Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

A Microphase-Separated Design toward an All-Round Ionic Hydrogel with Discriminable and Anti-Disturbance Multisensory Functions

Stretchable ionic hydrogels with superior all-round properties that can detect multimodal sensations with excellent discriminability and robustness against external disturbances are highly required for artificial electronic skinapplications. However, some critical material parameters exhibit intrinsic tradeoffs with each other for most ionic hydrogels. Here, a microphase-separated hydrogel is demonstrated by combining three strategies: (1) using of a low crosslinker/monomer ratio to obtain highly entangled polymer chains as the first network; (2) the introduction of zwitterions into the first network; (3) the synthesis of an ultrasoft polyelectrolyte as the second network. This all-round elastic ionic hydrogel exhibits a low Young's modulus (< 60 kPa), large stretchability (> 900%), high resilience (> 95%), unique strain-stiffening behavior, excellent fatigue tolerance, high ionic conductivity (> 2.0 S m⁻1), and anti-freezing capability, which have not been achieved before. These properties allow the ionic hydrogel to operate as a stretchable multimodal sensor that can detect and decouple multiple stimuli (temperature, pressure, and proximity) with excellent discriminability, high sensitivity, and strong sensing-robustness against strains or temperature perturbations. The ionic hydrogel sensor exhibits great potential for intelligent electronic skin applications such as reliable health monitoring and accurate object identification.

Knitting from Nature: Self-Sensing Soft Robotics Enabled by All-in-One Knit Architectures

Self-sensing soft robotics that mimic the proprioception and exteroception abilities of natural biological systems have shown great potential in challenging applications. However, current add-on strategies that simply combine sensors with actuators by post processing generally suffer from poor compatibility in mechanical properties, interfacing problems, complex manufacturing, and high cost. Herein, we present knitted soft robotics with build-in textile-integrated multimodal sensors, where the knit structure is used not only as a physical actuating layer but also as a sensing functional component. Based on different knit-stitch arrangements, an all-in-one knitted electronic skin with functions of neurons, sensing, and actuation in a single knit-structured fabric layer is constructed. The knitted electronic skin is then integrated into knitted soft robotics, enabling a proprioceptive sense of actuation deformation and an exteroceptive perception of ambient stimuli with minimized interferences for actuation. In addition, the tuck stitches serve as an anisotropic strain-limiting layer to increase the actuating energy efficiency, which resolves the key conflict of softness and volumetric power density in soft actuators. This design strategy provides a convenient, low-cost, and customized method to bring about structural and functional integrability into soft actuators, greatly extending the adaptability of current soft robotics for real-world applications.

Stretchable, Adhesive, and Bioinspired Visual Electronic Skin with Strain/Temperature/Pressure Multimodal Non-Interference Sensing

It is highly desirable to construct a single-multimodal sensor that could synchronously perceive multiple stimuli without interference. Here, we propose an adhesive multifunctional chromotropic electronic skin (MCES) that can respond to and distinguish three different stimuli of stain, temperature, and pressure within the two-terminal sensing unit. The mutually discriminating "three-in-one" device converts strain into capacitance and pressure into voltage signals for a tactile stimulus response and produces visual color changes against temperature. In this MCES system, the interdigital capacitor sensor shows high linearity (R² = 0.998), and temperature sensing is realized via reversible multicolor switching bioinspired by the chameleon, showing attractive potential in visualization interaction. Notably, the energy-harvesting triboelectric nanogenerator in MCES can not only detect pressure incentive but also identify objective material species. Looking forward, these findings promise for multimodal sensor technology with reduced complexity and production costs that are highly anticipated in soft robotics, prosthetics, and human-machine interaction applications.

Visual-Olfactory Synergistic Perception Based on Dual-Focus Imaging and a Bionic Learning Architecture

The synergistic interaction of vision and olfaction is critical for both natural and artificial intelligence systems to recognize and adapt to complex environments. However, current bioinspired systems with visual and olfactory sensations are mostly assembled with separate and heterogeneous sensors, inevitably leading to bulky systems and incompatible datasets. Here, we demonstrate on-chip integration of visual and olfactory sensations through a dual-focus imaging approach. By combining lens-based visual imaging and lensless colorimetric imaging, a target object and its odor fingerprint can be captured with a single complementary metal-oxide-semiconductor imager, and the obtained multimodal images are analyzed with a bionic learning architecture for information fusion and perception. To demonstrate the capability of this system, we adapted it to food detection and achieved 100% accuracy in identifying meat freshness and category with a 10 s sampling time. In addition to the highly integrated sensor design, our approach exhibits superior accuracy and efficiency in object recognition, providing a promising approach for robotic sensing and perception.

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.

A Machine-Learning-Enhanced Simultaneous and Multimodal Sensor Based on Moist-Electric Powered Graphene Oxide

Simultaneous multimodal monitoring can greatly perceive intricately multiple stimuli, which is important for the understanding and development of a future human-machine fusion world. However, the integrated multisensor networks with cumbersome structure, huge power consumption, and complex preparation process have heavily restricted practical applications. Herein, a graphene oxide single-component multimodal sensor (GO-MS) is developed, which enables simultaneous monitoring of multiple environmental stimuli by a single unit with unique moist-electric self-power supply. This GO-MS can generate a sustainable moist-electric potential by spontaneously adsorbing water molecules in air, which has a characteristic response behavior when exposed to different stimuli. As a result, the simultaneous monitoring and decoupling of the changes of temperature, humidity, pressure, and light intensity are achieved by this single GO-MS with machine-learning (ML) assistance. Of practical importance, a moist-electric-powered human-machine interaction wristband based on GO-MS is constructed to monitor pulse signals, body temperature, and sweating in a multidimensional manner, as well as gestures and sign language commanding communication. This ML-empowered moist-electric GO-MS provides a new platform for the development of self-powered single-component multimodal sensors, showing great potential for applications in the fields of health detection, artificial electronic skin, and the Internet-of-Things.

A Soft Sponge Sensor for Multimodal Sensing and Distinguishing of Pressure, Strain, and Temperature

Soft wearable sensors are essential components for applications such as motion tracking, human-machine interface, and soft robots. However, most of the reported sensors are either specifically designed to target an individual stimulus or capable of responding to multiple stimuli (e.g., pressure and strain) but without the necessary selectivity to distinguish those stimuli. Here we report an elastomeric sponge-based sensor that can respond to and distinguish three different kinds of stimuli: pressure, strain, and temperature. The sensor utilizes a porous polydimethylsiloxane (PDMS) sponge fabricated from a sugar cube sacrificial template, which was subsequently coated with a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conductive polymer through a low-cost dip-coating process. Responses to different types of stimuli can be distinguished by simultaneously recording resistance and capacitance changes. Because pressure, tensile strain, and temperature change result in different trends in resistance and capacitance change, those stimuli can be clearly distinguished from each other by simultaneously measuring the resistance and capacitance of the sensor. We have also studied the effect of the pore size on the sensor performance and have found that the sponge sensor with smaller pores generally offers greater resistance change and better sensitivity. As a proof-of-concept, we have demonstrated the use of the porous sponge sensor on an artificial hand for object detection, gesture recognition, and temperature sensing applications.

Resilin Distribution and Sexual Dimorphism in the Midge Antenna and Their Influence on Frequency Sensitivity

Small-scale bioacoustic sensors, such as antennae in insects, are often considered, biomechanically, to be not much more than the sum of their basic geometric features. Therefore, little is known about the fine structure and material properties of these sensors-even less so about the degree to which the well-known sexual dimorphism of the insect antenna structure affects those properties. By using confocal laser scanning microscopy (CLSM), we determined material composition patterns and estimated distribution of stiffer and softer materials in the antennae of males and females of the non-biting midge Chironomus riparius. Using finite element modelling (FEM), we also have evidence that the differences in composition of these antennae can influence their mechanical responses. This study points to the possibility that modulating the elastic and viscoelastic properties along the length of the antennae can affect resonant characteristics beyond those expected of simple mass-on-a-spring systems-in this case, a simple banded structure can change the antennal frequency sensitivity. This constitutes a simple principle that, now demonstrated in another Dipteran group, could be widespread in insects to improve various passive and active sensory performances.

Physiology of the Carotid Body: From Molecules to Disease

The carotid body (CB) is an arterial chemoreceptor organ located in the carotid bifurcation and has a well-recognized role in cardiorespiratory regulation. The CB contains neurosecretory sensory cells (glomus cells), which release transmitters in response to hypoxia, hypercapnia, and acidemia to activate afferent sensory fibers terminating in the respiratory and autonomic brainstem centers. Knowledge of the physiology of the CB has progressed enormously in recent years. Herein we review advances concerning the organization and function of the cellular elements of the CB, with emphasis on the molecular mechanisms of acute oxygen sensing by glomus cells. We introduce the modern view of the CB as a multimodal integrated metabolic sensor and describe the properties of the CB stem cell niche, which support CB growth during acclimatization to chronic hypoxia. Finally, we discuss the increasing medical relevance of CB dysfunction and its potential impact on the mechanisms of disease.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

A miniature integrated multimodal sensor for measuring pH, EC and temperature for precision agriculture

Making several simultaneous measurements with different kinds of sensors at the same location in a solution is difficult because of crosstalk between the sensors. In addition, because the conditions at different locations in plant beds differ, in situ measurements in agriculture need to be done in small localized areas. We have fabricated a multimodal sensor on a small Si chip in which a pH sensor was integrated with electrical conductivity (EC) and temperature sensors. An ISFET with a Si₃N₄ membrane was used for the pH sensor. For the EC sensor, the electrical conductivity between platinum electrodes was measured, and the temperature sensor was a p-n junction diode. These are some of the most important measurements required for controlling the conditions in plant beds. The multimodal sensor can be inserted into a plant bed for in situ monitoring. To confirm the absence of crosstalk between the sensors, we made simultaneous measurements of pH, EC, and temperature of a pH buffer solution in a plant bed. When the solution was diluted with hot or cold water, the real time measurements showed changes to the EC and temperature, but no change in pH. We also demonstrated that our sensor was capable of simultaneous in situ measurements in rock wool without being affected by crosstalk.