technologies commercialized in various fields mainly rely on acquired images of structural information, such as fingerprints, irises, and faces. However, bio-recognition techniques using these existing physical features are always at risk of template forgery threats, such as fake fingerprints. Due to the risk of theft and duplication, studies have recently been attempted using the internal structure and biological characteristics of the human body, including our previous works on the ratiometric biological impedance feature. However, one may still question its accuracy in real-life use due to the artifacts from sensing position variability and electrode-skin interfacing noise. Moreover, since the finger possesses more severe thermoregulatory vasomotion and large variability in the tissue properties than the core of the body, it is necessary to mitigate the harsh changes occurring at the peripheral extremities of the human body. To address these challenges, we propose a biometric authentication method through robust feature extraction from the upper-limb impedance acquired based on a portable wearable device. In this work, we show that the upper limb impedance features obtained from wearable devices are robust against undesirable factors such as finger placement deviations and day-to-day physiological changes, along with ratiometric impedance features. Overall, our upper-limb impedance-based analysis in a dataset of 1627 measurement from 33 subjects lowered the classification error rate from 22.38% to 4.3% (by a factor of 5), and further down to 2.4% (by a factor of 9) when combined with the ratiometric features.

Hetero-Dimensional 2D Ti3C2Tx MXene and 1D Graphene Nanoribbon Hybrids for Machine Learning-Assisted Pressure Sensors.

Hybridization of low-dimensional components with diverse geometrical dimensions should offer an opportunity for the discovery of synergistic nanocomposite structures. In this regard, how to establish a reliable interfacial interaction is the key requirement for the successful integration of geometrically different components. Here, we present 1D/2D heterodimensional hybrids via dopant induced hybridization of 2D Ti3C2Tx MXene with 1D nitrogen-doped graphene nanoribbon. Edge abundant nanoribbon structures allow a high level nitrogen doping (∼6.8 at%), desirable for the strong coordination interaction with Ti3C2Tx MXene surface. For piezoresistive pressure sensor application, strong adhesion between the conductive layers and at the conductive layer/elastomer interface significantly diminishes the sensing hysteresis down to 1.33% and enhances the sensing stability up to 10 000 cycles at high pressure (100 kPa). Moreover, large-area pressure sensor array reveals a high potential for smart seat cushion-based posture monitoring application with high accuracy (>95%) by exploiting machine learning algorithm.

Identity Recognition Based on Bioacoustics of Human Body.

Current biometrics rely on images obtained from the structural information of physiological characteristics, which is inherently a fatal problem of being vulnerable to spoofing. Here, we studied personal identification using the frequency-domain information based on human body vibration. We developed a bioacoustic frequency spectroscopy system and applied it to the fingers to obtain information on the anatomy, biomechanics, and biomaterial properties of the tissues. As a result, modulated microvibrations propagated through our body could capture a unique spectral trait of a person and the biomechanical transfer characteristics persisted for two months and resulted in 97.16% accuracy of identity authentication in 41 subjects. Ultimately, our method not only eliminates the practical means of creating fake copies of the relevant characteristics but also provides reliable features.

Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System.

Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S11 ) spectrum, and the applied pressure changes the magnitude of the S11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.

Mechanically transformative electronics, sensors, and implantable devices.

Traditionally, electronics have been designed with static form factors to serve designated purposes. This approach has been an optimal direction for maintaining the overall device performance and reliability for targeted applications. However, electronics capable of changing their shape, flexibility, and stretchability will enable versatile and accommodating systems for more diverse applications. Here, we report design concepts, materials, physics, and manufacturing strategies that enable these reconfigurable electronic systems based on temperature-triggered tuning of mechanical characteristics of device platforms. We applied this technology to create personal electronics with variable stiffness and stretchability, a pressure sensor with tunable bandwidth and sensitivity, and a neural probe that softens upon integration with brain tissue. Together, these types of transformative electronics will substantially broaden the use of electronics for wearable and implantable applications.

Ratiometric Impedance Sensing of Fingers for Robust Identity Authentication.

We present a novel biometric authentication system enabled by ratiometric analysis of impedance of fingers. In comparison to the traditional biometrics that relies on acquired images of structural information of physiological characteristics, our biological impedance approach not only eliminates any practical means of making fake copies of the relevant physiological traits but also provides reliable features of biometrics using the ratiometric impedance of fingers. This study shows that the ratiometric features of the impedance of fingers in 10 different pairs using 5 electrodes at the fingertips can reduce the variation due to undesirable factors such as temperature and day-to-day physiological variations. By calculating the ratio of impedances, the difference between individual subjects was amplified and the spectral patterns were diversified. Overall, our ratiometric analysis of impedance improved the classification accuracy of 41 subjects and reduced the error rate of classification from 29.32% to 5.86% (by a factor of 5).

Wireless optofluidic brain probes for chronic neuropharmacology and photostimulation.

Both in vivo neuropharmacology and optogenetic stimulation can be used to decode neural circuitry, and can provide therapeutic strategies for brain disorders. However, current neuronal interfaces hinder long-term studies in awake and freely behaving animals, as they are limited in their ability to provide simultaneous and prolonged delivery of multiple drugs, are often bulky and lack multifunctionality, and employ custom control systems with insufficiently versatile selectivity for output mode, animal selection and target brain circuits. Here, we describe smartphone-controlled, minimally invasive, soft optofluidic probes with replaceable plug-like drug cartridges for chronic in vivo pharmacology and optogenetics with selective manipulation of brain circuits. We demonstrate the use of the probes for the control of the locomotor activity of mice for over four weeks via programmable wireless drug delivery and photostimulation. Owing to their ability to deliver both drugs and photopharmacology into the brain repeatedly over long time periods, the probes may contribute to uncovering the basis of neuropsychiatric diseases.

Highly Uniform and Low Hysteresis Piezoresistive Pressure Sensors Based on Chemical Grafting of Polypyrrole on Elastomer Template with Uniform Pore Size.

Sensor-to-sensor variability and high hysteresis of composite-based piezoresistive pressure sensors are two critical issues that need to be solved to enable their practical applicability. In this work, a piezoresistive pressure sensor composed of an elastomer template with uniformly sized and arranged pores, and a chemically grafted conductive polymer film on the surface of the pores is presented. Compared to sensors composed of randomly sized pores, which had a coefficient of variation (CV) in relative resistance change of 69.65%, our sensors exhibit much higher uniformity with a CV of 2.43%. This result is corroborated with finite element simulation, which confirms that with increasing pore size variability, the variability in sensor characteristics also increases. Furthermore, our devices exhibit negligible hysteresis (degree of hysteresis: 2%), owing to the strong chemical bonding between the conductive polymer and the elastomer template, which prevents their relative sliding and displacement, and the porosity of the elastomer that enhances elastic behavior. Such features of the sensor render it highly feasible for various practical applications in the near future.

Microstructured Porous Pyramid-Based Ultrahigh Sensitive Pressure Sensor Insensitive to Strain and Temperature.

An ultrahigh sensitive capacitive pressure sensor based on a porous pyramid dielectric layer (PPDL) is reported. Compared to that of the conventional pyramid dielectric layer, the sensitivity was drastically increased to 44.5 kPa-1 in the pressure range <100 Pa, an unprecedented sensitivity for capacitive pressure sensors. The enhanced sensitivity is attributed to a lower compressive modulus and larger change in an effective dielectric constant under pressure. By placing the pressure sensors on islands of hard elastomer embedded in a soft elastomer substrate, the sensors exhibited insensitivity to strain. The pressure sensors were also nonresponsive to temperature. Finally, a contact resistance-based pressure sensor is also demonstrated by chemically grafting PPDL with a conductive polymer, which also showed drastically enhanced sensitivity.

Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress.

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.