Prokaryotic transcription factors (TFs) serve as small molecule biosensors with broad applications in biotechnology, yet only a fraction have been characterized. To address this gap, we recently described the bioinformatic method Ligify, which leverages information from genome context and enzyme reaction databases to predict a TF's cognate effector molecule. Here, we report Ligify 2.0, a modern web server for Ligify predictions. We systematically evaluate 10 965 small molecules within the Rhea enzyme reaction database for associations to TFs, ultimately generating 13 435 hypothetical interactions between 1 362 small molecules and 3 164 TFs. We then develop an interactive web server (https://ligify.groov.bio) to search and visualize prediction data. Each TF sensor page includes visualizations for chemical ligand structures, interactive TF protein structures, and genome context. Pages also include metadata links, predicted promoter sequences, prediction confidence metrics, and references to relevant literature. A plasmid builder tool enables users to generate custom biosensor circuit designs. Finally, we provide case studies using Ligify 2.0 to identify two TFs from the pathogens Escherichia coli O157:H7 and Mycobacterium abscessus responsive to 4-hydroxybenzoate and Pseudomonas Quinolone Signal, respectively. The Ligify web server aims to facilitate the systematic characterization of biosensors for chemical-control of biological systems.
BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying recognition and treatment. Near-field radio-frequency (NFRF) sensors offer touchless, covert cardiopulmonary monitoring that may be better tolerated than tethered devices.ObjectivesTo assess the feasibility and acceptability of NFRF bed sensor for home monitoring of PLWD and to estimate machine-learning (ML) performance for detecting respiratory distress.MethodsIn a 48-hour pilot study, PLWD were recruited from a geriatrics practice. A lab-designed NFRF bed sensor recorded cardiopulmonary waveforms. Recorded video enabled minute-level Respiratory Distress Observation Scale scoring as reference. Feasibility outcomes included adverse events, acceptability, and percentage of usable data. ML classifiers (eg, random forest, k-nearest neighbors) were evaluated using 5-fold cross-validation, and class imbalance was addressed through data augmentation.ResultsTen patient-legally authorized representative dyads were enrolled. No adverse events were reported, and no participants intentionally removed the sensor. Usable data averaged 52% (range 34-68%). Caregivers reported minimal burden and no patient distress. With augmented data, the random forest performed best, achieving 74.6% sensitivity and 95.5% specificity in detecting RDOS scores.ConclusionsNFRF bed sensors were feasible and acceptable to implement in the home setting with PLWD, with promising ML-based detection of respiratory distress. Larger, longer studies with a broader range of RDOS severity are needed to validate performance and refine deployment. As this technology develops and matures, it could provide a method for non-invasive continuous monitoring to detect respiratory distress in PLWD in palliative care settings.
To determine if early spontaneous leg movements of infants differentiate risk status for cerebral palsy (CP) in infants who had a perinatal brain injury. Infants born at risk for CP (due to a perinatal brain injury) were classified as high risk for CP (n = 33) or not at high risk (n = 21), at 4 months of age, using General Movements Assessment and Test of Infant Motor Performance. Full-day leg movement data were recorded using wearable inertial sensors on each ankle (2 days per month, from age 1 to 4 months). Kinematic parameters were derived from accelerometer and gyroscope data as follows: (1) number of leg movements per hour awake; (2) median leg movement acceleration; (3) median leg movement duration; and (4) fuzzy entropy of peak leg movement acceleration. The leg kinematic parameters measured at month 1 and month 4 were analyzed using mixed-effects models. Infants classified as high risk for CP had statistically significantly fewer number of leg movements per hour awake and shorter median leg movement duration than infants not at high risk (P= 0.0322 and P= 0.0023, respectively). The spontaneous movements of infants can be characterized in their natural environments using wearable sensors and have potential to inform recommendations for early intervention and follow-up with experts in CP diagnosis.
As global populations age rapidly, extending healthy lifespan has become a major public health priority. Physical exercise is widely recognized as a key strategy to slow functional decline and promote healthy aging, but its effectiveness and optimal prescription likely vary across individuals and should be evaluated using objective technologies and validated biomarkers. This review summarizes recent developments in technology-assisted physical activity and examines how wearable sensors, tele-exercise platforms, and digital health applications can improve adherence and enable individualized interventions for older adults. It also discusses how biological aging biomarkersons for oldepigenetic clocks, senescence-associated secretory phenotype (SASP) markers, and organ-specific plasma proteomicss, and organsto quantify exercise-related changes in biological aging and support mechanistic interpretation. This review discusses current translational challenges and future research directions, and proposes a biomarker-informed precision exercise anti-aging framework to support healthy aging through innovative technology-assisted physical activity interventions. Specifically, we ask: (i) which technology modalities and intervention components most effectively support sustained, individualized physical activity in older adults, and (ii) which validated biological aging biomarkers can serve as actionable endpoints to quantify geroprotective effects.
The eddy current sensor is a representative nondestructive sensing technique that enables sensitive interrogation of the conductive object surface in close proximity. Here, we propose an eddy current-based proximity detection strategy using a micropatterned inductor fabricated on a thin-film substrate (25 μm thickness) for integration as a smart, in situ inspection modality in advanced capillary pick-and-place systems. Notably, the eddy current sensing mechanism exhibited small sensitivity to liquids, allowing successful proximity detection during capillary pick-and-place operations. Our new suggested sensor consists of a copper inductor micro-fabricated on one side of a thin polyimide film surface, while the other side is roughened in nanoscale by argon plasma treatment. This new design offers several advantages: (1) enhanced eddy current induction efficiency enabled by thickness reduction, achieving an inductance change of up to 54.7% upon copper contact; and (2) facile release of lightweight objects due to reduced surface adhesion during the pick-and-place process. We verified the electrical and eddy current sensing characteristics of the micropatterned inductors via experiments and finite element simulations. We then further explored the in situ sensing capability during capillary pick-and-place operation by measuring the impedance change over time. The decreased adhesion of the plasma-treated polyimide surface was verified by atomic force microscopy and supported by an analytical model. We envision that this approach provides a promising strategy for advanced manufacturing applications requiring high reliability and nondestructive in situ monitoring during pick-and-place operations.
Suboptimal ergonomics during colonoscopy procedures can lead to physical discomfort, fatigue and musculoskeletal conditions for the endoscopist. Measurement of possible risks is usually done using subjective ergonomic reports or involving trained observers who follow methods such as RULA and REBA. This paper introduces the use of IMUs for gathering motion data from colonoscopy practitioners and generating RULA and REBA reports automatically. Eight endoscopists performed two simulated functional colonoscopy procedures while wearing an inertial-magnetic motion tracker suit, the MTw Awinda by Xsens. The procedure was recorded while IMUs tracking data was captured and processed by the Xsens MotionCloud for automatic REBA and RULA report generation. Physicians also answered a NASA-TLX questionnaire to subjectively measure comfort and workload. Automatic REBA and RULA reports show higher ergonomic risk for novices, while subjective questionnaire results mismatch. The experiment indicates the usability of the system for automatic RULA and REBA generation. This study evaluates inertial-sensor motion tracking with automatically generated RULA and REBA ergonomic assessments during simulated colonoscopy. Eight clinicians completed procedures while full-body kinematics were recorded. Automated scores differentiated novices from experts, demonstrating a feasible and objective alternative to observer-based evaluations while supporting improved ergonomic assessment in endoscopy practice.
Wearable technology has become increasingly important for health monitoring, sports performance, and ergonomic assessments because it enables continuous, non-invasive, and real-time tracking of physiological and biomechanical signals in real-world environments, overcoming limitations of laboratory-based assessments. This paper presents the development, testing, and initial study of a sensor-embedded loose garment designed for motion analysis using conductive ink. Sensors were strategically placed across key areas of the T-shirt to capture comprehensive motion data from the torso. Positioned on the chest, shoulders, ribcage, and lower torso, these sensors detect detailed movements. The study evaluates various sensor combinations with four classifiers-XGBoost, RandomForest, SVM, and K-Nearest Neighbors-using data from ten sensor locations analyzed with three holdout methods (20-80%, 30-70%, and 50-50%). Results underscore the impact of specific sensor placements, with combinations on the shoulder, ribcage, and abdomen yielding the highest accuracy. This work advances textile-based motion recognition, showing the potential for wearable technology to distinguish among eight movements in a loose garment.
Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with frequent exacerbations of COPD (ECOPD) significantly impacting patient health and health care systems. Predicting ECOPD early would increase patients' quality of life and decrease the economic burden. The advancement of wearable technologies and Internet of Things (IoT) sensors has enabled continuous remote monitoring (RM), offering new opportunities for early ECOPD prediction. However, effectively leveraging wearable data requires robust artificial intelligence (AI) frameworks capable of processing heterogeneous physiological and environmental information. This systematic review aims to provide a comprehensive overview of both hardware and software solutions for predicting ECOPD using RM. From the reviewed literature, we first focus on key physiological and environmental variables essential for COPD monitoring that can be extracted from wearables and IoT sensors. Second, we describe the wearable and IoT devices currently deployed in COPD management. Finally, we review machine learning, including deep learning models, used for ECOPD prediction, discussing limitations for real-world implementation. By bridging AI-driven data processing with real-world sensor applications, this review aims to outline the current landscape, existing challenges, and future directions for developing effective RM solutions for ECOPD predictions. A comprehensive search was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies using AI or machine learning techniques for predicting ECOPD in in-home contexts. This review identified 26 studies that met the inclusion criteria. Twenty studies aimed at predicting or detecting exacerbations at the onset. The variables tracked most frequently were heart rate (n=9), peripheral oxygen saturation (n=9), and symptoms (n=8). Daily or weekly sampling was most common (n=14). Most studies (n=13) applied machine learning models-primarily random forest (n=5), CatBoost (n=2), decision trees (n=2), and support vector machines (n=2). Deep learning was used in 3 papers, while the remaining applied rule-based logics and probabilistic models. Wearables and IoT were used in only 6 out of 20 studies. Six papers analyzed changes in vital parameters during prodromal phases, defined as the period shortly before the onset of an exacerbation. Three studies collected data continuously, 2 daily, and 1 compared once-daily versus overnight monitoring; 4 of these 6 used wearable devices. Overall, current evidence highlights the potential of continuous monitoring of physiological and environmental variables for early ECOPD prediction, offering advantages over questionnaires or once-daily measurements. While wearables and IoT devices show promise, their use remains limited. Many studies rely on balanced datasets that do not mirror real-world exacerbation patterns and lack external validation across diverse populations. Future research should emphasize large-scale validation, integration of multimodal data, and translation of AI models into clinically feasible tools to enable timely intervention and improve COPD management.
Laser-induced graphene (LIG) is widely utilized in flexible sensors because of its simple fabrication and low cost. However, transferring LIG to PDMS results in insufficient conductivity and high overall device resistance, which limits its application in high-sensitivity signal detection. Liquid metal (LM), characterized by high conductivity and low resistance, offers a promising pathway to enhance the sensing performance of LIG. This study proposes a hierarchical composite structure that adopts the liquid metal/polydimethylsiloxane (LP) composite strategy to prepare liquid metal graphene flexible sensors (LM-LIGFS). By adopting this composite strategy, the resistance of the pure LIG flexible sensor has been reduced by approximately 50%, significantly enhancing sensitivity. Furthermore, the composite sensor demonstrates excellent dynamic response capabilities under various pressures, frequencies, and grip strengths, featuring both rapid response times (loading: 0.138 s/unloading: 0.234 s) and high stability (over 9000 s of repetitive bending). Additionally, the sensor possesses superior waterproof properties and generates distinct responses at different underwater depths. Finally, the potential of the LM-LIG flexible sensor in correcting human breaststroke movements was demonstrated. These attributes make the LM-LIG sensor a low-cost and green solution for wearable electronics.
Misfolded α-synuclein (αS) is significantly associated with the onset and progression of Parkinson's disease (PD), a critical neurodegenerative disorder. In particular, small-sized aggregated αS oligomers are more cytotoxic than mature amyloid-type aggregates and are essential inducers of PD. Solid-state nanopores, functioning as single-molecule sensors, are highly effective for identifying native proteins and determining conformational changes with high resolution. However, nanopore sensors based on the resistive pulse sensing principle face challenges in identifying target molecules for highly heterogeneous biomolecules or bioparticles such as αS oligomers. Herein, we report an approach for the quantitative determination of αS oligomers varying excluded volumes using 10 nm diameter silicon nitride (SiNx) nanopores assisted by a circular single-stranded DNA (CssDNA) frame. This frame comprises an aptamer designed to specifically capture αS oligomers. The αS oligomers can be identified and classified into four different species, from dimers to heptamers, by analyzing the events based on particular subpeaks produced by the complexes of the CssDNA frame incubated with αS monomers for 2 h and translocation through the nanopores. CssDNA frame also captured αS oligomers with 0.5 h incubation, and the detected oligomers are primarily dimers. Finally, the selectivity of CssDNA frame is confirmed by detecting and analyzing the complexes formed by the CssDNA frame incubated with β-lactoglobulin, Aβ1-42 and tau proteins. The developed approach is a promising alternative for achieving the quantitative analysis of αS oligomers at single-molecule levels.
Recent studies indicate that both sprayed and condensed water droplets can spontaneously generate hydroxyl radicals (•OH) via interfacial reactions and contact electrification, leading to the formation of hydrogen peroxide (H2O2). However, their behavior under natural conditions, such as in tiny rain droplets, remains poorly understood. Here, we performed on-site electroanalysis of single rain droplets (5 μL) and demonstrated the spontaneous, catalyst-free formation of detectable H2O2 levels within tiny droplets on natural leaf surfaces. Notably, the amount of H2O2 is influenced by leaf surface topography and hydrophobicity (Rosa vs Buxus leaves as model systems), sunlight exposure versus shade─likely due to temperature rather than UV effects─and the pH of rain droplets. Using a combined electrode (≈1.2 mm diameter), we achieved direct electrochemical detection of H2O2 in single droplets with a detection limit of 0.27 ppm (∼8 μM), complemented by parallel colorimetric assays for validation. Controls excluded UV or photosynthetic interference, and repeated analyses on artificial plastic surfaces and behind glass (blocking UV radiation) confirmed that H2O2 formation originates solely from the droplet interface (∼5 μL), consistent with previous studies of artificial droplet studies. In contrast, bulk rainwater (0.5 mL) or large droplets showed no detectable oxidants. Importantly, this study focuses not on H2O2 sensing but on revealing natural phenomena and their regulating factors on leaves, as probed by miniaturized sensors and optical kits. These findings reveal previously hidden interfacial chemistry, demonstrating that tiny rain droplets can act not only as cleansing agents but also as natural oxidants, potentially providing protection against microbial contamination and localized biocorrosion, although further studies on natural rain droplets are needed. The results further suggest that similar processes may occur in aerosols, dew, and fog, highlighting the need for further investigation of spontaneous reactive oxygen species formation in natural droplets.
Two-dimensional porphyrin-based metal-organic frameworks (2D Por-MOFs) have emerged as promising candidates in biomedical applications due to their ultrathin morphology, high surface area, tunable electronic properties, and excellent optical characteristics. This review systematically summarizes recent advances in their utilization for cancer therapy, antibacterial treatment, and biosensing. In oncology, 2D Por-MOFs serve as efficient photosensitizers for photodynamic therapy (PDT) by generating reactive oxygen species (ROS) to eradicate tumor cells, while also enabling synergistic therapeutic outcomes through integration with chemodynamic therapy (CDT), chemotherapy, immunotherapy, sonodynamic therapy (SDT), and novel mechanisms such as copper-dependent cell death. For antibacterial applications, these materials enhance ROS production via size engineering, single-atom modification, or nanozyme loading, effectively killing pathogens and promoting wound healing, as well as being incorporated into smart dressings to achieve combined hemostatic and antimicrobial functions. In biosensing, 2D Por-MOFs act as ideal platforms for photoelectrochemical signal transduction or fluorescent probes, facilitating the development of highly sensitive fiber-optic SPR, electrochemical, and fluorescence sensors capable of detecting disease biomarkers, pathogens, small-molecule metabolites, and ions with high sensitivity. Finally, the current challenges and future prospects for the clinical translation of 2D Por-MOFs are discussed.
Adolescent idiopathic scoliosis bracing is best conceptualized as a dose-dependent therapy in which clinical benefit depends on the delivered, rather than prescribed, corrective exposure. This narrative review synthesizes post-2015 evidence to present a clinically oriented framework for "precision bracing" that links brace prescription to objectively measured delivered dose and subsequent clinical response. We summarize evidence that bracing reduces progression risk in skeletally immature patients, while highlighting the efficacy-effectiveness gap driven by incomplete adherence. We review objective adherence monitoring technologies, focusing on temperature-based data loggers and emerging force or pressure sensing approaches, and explain how objective dosing data refine outcome interpretation by distinguishing undertreatment from true treatment failure. We conceptualize brace dose as a multidimensional construct that includes quantity (objectively measured wear time), pattern (regularity), and correction-related indicators (including in-brace and early out-of-brace radiographic metrics and, when available, interface force surrogates). We synthesize key effect modifiers, including skeletal maturity, curve magnitude and phenotype, early radiographic response, and contextual determinants of achieved wear time. We translate this evidence into a practical clinical framework for risk stratification, brace schedule selection, objective monitoring, early reassessment, and escalation when delivered dose is inadequate or response is unacceptable We conclude by outlining research priorities for standardized reporting of delivered dose, pragmatic trials embedded in routine care, and core outcome sets that integrate radiographic and patient-reported outcomes. Many teenagers develop a sideways curve of the spine called adolescent idiopathic scoliosis. For some, wearing a brace can slow down curve progression and reduce the chance of needing surgery. In everyday practice, brace treatment works best when it fits well into daily life and when clinicians, patients, and families can understand how much treatment is actually being delivered. This review looks at bracing as a “dose-dependent” treatment. This means the benefit depends on the dose that is actually delivered, not just the number of hours written on a prescription. We reviewed research published since 2015 on how to measure brace wear objectively (for example, using small temperature sensors), how brace design is becoming more personalised (including computer-designed braces), how full-time and night-time schedules compare, what is known about younger children with scoliosis, and how bracing affects quality of life. Across studies, objective monitoring often showed that real-world brace use differed from self-report. Early wear patterns in the first weeks helped predict later wear. More time in the brace was generally linked to better curve control, and for some outcomes benefits continued above 18 hours per day. At the same time, “hours worn” is not the whole story; how well the brace corrects the spine and how consistently it is worn also matter. These findings support a practical “precision bracing” approach: set clear targets, measure real-world brace use, check early response, address comfort and psychosocial concerns, and adjust the plan promptly to achieve an effective, tolerable dose.
Plant water potential is a central integrator of plant water status, linking hydraulic function with physiological performance and ecosystem water dynamics across species and systems. This review is motivated by the need to capture these dynamics under rapidly changing environmental conditions, which are often missed by discrete measurements. We evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote-sensing proxies. We discuss the principles, measurement mechanisms, practical constraints, and environmental sensitivities of each approach. Relative to traditional methods, such as pressure chambers, continuous measurements offer major advantages by resolving rapid variation in water status and strengthening inference on plant-soil-atmosphere interactions. These approaches are especially valuable under dynamic field conditions, where temporal variability in vapor pressure deficit, soil moisture, temperature, and radiation strongly shapes hydraulic behavior. We conclude that continuous monitoring has substantial potential to advance plant and ecosystem science, but wider application will depend on careful interpretation and greater harmonization across comparable methodologies. By synthesizing core principles, methodological challenges and best practices, this review provides a practical framework for researchers and practitioners applying continuous water potential measurements.
Extreme environments impose significant demands on human physiological regulation, yet the specific environmental determinants of cardiac autonomic function during prolonged field deployments remain incompletely characterized. This study investigated associations between environmental conditions and heart rate variability during a 49-day austral summer expedition at the Johann Gregor Mendel Czech Antarctic Station on James Ross Island. Twelve participants were monitored using wearable electrocardiogram devices. Environmental data were recorded continuously from automatic weather stations and indoor sensors. Linear mixed-effects models with random intercepts and slopes for outdoor temperature were employed to investigate the relationships between environmental conditions and heart rate variability. Lower outdoor temperatures were associated with increased parasympathetic activity, with significant negative associations observed for RMSSD ([Formula: see text] [Formula: see text]), pNN50 ([Formula: see text], [Formula: see text]), SDNN ([Formula: see text], [Formula: see text]), and sample entropy ([Formula: see text], [Formula: see text]). Warmer indoor temperatures were independently associated with enhanced vagal modulation (RMSSD: [Formula: see text], [Formula: see text]; pNN50: [Formula: see text], [Formula: see text]; SDNN: [Formula: see text], [Formula: see text]). Lower outdoor temperatures were also associated with significantly longer R-R intervals ([Formula: see text], [Formula: see text]), consistent with cold-induced cardiac slowing. A progressive decline in heart rate variability was observed during the expedition, with RMSSD decreasing by approximately 22% over the 49-day period, while heart rate showed no significant temporal trend. Substantial inter-individual variability in autonomic temperature sensitivity was documented. These findings demonstrate that environmental conditions are associated with cardiac autonomic function through potentially distinct pathways, with implications for occupational health monitoring and the optimization of indoor climate in extreme environments.
The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning-based methods, it is challenging to perform information fusion from the temporal perspective due to the complexities in fusing heterogeneous sensor data and establishing long-term context correlations. This paper proposes a novel triple spectral fusion framework tailored for HAR. First, we develop an adaptive complementary filtering technique for noise suppression and organize each IMU's sensors into posture and motion modality nodes. Given that IMU nodes form a dynamic heterogeneous graph, we then apply adaptive filtering within the graph Fourier domain to merge both homogeneous and heterogeneous node information. Furthermore, an adaptive wavelet frequency selection approach is implemented to suppress context redundancy and shorten the length of features. This approach enhances both timestamp-based graph aggregation and the correlation of long-term contexts. Our framework uses adaptive filtering in the Fourier, graph Fourier, and wavelet domains, enabling effective multi-sensor fusion and context correlation. Extensive experiments on ten benchmark datasets demonstrate the superior performance of our framework. Project page: https://github.com/crocodilegogogo/TSF-TPAMI2026.
Driven by the "More than Moore" law, the miniaturization and multi-functional integration of micro-energy units and micro-sensors are crucial for next-generation compact microsystems. To address severe signal oscillation and unstable energy supply under continuous extreme overload (>10 000 g), this study proposes a Sensing-in-Energy (SiE) microdevice featuring a MEMS movable inertial structure built into a supercapacitor electrolyte cavity. This architecture leverages high-g shock-driven transient contact between embedded metal microspheres and the electrode to modulate the soft short-circuit sensing effect. By utilizing electrolyte damping for signal self-filtering, the device achieves a high-range (30 000 g) and large-amplitude (450 mV) output with weak oscillation (signal adhesion coefficient reduced by 90.84%). To ensure precise development of the SiE microdevice, a multi-physics-driven design method coupling transient fluid-structure interaction (FSI) and micro-nano scale rough electrical contact theory was established. This reveals the dynamic mapping laws from mechanical excitation to fluid-structure coupling interface response and electrochemical output, reducing simulation-experiment error to within 8%. Furthermore, a high-precision microsphere embedding process was developed to minimize manufacturing randomness, maintaining signal repeatability error below 10%. This work offers a new paradigm for designing SiE microdevices for extreme environments and lays a technical foundation for the development of future high-performance heterogeneous microsystems.
This literature review critically examines the design, validation, and application of non-invasive in-ear electroencephalography (ear-EEG) systems as emerging wearable platforms for long-term neurophysiological monitoring and intervention. Following PRISMA guidelines, studies published between 2010 and 2025 were systematically selected from four major databases and organized into four thematic domains: in-ear wearable system design and validation, multimodal sensing and stimulation, embedded intelligence, and brain-state monitoring and rehabilitation. The review focuses exclusively on wearable, ear-centered EEG technologies, explicitly excluding cochlear implants and other invasive or behind-the-ear systems. We analyze key engineering challenges unique to ear-EEG, including electrode placement constraints, mechanical-electrical coupling, motion robustness, power efficiency, and long-term wearability. The review highlights a growing transition toward compact, wireless ear-EEG systems with on-device signal processing and embedded machine learning, enabling real-time brain-state estimation under ambulatory conditions. Multimodal integration, combining ear-EEG with complementary sensors such as EOG, inertial units, and cardiovascular signals is shown to improve artifact awareness, contextual interpretation, and closed-loop capability. Beyond summarizing existing technologies, this review identifies critical gaps limiting clinical translation, including the lack of standardized validation protocols, limited embedded autonomy, and underexplored closed-loop neurofeedback and neuromodulation architectures. By synthesizing advances across hardware design, signal processing, and intelligent system integration, this work provides a systems-level roadmap for the future development of wearable, intelligent, and clinically robust ear-EEG platforms for mental health, neurorehabilitation, and continuous brain monitoring.
Recognizing fine-grained hand and arm gestures, especially those that occur naturally in daily activities, remains a challenge in wearable-based human activity recognition. This dataset supports fine-grained gesture recognition using wearable inertial sensors, with a focus on distinguishing subtle daily activities such as liquid ingestion and similar upper-body gestures. Fifty volunteers participated in controlled recording sessions, each performing predefined gestures including answering a phone call, scratching the head, adjusting glasses, passing the hand over the face, holding the chin, and stretching the arms behind the neck. Data were collected from a WT901BLECL5 sensor placed on the dominant wrist, capturing tri-axial accelerometer and gyroscope readings at 200 Hz. Real-time annotations were performed via a custom mobile application synchronized with sensor acquisition. The dataset is provided as CSV files, structured both by segmented gesture occurrences and by continuous recordings, with each sample labeled using standardized gesture identifiers. This structure facilitates straightforward reuse for machine learning tasks such as gesture classification, activity recognition, and sequence modeling. The dataset is expected to support the development of robust models for real-world wearable gesture recognition applications.
A flexible sensor has been developed comprising a bismuth film and a gold nanoparticle-decorated laser-induced graphene electrode (Bi/AuNP/LIGE) for the concurrent detection of Zn²⁺ and Cd²⁺ using differential pulse anodic stripping voltammetry (DPASV). The sensor was thoroughly characterized, and the key experimental parameters, including buffer composition, deposition potential, accumulation time, and pH, were systematically optimized. The optimal performance was achieved in borate buffer (pH 7.5) with a deposition potential of -1.1 V and an accumulation time of 150 s. Under these conditions, the detection limits are 0.072 µM for Zn²⁺ and 0.031 µM for Cd²⁺, with quantification limits of 0.24 µM and 0.103 µM, respectively. The sensor demonstrates good reproducibility and accuracy when tested on seawater and soil samples. These findings show that the Bi/AuNP/LIGE platform offers enhanced sensitivity and selectivity, paving the way for advanced sensors in portable, on-site monitoring systems.