Aspiration is prevalent in the elderly population, and can lead to life-threatening conditions such as suffocation and aspiration pneumonia. However, gold-standard diagnostic approaches for aspiration-videofluoroscopic swallowing studies (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES)-involve radiation, invasive procedures, or operational complexity, and cannot provide continuous aspiration monitoring. Here, we develop a skin-interfaced wireless biomechanical system with tailored machine learning algorithms to detect aspiration in a continuous, user-friendly, non-invasive, and automated manner. The system detects post-swallow choking coughs as an indicator of aspiration, and evaluates a nursing-home cohort including aspiration elderly (n = 14) and non-aspiration elderly (n = 64). Leave-one-out cross-validation yields an average accuracy of 92% across four activity types, with a sensitivity of 91% in aspiration recognition. Additional testing on unseen aspiration patients supports patient-level generalization. Reliability analysis and confidence calibration enhance the interpretability of the machine learning classification model, and underpin clinical deployment. The approach provides a compact, user-friendly solution for continuous and dynamic aspiration monitoring in both home and institutional settings, holding promise for improving elderly healthcare.

Inductive sensors are widely implemented in industry due to their noncontact measurement capabilities and inherent immunity to environmental factors such as humidity and electrostatic charges. The rigid construction, however, confines the applications of inductive sensors to heavy machinery and robotics, with few uses in detecting subtle mechanical signals in biology. Here, we cover planar coils with structured magnetorheological elastomers (MRE) to yield a flexible inductive sensor that matches the soft mechanical properties of biological tissues. Multilayer microcavities within the MRE enable structural deformation under tiny fluctuations of pressure and strain, thereby modulating the magnetic reluctance surrounding the coil. Demonstrations in wearable monitoring of pulse, motion, and so forth, as well as implantable measurement of intracranial pressure (ICP) in a rat model, validate the capabilities of the wireless, continuous sensing in vivo. The materials, device architecture, and sensing mechanism offer a promising solution for fundamental and clinical research in biomechanics.

Stretchable sensor technologies capable of tracking multiple deformation modalities have attracted increasing attention due to their potential ability to improve the intelligence of soft robotics, stretchable electronics, and other soft-body systems. Existing multimodal deformation sensors typically require either complicated system integration and sensor layout or complex algorithmic techniques based on computational modeling and machine learning methods. Here, we report a stretchable multimodal deformation sensor that can measure and distinguish the magnitudes and modes of mechanical deformation (stretching, bending, twisting, and pressing) without reliance on complex computation or trained classification/regression models. This sensor relies on the layout of a stretchable magnetic film with a gradually varied magnetization profile and a 3-axis Hall sensor to decode the patterns of spatial change of magnetic flux strength under different deformation modes. We demonstrate the potential of this sensor in wearables and robotics by using it for neck motion monitoring, self-sensing electrical muscular stimulation, intelligent pneumatic gripper for objects classification, control of a stingray-inspired soft robot, and deformation recognition of an artificial elephant trunk.

The development of miniature power generators is essential for advancing wearables and portable devices. Triboelectric nanogenerators have emerged as a promising solution, but miniaturization and broadband energy harvesting remain challenging. To address this challenge, this study developed a multi-functional device with 3D structures using a controlled mechanical buckling process. We designed and converted planar precursor layouts into the desired 3D structures for miniature nanogenerators without using additional connecting parts. Changing the topology and dimension of 3D structures expands the device's functionality and adaptability. The compact device works well for sensing and power generation with excellent cyclic stability and broadband energy conversion. The synergy of charge generation with PVDF and charge storage with PI improves the performance of the device. The 3D structure-based miniature device provides a strategic approach to advancing wearable and self-powered sensing technology.

Soft wearable electronics provide a seamless interface between the human body and electronic systems to support real-time, continuous, long-term monitoring in healthcare and other applications. Incorporating mechanically active materials to these soft electronic systems can further expand sensing modalities, enhance sensing performances, and/or enable new functions that are challenging to achieve with physically static electronic devices. A key property of such mechanically active materials is that their shapes can change upon various external stimuli. This review highlights recent advances in this type of material, with a focus on discussing their integration with soft wearable devices and the resulting impact on the performances. Specifically, the content ranges from piezoelectric materials that generate ultrasound and surface acoustic waves, to magnetic materials that allow for new sensing modalities and haptic feedback, and to elastomeric materials that facilitate pneumatic and hydraulic actuation-all designed for soft wearable devices. The review concludes with an analysis of the key challenges and future opportunities for mechanically active materials.

Reconfigurable antennas have attracted significant interest because of their ability to dynamically adjust radiation properties, such as operating frequencies, thereby managing the congested frequency spectrum efficiently and minimizing crosstalk. However, existing approaches utilizing switches or advanced materials are limited by their discrete tunability, high static power consumption, or material degradation for long-term usage. In this study, we present a W-band frequency reconfigurable antenna that undergoes a geometric transformation from a two-dimensional (2D) precursor, selectively bonded to a prestretched elastomeric substrate, into a desired 3D layout through controlled compressive buckling. Modeling the buckling process using combined mechanics-electromagnetic finite element analysis (FEA) allows for the rational design of the antenna with desired strains applied to the substrate. By releasing the substrate at varying compression ratios, the antenna reshapes into different 3D configurations, enabling continuous frequency reconfigurability. Simulation and experimental results demonstrate that the antenna’s resonant frequency can be tuned from 77 GHz in its 2D state to 94 GHz in its 3D state in a folded-dipole-like design.

Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and the treatment of patients. Current technologies for such a purpose include quasi-static measurements that lack microscale resolution and sensing sites, with limited capabilities for time-dependent, three-dimensional profiling of tumors, particularly at the early growth stage. Here, we report the conformal Hall-sensor-based systems for continuous monitoring of tumor morphological features such as growth rates and volumes. Such platforms incorporate ultrathin crystalline-silicon nanomembranes (200 nm thick) as a basis for displacement sensing via magnetic flux detection, in an array design that yields spatiotemporal information of tumor geometries at high sensitivity. Evaluation involves real-time measurements on a living mouse model with tumor tissues at various pathological conditions, where the integration with deep learning algorithms can further enable the system for large-scale tumor profile reconstruction across tissue surfaces. These microsystems provide the potential for monitoring of tumor progression and treatment guidance in patients.

Assessing the mechanical properties of soft tissues holds broad clinical relevance. Advances in flexible electronics offer possibilities for wearable monitoring of tissue stiffness. However, existing technologies often rely on tethered setups or require frequent calibration, restricting their use in ambulatory environments. This study introduces a mechano-acoustic wave sensing technology for automated, wireless elastography. The patch-form sensor maintains conformal contact with the skin, regardless of body motion or deformation. It provides continuous, depth-sensitive estimation of subcutaneous tissue stiffness through real-time surface wave dispersion analysis. Theoretical and experimental investigations on phantom materials and tissues spanning a wide range of Young's modulus (in kilopascals to megapascals) demonstrate the capability of the device to rapidly and robustly evaluate the stiffness at depths up to several centimeters. The device shows compatibility with various tissue models, with results consistent with in-parallel ultrasound elastography measurements. Deployment of the device during exercises confirms its viability for ambulatory monitoring, enabling continuous assessment of variation in tissue stiffness.

Understanding the brain's complexity and developing treatments for its disorders necessitates advanced neural technologies. Magnetic fields can deeply penetrate biological tissues-including bone and air-without significant attenuation, offering a compelling approach for wireless, bidirectional neural interfacing. This review explores the rapidly advancing field of magnetic implantable devices and materials designed for modulation and sensing of the brain. Key modulation strategies include: magnetoelectric (ME) materials that convert magnetic into electric fields for stimulation; magnetothermal (MT) effects, where heating of nanoparticles activates thermosensitive ion channels; and magnetomechanical (MM) approaches that use magnetic forces to gate mechanosensitive channels. Methods for magnetic-based detection encompass: implantable magnetoresistive probes for the reference-free measurement of weak local neural magnetic fields; magnetic resonance needles that enhance metabolic profiling; and magnetoelastic systems where external magnetic fields vibrate magnetic implants to sense biophysical and biochemical conditions. The breadth of these magnetic transduction mechanisms promises future technologies that provide less invasive and more precise methods for understanding and regulating brain function.

Deep learning (DL) has been used for electromyographic (EMG) signal recognition and achieved high accuracy for multiple classification tasks. However, implementation in resource-constrained prostheses and human-computer interaction devices remains challenging. To overcome these problems, this paper implemented a low-power system for EMG gesture and force level recognition using Zynq architecture. Firstly, a lightweight network model structure was proposed by Ultra-lightweight depth separable convolution (UL-DSC) and channel attention-global average pooling (CA-GAP) to reduce the computational complexity while maintaining accuracy. A wearable EMG acquisition device for real-time data acquisition was subsequently developed with size of 36mm×28mm×4mm. Finally, a highly parallelized dedicated hardware accelerator architecture was designed for inference computation. 18 gestures were tested, including force levels from 22 healthy subjects. The results indicate that the average accuracy rate was 94.92% for a model with 5.0k parameters and a size of 0.026MB. Specifically, the average recognition accuracy for static and force-level gestures was 98.47% and 89.92%, respectively. The proposed hardware accelerator architecture was deployed with 8-bit precision, a single-frame signal inference time of 41.9μs, a power consumption of 0.317W, and a data throughput of 78.6 GOP/s.

The cortisol in human body is a crucial biomarker in terms of wellness management, mental state monitoring and stress-related disorder diagnosis. Therefore, the rapid, reliable and facile measurement of cortisol concentration has attracted extensive research interest. However, traditional cortisol detection such as immunosensing requires demanding laboratory layout, lengthy procedures and high costs, which means, consequently, it is incompatible with the current goal of cortisol sensing. Given the contradiction, an electrochemical sensor based on molecularly imprinted polymer (MIP) for simple, efficient, non-invasive cortisol detection was proposed. The two-step approach employed is simple enough and allows for the mass production of devices. And the embedding of Prussian Blue (PB) within the MIP layer eliminates the need for complex external probes, thereby making the resultant sensors more suitable for integration into wearable devices. We firstly demonstrated the feasibility of the proposed strategy and characterized the successful formation of cavities specific to cortisol molecules. Thereafter, we measured the dependence of the current response on cortisol concentration in Phosphate Buffered Saline (PBS) buffer, which revealed a near-linear relationship between the logarithm of the cortisol concentration and the redox current from 10−9 mol/L to 10−5 mol/L, covering the optimal range of cortisol concentration in sweat. Subsequently, sensors with the same specifications were prepared and tested in PBS buffer, exhibiting good consistency. In artificial sweat, we further demonstrated that they have benign selectivity, interference immunity and great potential in practical applications.

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

Innovative functional electrical stimulation has demonstrated effectiveness in enhancing daily walking and rehabilitating stroke patients with foot drop. However, its lack of precision in stimulating timing, individual adaptivity, and bilateral symmetry, resulted in diminished clinical efficacy. Therefore, a closed-loop wearable device network of intrinsically controlled functional electrical stimulation (CI-FES) system is proposed, which utilizes the personal surface myoelectricity, derived from the intrinsic neuro signal, as the switch to activate/deactivate the stimulation on the affected side. Simultaneously, it decodes the myoelectricity signal of the patient's healthy side to adjust the stimulation intensity, forming an intrinsically controlled loop with the inertial measurement units. With CI-FES assistance, patients' walking ability significantly improved, evidenced by the shift in ankle joint angle mean and variance from 105.53° and 28.84 to 102.81° and 17.71, and the oxyhemoglobin concentration tested by the functional near-infrared spectroscopy. In long-term CI-FES-assisted clinical testing, the discriminability in machine learning classification between patients and healthy individuals gradually decreased from 100% to 92.5%, suggesting a remarkable recovery tendency, further substantiated by performance on the functional movement scales. The developed CI-FES system is crucial for contralateral-hemiplegic stroke recovery, paving the way for future closed-loop stimulation systems in stroke rehabilitation is anticipated.

Flexible tactile sensors play important roles in many areas, like human-machine interface, robotic manipulation, and biomedicine. However, their flexible form factor poses challenges in their integration with wafer-based devices, commercial chips, or circuit boards. Here, we introduce manufacturing approaches, device designs, integration strategies, and biomedical applications of a set of flexible, modular tactile sensors, which overcome the above challenges and achieve cooperation with commercial electronics. The sensors exploit lithographically defined thin wires of metal or alloy as the sensing elements. Arranging these elements across three-dimensional space enables accurate, hysteresis-free, and decoupled measurements of temperature, normal force, and shear force. Assembly of such sensors on flexible printed circuit boards together with commercial electronics forms various flexible electronic systems with capabilities in wireless measurements at the skin interface, continuous monitoring of biomechanical signals, and spatial mapping of tactile information. The flexible, modular tactile sensors expand the portfolio of functional components in both microelectronics and macroelectronics.

Recent advances in passive flying systems inspired by wind-dispersed seeds contribute to increasing interest in their use for remote sensing applications across large spatial domains in the Lagrangian frame of reference. These concepts create possibilities for developing and studying structures with performance characteristics and operating mechanisms that lie beyond those found in nature. Here, we demonstrate a hybrid flier system, fabricated through a process of controlled buckling, to yield unusual geometries optimized for flight. Specifically, these constructs simultaneously exploit distinct fluid phenomena, including separated vortex rings from features that resemble those of dandelion seeds and the leading-edge vortices derived from behaviors of maple seeds. Advanced experimental measurements and computational simulations of the aerodynamics and induced flow physics of these hybrid fliers establish a concise, scalable analytical framework for understanding their flight mechanisms. Demonstrations with functional payloads in various forms, including bioresorbable, colorimetric, gas-sensing, and light-emitting platforms, illustrate examples with diverse capabilities in sensing and tracking.

The cortisol molecules in human body is a crucial biomarker in terms of wellness management, mental state monitoring and stress-related disorder diagnosis. Therefore, the rapid, reliable and facile measurement of cortisol concentration has attracted extensive research interest. However, traditional cortisol detection such as immunosensing requires demanding laboratory layout, lengthy procedures and high costs, which means, consequently, it is incompatible with the current goal of cortisol sensing. Given the contradiction, an electrochemical sensor based on molecularly imprinted polymer (MIP) for simple, efficient, non-invasive cortisol detection was proposed. The two-step approach employed is simple enough and allows for the mass production of devices. And the synthesis of MIP involving implanted Prussian Blue (PB) could get rid of the reliance of complex external probes, leading the resultant sensors are more suitable for assembly with wearable devices. We firstly characterized the successful formation of cavities specific to cortisol molecules. Thereafter, we measured the dependence of the current response on cortisol concentration in PBS buffer, which revealed a near-linear relationship between the logarithm of the cortisol concentration and the redox current from 1 × 10−9 mol/L to 10 × 10−6 mol/L, covering the optimal range of cortisol concentration in sweat. Subsequently, sensors with the same specifications were prepared and tested in PBS buffer, exhibiting good consistency. In artificial sweat, we further demonstrated that they have benign selectivity and great potential in practical applications.

Biomechanical signals, such as strain variations of the skin, vibrations of the chest and throat, as well as motions of the limbs, hold immense significance in healthcare monitoring, disease diagnosis, and human-machine interface. Examples span from monitoring blood pressure and pulse waves for atherosclerosis diagnosis, to distinguishing between metatarsalgia patients and healthy individuals by tracking their walking postures, and to voiceprint recognition and hearing aid technology based on vibration sensing. Wearable biomechanical sensors play a crucial role in providing valuable insights into one's health condition and physiological features. However, the development of high-performance sensors capable of prolonged monitoring poses challenges. Traditional batteries have limited lifespan and pose difficulty in replacement. Using self-powered devices for the measurement of biomechanical signals represents an attractive solution to tackle the issues caused by batteries. This review focuses on the mechanisms of wearable self-powered biomechanical sensors, and delves into recent advancements in their applications, covering areas of cardiovascular system monitoring, acoustic signals detection, human motion tracking, and many others associated with biomechanics. A concluding section outlines the potential future prospects in this evolving field of materials and biomedical research. This article reviews the latest progress in wearable self-powered biomechanical sensors. Based on piezoelectric, electrostatic, and electromagnetic effects, these sensors find their potential in various applications, such as cardiovascular system monitoring, acoustic signals detection, human motion tracking, and other biomedical scenarios. Challenges in this realm lie in the development of self-powered sensors with high output performances, compatible bio-interfaces, and multi-directional sensing capabilities. image

Flexible pressure sensors capable of detecting normal and tangential forces through physical contact have garnered considerable interest in the realm of human-interactive systems. However, simultaneous detection of multi-directional forces is still a challenge for current research. Herein, a capacitive flexible pressure sensor based on a sandwich structure for three-dimensional force detection is proposed. The fabrication process of the sensor array is straightforward, capable of effectively distinguishing between normal and tangential forces. Polyimide (PI) serves as the flexible substrate for depositing the metal electrode pattern, while Polydimethylsiloxane (PDMS) acts as the intermediate dielectric layer material and the three-dimensional force conduction block. Through a comparative study of the thickness of the hollow dielectric layer, a pressure sensor with superior performance was prepared, featuring high sensitivity across a wide working range. Test results demonstrate its capability to detect normal forces ranging from 0 to 46 N (0–520 kPa) with a sensitivity of 0.442 N−1 (0.031 kPa−1) and tangential forces from 0 to 10 N with a sensitivity of 0.08 N−1 (X-axis) and 0.07 N−1 (Y-axis). The designed acquisition system can simultaneously gather data from 6 sensor arrays, totaling 240 channels, with a response time of 11 ms. This sensor array, characterized by flexibility, versatility, and a wide range, is suitable for applications in robot tactile perception.

Wearable electronics have the capability to monitor human activities and various physiological indicators in a real-time, continuous, non-invasive, and wireless manner, making them a valuable complement to traditional clinical health monitoring technologies. Currently, the adoption of specialized and reliable wearable electronics for disease diagnosis, monitoring, and treatment is flourishing. This article delves into the development of wearable electronics from a clinical application perspective, examining the application modes, mechanisms, and benefits of (1) commercial wearable electronics, (2) customized wearable electronics, and (3) customized flexible electronics in clinical settings. The focus of the article is on analyzing the diverse forms of electronic systems utilizing mechanical, thermal, optical, electrophysiological, and electrochemical technologies, in alignment with clinical application requirements. Furthermore, the article outlines the challenges encountered by wearable electronics in clinical environments and discusses potential future directions for wearable electronics in clinical applications.