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Abstract Despite extensive development of flexible pressure sensors, it is still difficult for them to simultaneously achieve high precision and a large response to subtle pressures. To address these challenges, this work demonstrates a flexible pressure sensing platform that features the reduced graphene oxide aerogel sandwiched between a polydimethylsiloxane encapsulation layer and a thin polyimide film with interdigital electrodes. The resulting pressure sensor exhibits a high sensitivity of 698.96 kPa⁻¹and a low limit of detection (~ 1 Pa), and outstanding stability over 20,000 loading/unloading cycles. Besides monitoring various physiological signals and human motions, the flexible pressure sensors can be configured into an array layout as a smart artificial electronic skin to recognize the spatial pressure distribution. The flexible pressure sensor can also be integrated with signal processing and wireless communication modules as a teleoperation system for gesture recognition, force feedback control, and kitchen food recognition, highlighting future potential toward smart robotics and human–machine interfaces. 

Abstract Mimicking the human eye’s ability to autonomously adapt to diverse and mixed illumination conditions remains a fundamental challenge in artificial vision systems. Although substantial progress has been made in materials and device engineering, current adaptive vision architectures still depend heavily on complex circuitry or algorithms and are typically restricted to uniform illumination owing to the strong intensity-dependence of photosensitivity. Here, this work presents a highly adaptive TiO₂/PEDOT:PSS photomemristor that leverages the tunable conductivity of PEDOT:PSS together with the optoelectronic response of TiO₂. The photothermal effect dynamically modulates the water absorption/desorption equilibrium in PEDOT:PSS, enabling reversible suppression or enhancement of photosensitivity under bright or dim illumination, respectively. By combining with artificial neural networks (ANNs), the artificial vision system based on TiO₂/PEDOT:PSS photomemristor arrays achieves a high accuracy of 91.3% in image recognition under mixed-light conditions—without the need for complex circuitry or algorithms. This work may establish a new approach for designing autonomous, efficient, and high-performance neuromorphic vision systems to advance the development of autonomous driving and humanoid robots. 

ABSTRACT Soft sensors have shown great promise in emerging fields such as wearable electronics, soft robotics, personalized healthcare, and human‐machine interaction. However, their practical deployment remains limited due to signal interference and cross‐sensitivity arising from simultaneous mechanical and environmental stimuli. To address these challenges, this review presents an overview of recent signal decoupling strategies for accurate and stable sensing. First, approaches for suppressing interference from individual physical parameters, such as stretching, bending, temperature, humidity, pressure, and light, are discussed. Next, array‐level crosstalk elimination techniques are reviewed to ensure reliable spatial resolution. Furthermore, advanced strategies based on spatiotemporal separation and machine learning are summarized for decoupling complex and coupled input signals. These approaches offer promising routes to simplify sensor design, reduce system complexity, and enhance signal fidelity. Finally, remaining challenges and future directions are discussed to guide the development of high‐performance, scalable, and integrable sensing systems. 

Abstract Objective. High-density surface electromyography (HD-sEMG) has shown promise as a means of providing intuitive user control of assistive devices. Using motor unit (MU) firing activities obtained from HD-sEMG, neural drive-based approaches enable continuous fingertip force prediction. MU decomposition, however, requires intensive computation and frequent calibration, which are not practical to perform daily. This study aimed to evaluate predictive model performance for continuous user control of finger forces across multiple sessions with repeated donning and doffing of the HD-sEMG arrays between each testing session and investigated the impact of simulated electrode channel loss.Approach. Eight neurotypical participants completed three sessions on separate days. In each session, index and middle fingertip forces and HD-sEMG signals from finger flexor and extensor muscles were recorded simultaneously during isometric uni- and bi-directional finger force tasks. sEMG signals were decomposed using independent component analysis to extract MU firing activities. Force prediction performances using only agonist muscles (single array) were compared to those combining agonist–antagonist (both electrode arrays) activity. Cross-day reliability was assessed by applying models derived from the first session to subsequent sessions. Robustness under electrode loss conditions was further evaluated by randomly removing selected EMG channels from data collected during later sessions.Main results. Models using both electrode arrays outperformed those using a single array across all sessions for flexion tasks (p< 0.05) but not for extension tasks. Applying models developed from session 1 data to subsequent sessions’ sEMG signals did not significantly increase average root mean square error, which rose by 2.6 ± 2.1% MVF (maximum voluntary force) in session 2 and 3 ± 2.6% MVF in session 3 compared to session 1. Simulated electrode channel loss up to 50% channels caused relatively minor, non-significant differences in prediction performance.Significance. These findings suggest robust multi-day performance and resilience to electrode loss, thereby supporting the use of neural-drive-based approaches for continuous user control of assistive devices. 

Abstract L‐cysteine plays a significant role in numerous physiological processes in the human body health, and monitoring its level has important clinical implications. However, L‐cysteine tests are limited to invasive blood drawing and a time‐consuming laboratory process, which also fails to give real‐time and continuous results. This study introduces a flexible electrochemical sensor based on a self‐assembled monolayer (SAM) of 3‐mercaptopropyltrimethoxysilane (MPTS) molecular receptor and catalytic titanium oxide‐modified carbon cloth (MPTS/TiO₂/CC). The MPTS and TiO₂modification serve as the artificial antibody through thiol interactions and the charge transfer layer, respectively, which is further confirmed by density function theory (DFT) calculations. The flexible L‐cysteine sensor based on MPTS/TiO₂/CC shows a wide linear range of up to 500 µmand an ultra‐low limit of detection of 0.046 µm. A sweat patch consisting of an electrochemical sensing unit, a laser‐fabricated microfluidic channel, and a miniaturized electrochemical workstation affords real‐time monitoring of L‐cysteine detection in dynamic environments and reveals the strong correlation between sweat and blood L‐cysteine concentrations. The introduction of molecular receptors and band alignment may offer a general way of designing sensitive, specific electrochemical sensors for point‐of‐care diagnosis. 

Abstract Skin‐like robust materials with prominent sensing performance have potential applications in flexible bioelectronics. However, it remains challenging to achieve mutually exclusive properties simultaneously including low interfacial impedance, high stretchability, sensitivity, and electrical resilience. Herein, a material and structure design concept of mixed ion‐electron conduction and mechanical interlocking structure is adopted to fabricate high‐performance mechanical‐bioelectrical dual‐modal composites with large stretchability, excellent mechanoelectrical stability, low interfacial impedance, and good biocompatibility. Flower‐like conductive metal‐organic frameworks (cMOFs) with enhanced conductivity through the overlapped level of metal‐ligand orbital are assembled, which bridge carbon nanotubes (denoted as cMOFs‐b‐CNTs). Then, precursor of poly(styrene‐block‐butadiene‐block‐styrene)/ionic liquid penetrates the pores and cavities in cMOFs‐b‐CNTs‐based network fabricated via filtration process, creating a semi‐embedded structure via mechanical interlocking. Thus, the mixed ion‐electron conduction and semi‐embedded structure endow the as‐prepared composites with a low interfacial impedance (51.60/28.90 kΩ at 10/100 Hz), wide sensing range (473%), high sensitivity (2195.29), rapid response/recovery time (60/85 ms), low limit of detection (0.05%), and excellent durability (>5000 cycles to 50% strain). Demonstrations of multifunctional mechanical‐bioelectrical dual‐modal sensors for in vivo/vitro monitoring physiological motions, electrophysiological activities, and urinary bladder activities validate the possibility for practical uses in biomedical research areas. This concept creates opportunities for the construction of durable skin‐like sensing materials. 

Abstract Although continuous and non‐invasive measurements of sweat biomarkers may provide vital health information, sweat collection often involves intense physical activities or chemical/thermal stimuli. The natural body sweat during endogenous metabolic or stress processes, secreted at much lower rates at rest, may be continuously analyzed using microfluidic devices integrated with hydrophilic rigid fillers; however, the sweat uptake and accumulation in thermoregulatory processes take too long for near‐real‐time measurements. This work provides an innovative body fluid collection strategy using a granular hydrogel scaffold (GHS), facilitating osmotic and capillary effects to uptake and transfer an ultralow amount of sweat into a microfluidic device at rest. Taken together with a spiral microfluidic channel, the GHS‐embedded microfluidics reduce the evaporation of collected sweat and store it in a sensing well for near‐real‐time measurements. Integrating the sweat‐collecting system with an enzymatic gold‐graphene nanocomposite‐modified laser‐induced graphene (LIG) electrode and a LIG‐based pH sensor enables the accurate continuous on‐body detection of sweat lactate during normal daily activities at a low perspiration rate. The novel combination of a GHS‐integrated microfluidic system with a low‐cost, flexible, sensitive, and stable LIG‐based sensing system provides an accessible technology for sweat‐based biosensing during normal daily activities. 

Abstract The cost‐effective and scalable synthesis and patterning of soft nanomaterial composites with improved electrical conductivity and mechanical stretchability remains challenging in wearable devices. This work reports a scalable, low‐cost fabrication approach to directly create and pattern crumpled porous graphene/NiS₂nanocomposites with high mechanical stretchability and electrical conductivity through laser irradiation combined with electrodeposition and a pre‐strain strategy. With modulated mechanical stretchability and electrical conductivity, the crumpled graphene/NiS₂nanocomposite can be readily patterned into target geometries for application in a standalone stretchable sensing platform. By leveraging the electrical energy harvested from the kinetic motion from wearable triboelectric nanogenerator (TENG) and stored in micro‐supercapacitor arrays (MSCAs) to drive biophysical sensors, the system is demonstrated to monitor human motions, body temperature, and toxic gas in the exposed environment. The material selections, design strategies, and fabrication approaches from this study provide functional nanomaterial composites with tunable properties for future high‐performance bio‐integrated electronics. 

Abstract Objective.Accurate prediction of motor unit (MU) discharge activity from surface electromyogram (sEMG) signals is critical for understanding neuromuscular control and for enabling practical neural interface applications. However, current MU decomposition approaches rely on person-specific data, limiting their generalizability.Approach.We developed a cross-person decomposition framework and validated the algorithm using synthesized high-density sEMG data by convoluting simulated MU firing spike trains with action potential templates derived from human experimental data. We first obtained separation matrix from multiple training subjects and applied them to decompose sEMG signals from unseen test subjects. This allowed us to obtain MU spike trains. The predicted outcomes were then compared with the ground truth across multiple metrics, including spike detection accuracy, MU firing rate (FR), waveform similarity of MU action potentials (MUAPs), and MU recruitment thresholds.Main results. Our results demonstrated strong agreement between predicted and true MU activity. Specifically, we found high $R^2$ values (⩾0.95) for the populational FR, and the coefficient of variation of FR remained stable across different MU retention thresholds. The MU similarity analyzes revealed that the predicted MUAPs closely matched ground truth counterparts both in waveform shape and spatial distribution. Furthermore, recruitment thresholds exhibited strong linear relation ( $R^2$ =  0.98 ± 0.006) with minimal error.Significance. These findings demonstrate the feasibility of efficient cross-person MU decomposition with minimal accuracy loss, laying the groundwork for generalized, plug-and-play myoelectric systems in neurophysiology, neuroprosthetic, and rehabilitation applications. 

Abstract Nanomaterial‐based stretchable electronics composed of conductive nanomaterials in elastomer can seamlessly integrate with human skin to imperceptibly capture electrophysiological signals. Despite the use of transfer printing to form embedded structures, it remains challenging to facilely and stably integrate conductive nanomaterials with thin, low‐modulus, adhesive elastomers. Here, a facile‐yet‐simple laser‐induced graphene (LIG)‐assisted patterning and transfer method is demonstrated to integrate patterned silver nanowires onto an ultra‐low modulus silicone adhesive as ultra‐conformal epidermal electrodes. The resulting thin epidermal electrodes of ≈50 µm exhibit a low sheet resistance (0.781 Ω sq⁻¹), tissue‐like Young's modulus (0.53 MPa), strong self‐adhesion, and excellent breathability. The breathable electrodes dynamically conformed to the skin with low contact impedance allow for long‐term, high‐fidelity monitoring of electrophysiological signals in complex environments (even during exercise and heavy sweating). Moreover, the LIG‐assisted transfer can provide a robust interface to establish a stable connection between the soft electrodes and rigid hardware. The large‐scale fabrication further provides an eight‐channel electromyography system combined with a deep learning algorithm for gesture classification and recognition with remarkable accuracy (95.4%). The results from this study also provide design guidelines and fabrication methods of the next‐generation epidermal electronics for long‐term dynamic health monitoring, prosthetic control, and human‐robot collaborations.