Search Results (217)

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

Detecting alcohol intoxication is crucial for preventing accidents and enhancing public safety. Traditional intoxication detection methods rely on direct blood alcohol concentration (BAC) measurement via breathalyzers and wearable sensors. These methods require the user to purchase and carry external hardware such as breathalyzers, which is expensive and cumbersome. Convenient, unobtrusive intoxication detection methods using equipment already owned by users are desirable. Recent research has explored machine learning-based approaches using smartphone accelerometers to classify intoxicated gait patterns. While neural network approaches have emerged, due to the significant challenges with collecting intoxicated gait data, gait datasets are often too small to utilize such approaches. To avoid overfitting on such small datasets, traditional machine learning (ML) classification is preferred. A comprehensive set of ML features have been proposed. However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. This study evaluates 27 signal processing features handcrafted from accelerometer gait data across five domains: time, frequency, wavelet, statistical, and information-theoretic. The data were collected from 24 subjects who experienced alcohol stimulation using goggle busters. Correlation-based feature selection (CFS) was employed to rank the features most correlated with alcohol-induced gait changes, revealing that 22 features exhibited statistically significant correlations with BAC levels. These statistically significant features were utilized to train supervised classifiers and assess their impact on alcohol intoxication detection accuracy. Statistical features yielded the highest accuracy (83.89%), followed by time-domain (83.22%) and frequency-domain features (82.21%). Classifying all domain 22 significant features using a random forest model improved classification accuracy to 84.9%. These findings suggest that incorporating a broader set of signal processing features enhances the accuracy of smartphone-based alcohol intoxication detection. Full article

Fluorescence microscopy enabled by smartphone-coupled 3D instruments has shown utility in different biomedical applications ranging from diagnostics to biomanufacturing. Recently, we have designed and developed these devices and have demonstrated their utility in micro-nano particle sensing and leukocyte imaging. Here, we present a novel application for enhancing the imaging performance of smartphone fluorescence microscopes (SFM) and reducing their operational complexity. Computational noise correction is employed using 3D Averaging and 3D Gaussian filters of different kernel sizes (3 × 3 × 3, 7 × 7 × 7, 11 × 11 × 11, 15 × 15 × 15, and 21 × 21 × 21) and various standard deviations σ (for Gaussian only). Fluorescent beads of different sizes (8.3, 2, 1, 0.8 µm) were imaged using a custom-designed SFM. The application of the computational filters significantly enhanced the signal quality of particle detection in the captured fluorescent images. Amongst the Averaging filters, a kernel size of 21 × 21 × 21 produced the best results for all bead sizes, and similarly, amongst Gaussian filters, σ equal to 5 and a kernel size equal to 21 × 21 × 21 produced the best results. This visual improvement was then quantified by calculating the signal-difference-to-noise ratio (SDNR) and contrast-to-noise ratio (CNR) of filtered and unfiltered original images using a custom-developed quality assessment algorithm (AQAFI). Lastly, noise correction using Averaging and Gaussian filters with the previously identified optimal parameters was applied to images of fluorescently tagged human peripheral blood leukocytes captured using an SFM under various conditions. The ubiquitous nature and simplistic application of these filters enable their utility with a range of existing fluorescence microscope designs, thus allowing us to enhance their imaging capabilities. Full article

Smartphone-embedded inertia sensors are widely available nowadays. We have developed a smartphone application that could assess temporal gait characteristics using the built-in inertia measurement unit with the aim of enabling mass screening for gait abnormality. This study aimed to examine the test–retest reliability and concurrent validity of the smartphone-based gait assessment in assessing temporal gait parameters in level-ground walking. Twenty-six healthy young adults (mean age: 20.8 ± 0.7) were recruited. Participants walked at their comfortable pace on a 10 m pathway repetitively in two walking sessions. Gait data were simultaneously collected by the smartphone application and a VICON system during the walk. Gait events of heel strike and toes off were detected from the sensors signal by a peak detection algorithm. Further gait parameters were calculated and compared between the two systems. Pearson Product–Moment Correlation was used to evaluate the concurrent validity of both systems. Test–retest reliability was examined by the intraclass correlation coefficients (ICCs) between measurements from two sessions scheduled one to four weeks apart. The validity of smartphone-based gait assessment was moderate to excellent for parameters involving only heel strike detection (r = 0.628–0.977), poor to moderate for parameters involving detection of both heel strike and toes off (r = 0.098–0.704), and poor for the proportion of gait phases within a gait cycle. Reliability was good to fair for heel strike-related parameters (ICC = 0.845–0.388), good to moderate for heel strike and toes-off-related parameters (ICC = 0.827–0.582), and moderate to fair for proportional parameters. Validity was adversely affected when toe off was involved in the calculation, when there was an insufficient number of effective steps taken, or when calculating sub-phases with short duration. The use of smartphone-based gait assessment is recommended in calculating step time and stride time, and we suggest collecting no less than 100 steps per leg during clinical application for better validity and reliability. Full article

A novel polyhedron-based anionic Er-MOF with three types of cages and abundant open metal sites (OMSs) and Lewis base sites (LBSs) has been successfully synthesized. The inorganic secondary unit possesses a rarely reported six-connected three-nucleated rare-earth cluster, and the overall framework shows a new (3,3,6)-connected topology. The Er-MOF has good fluorescence selectivity and anti-interference performance with Fe³⁺ and Cu²⁺. In addition, benefiting from the anionic framework, nanoscale cavity and small window size of the Er-MOF, the composite RhB@Er-MOF has been synthesized by in situ encapsulation of the cationic dye Rhodamine B (RhB). It can provide dual-emitting fluorescence that facilitates self-calibration in sensing. The RhB@Er-MOF has higher accuracy than the Er-MOF with regard to the fluorescence-selective and anti-interference performance of Fe³⁺ and quenching coefficient K_sv values of 1.97 × 10⁴ M⁻¹, which are attributed to its self-calibration function that can eliminate environmental interference. The fluorescence quenching mechanism was explained by our experiments and density functional theory (DFT) calculations. Furthermore, RhB@Er-MOF can achieve the visual and rapid selective detection of Fe³⁺ by a smartphone RGB color analysis application, resulting in the dual-signal output performance of the material. Full article

Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need for accessible and convenient diagnostic tools for respiratory health assessment. While traditional lung sound auscultation has been the primary method for evaluating pulmonary function, emerging research highlights the diagnostic potential of nasal and oral breathing sounds. These sounds, shaped by the upper airway, serve as valuable non-invasive biomarkers for pulmonary health and disease detection. Recent advancements in artificial intelligence (AI) have significantly enhanced respiratory sound analysis by enabling automated feature extraction and pattern recognition from spectral and temporal characteristics or even raw acoustic signals. AI-driven models have demonstrated promising accuracy in detecting respiratory conditions, paving the way for real-time, smartphone-based respiratory monitoring. This review examines the potential of AI-enhanced respiratory sound analysis, discussing methodologies, available datasets, and future directions toward scalable and accessible diagnostic solutions. Full article

In alpine skiing, accurate and real-time estimation of body pose and inclinations due to turning is critical as it demonstrates the skier’s turning behavior and abilities. Although inertial measurement units (IMUs) ease measuring kinematics in extreme conditions and provide such indications of skiers’ behavior, they often suffer from sensor placement and orientation variability. This study explains the impact of sensor placement and orientation on the captured signals and proposes a preprocessing algorithm that can rotate raw signals from various locations and orientations similar to those near the Center of Mass (CoM). The preprocessing algorithm involves a sensor fusion approach using a quaternion-based complementary filter (CF) to rotate raw signals and extract turning motions via the global wavelet spectrum. Our experiment, validated on data collected from 14 sensors including two smartphones placed on different body parts during skiing sessions, demonstrates that the preprocessing algorithm can effectively reconstruct side motions, represent skiing turns, and detect turns independent of sensor placement and orientation. In field experiments with six skiers, the suggested preprocessing algorithm consistently detected skiing turns with an overall RMSE of 0.77 and MAE of 0.50 on all of the sensors relative to a reference sensor. Full article

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R² > 0.999), non-significant biases, and Bland–Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R² values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices. Full article

Rockets generate complex acoustic signatures that can be detected over a thousand kilometers from their source. While many far-field acoustic rocket signatures have been collected and released to the public, very few signatures collected at distances less than 100 km are available. This work presents a curated and annotated dataset of acoustic signatures of 243 rocket launches collected by a network of smartphones stationed at distances between 10 and 70 km from the launch sites, resulting in 1089 individual recordings. Due to the frequency dependence of atmospheric attenuation and the relatively short propagation distances, higher-frequency features not preserved in most publicly available data are observed. The signals are time-aligned to allow for different segments of the signal (ignition, launch, trajectory, chronology) to be more easily examined and compared. Initial analysis of the features of these rocket launch stages is performed, observed features are compared to those found in the existing literature, and comparisons between signals from launches of different rocket types are made. The dataset is annotated and made available to the public to aid future analysis of the characteristics and source mechanisms of rocket acoustics as well as applications such as rocket detection and classification models. Full article

A copper-based nanoenzyme (Cu-BL) co-modified by L-L-lysine and 2-2-amino terephthalic acid has laccase-like activity and fluorescence characteristics. Based on this, a colorimetric and fluorescent dual-mode sensor was developed to visually and quantitatively detect tetracycline antibiotics (TCs), including tetracycline (TC), chlortetracycline (CTC), and oxytetracycline (OTC). In the colorimetric detection system, TCs can inhibit the generation of singlet oxygen (¹O₂) and weaken the ability of 2,4-dichlorophenol (2,4-DP) to be oxidized into pink-colored quinone substances. The linear ranges are 0.5–80 μM, 1–80 μM, and 0.25–80 μM, and the detection limits are 0.27 μM, 0.22 μM, and 0.26μM, respectively. In addition, due to the inner filter effect, tetracycline antibiotics can interact with Cu-BL, and with the increase in tetracycline antibiotic concentration, the fluorescence intensity will decrease. In addition, the smartphone sensing platform is combined with the colorimetric signal for the rapid and visual quantitative detection of tetracycline antibiotics. Generally speaking, the colorimetric/fluorescence dual-mode sensor demonstrates good stability, high specificity, and strong anti-interference capabilities, highlighting its practical application potential. This work is expected to offer novel insights for the development of multifunctional nanoenzymes and the integration of a multi-mode sensing platform. Full article

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors. Full article

Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling ‘On’ and ‘Off’ states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications. Full article

In order to identify carcinoembryonic antigen (CEA) in serum samples, an innovative smartphone-based, label-free electrochemical immunosensor was created without the need for additional labels or markers. This technology presents a viable method for on-site cancer diagnostics. The novel smartphone-integrated, label-free immunosensing platform was constructed by nanostructured materials that utilize the layer-by-layer (LBL) assembly technique, allowing for meticulous control over the interface. Detection relies on direct interactions without extra tagging agents, where ordered graphene oxide (GO), carbon nanotubes (CNTs), and copper oxide nanoparticles (CuONPs) were sequentially deposited onto a screen-printed carbon electrode (SPCE), designated as CuONPs/CNTs/GO/SPCE. This significantly amplifies the electrochemical signal, allowing for the detection of low concentrations of target molecules of CEA. The LBL approach enables the precise construction of multi-layered structures on the sensor surface, enhancing their activity and optimizing the electrochemical performance for CEA detection. These nanostructured materials serve as efficient carriers to significantly increase the surface area, conductivity, and structural support for antibody loading, thus improving the sensitivity of detection. The detection of carcinoembryonic antigen (CEA) in this electrochemical immunosensing transducer is based on a decrease in the current response of the [Fe(CN)₆]^3−/4− redox probes, which occurs in proportion to the amount of the immunocomplex formed on the sensor surface. Under the optimized conditions, the immunosensor exhibited good detection of CEA with a linear range of 0.1–5.0 ng mL⁻¹ and a low detection limit of 0.08 ng mL⁻¹. This label-free detection approach, based on signal suppression due to immunocomplex formation, is highly sensitive and efficient for measuring CEA levels in serum samples, with higher recovery ranges of 101% to 112%, enabling early cancer diagnosis. The immunosensor was successfully applied to determine CEA in serum samples. This immunosensor has several advantages, including simple fabrication, portability, rapid analysis, high selectivity and sensitivity, and good reproducibility with long-term stability over 21 days. Therefore, it has the potential for point-of-care diagnosis of lung cancer. Full article