Impedance Signal Research Articles

AuPt/NF prepared with the lattice induction of substrate was applied to construct the electrochemical immunosensor for PCT detection

For the electrodeposition, the conductivity and lattice structure of substrate is important to the morphology and lattice of the deposited material. In this study, gold-platinum (AuPt) nanopartical was deposited on nickel foam (NF) based on the lattice induced orientation of the Ni substrate, and the obtained AuPt/NF was applied to construct electrochemical impedimetric immunosensor for procalcitonin (PCT) detection. As a new immunosensor matrix, NF with higher electrical conductance, flexibility and specific surface area, which can improve the plasticity, sensitivity and universality of the immunoelectrode. Due to the lattice matching between Au and Ni, ultrathin AuPt nanolayer with good biocompatibility and large surface area can be modified on the NF surface, which can bind more biomolecules and amplifies the change of impedance signal. Based on the synergistic effect between AuPt and NF, PCT detection based on this electrochemical impedimetric immunosensor with a wide linear range (0.2 pg mL−1 to 20 ng mL−1) and low detection limit (0.11 pg mL−1). In addition, this impedimetric immunosensor exhibits high recovery in the PCT detection of serum samples. This work provides a new thought and method for the construction of electrochemical immunosensor.

Detection of AFB1 in corn by MXene paper‐based unlabeled aptasensor

AbstractIn this work, a paper‐based electrochemical aptamer sensor was developed for the detection of aflatoxin B1 (AFB1) using a combination of MXene–Ti3C2Tx and nucleic acid aptamers. The prepared single‐layer or few‐layer MXene suspension is suction‐filtered onto MXene paper, which is cut to prepare MXene electrodes. To accomplish AFB1 specific detection, an amino‐labeled AFB1 aptamer is mounted on the surface of the carboxy‐functionalized MXene electrode. When AFB1 is present, it particularly binds to the aptamer to form a 3D structure, reducing the efficiency of electron transmission on the sensor surface. The difference in impedance signal change at the electrode/electrolyte interface is used to quantify AFB1. The results indicated that the detection range is 0.05–100 ng/mL, the detection limit is 0.04 ng/mL, and the recovery rate of AFB1 in corn samples is 97.8%–111.52% with the optimal detection conditions. The MXene paper‐based label‐free aptasensor is versatile and can detect different targets by simply swapping out the aptamers of different targets. The sensor also has a wide range of applications in food analysis and environmental testing.Practical applicationsA paper‐based electrochemical aptamer sensor was developed to detect aflatoxin B1 using a combination of MXene–Ti3C2Tx and nucleic acid aptamers.The design is based on the preparation of MXene electrodes by pumping and filtering monolayer or multilayer MXene suspensions onto MXene paper and cutting.The MXene paper‐based label‐free aptamer sensor was designed to be versatile, allowing the detection of different targets by simply replacing the aptamer with one from a different targets.

A novel method for chronic obstructive pulmonary disease detection using comprehensive respiratory impedance and electrocardiogram-derived respiration

Currently, pulmonary function test is the only standard method for diagnosing COPD, but it often involves high testing costs, poor compliance, and a high dependence on patient cooperation. Other new detection methods, such as CT and FOT, also have disadvantages such as radiation exposure and high usage costs. Existing non-invasive COPD detection methods consider the impact of COPD on the lungs or heart, thus they can only roughly detect COPD in individuals, without the ability to grade COPD. Therefore, this study proposed a method for COPD detection using comprehensive respiratory impedance (CRI) and electrocardio-derived respiration (EDR) signals. The method measured both respiratory impedance and ECG synchronously, and the CRI and EDR signals were obtained through an algorithm. The effects of COPD on respiratory pattern were studied by taking CRI and EDR signal morphology as objects, from which characteristic parameters were extracted and input into the supervised classifier to fuse the information of COPD on the changes of respiratory signal morphology pattern in the heart and lungs. The final result is accurate detection of normal, mild COPD and moderate COPD. The experimental results of 46 subjects showed that the accuracy, sensitivity and specificity of CRI and EDR signal characteristics for COPD detection by different classifiers were up to 93.5 %, 90.5 % and 96.5 %, and the AUC values of different classifiers were all > 0.88. Therefore, the method can not only effectively distinguish COPD patients from healthy people, but also provide differentiated detection capabilities for different COPD clinical grades, providing strong support for the early diagnosis and treatment of COPD, and can be applied to the development of portable medical devices for the detection of COPD.

A Novel Microfluidics Droplet-Based Interdigitated Ring-Shaped Electrode Sensor for Lab-on-a-Chip Applications.

This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic on-chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing simultaneously. The microsensor, designed with interdigitated ring-shaped electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplet length in the range of 1.0-6.0 mm, velocity of 0.66-2.51 mm/s, and volume of 1.07 nL-113.46 nL. Experimental results demonstrated that the microsensor's performance is great in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip. This droplet-based microfluidics platform has great potential to be directly employed to promote advances in biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics.

Conformal, stretchable, breathable, wireless epidermal surface electromyography sensor system for hand gesture recognition and rehabilitation of stroke hand function

Surface electromyography (sEMG) plays a significant role in the everyday practice of clinic hand function rehabilitation. The materials and design of current typical clinic sEMG electrodes are rigid Ag/AgCl or flexible polyimide (PI) film, which cannot provide a stable interface between electrodes and skin for adequate long-term high-quality data. Thus, conformal, soft, breathable, wireless epidermal sEMG sensor systems have broad potential relevance to clinic rehabilitation settings. Herein, we demonstrate a stretchable epidermal sEMG sensor array system with optimized materials and structure strategies for hand gesture recognition and hand function rehabilitation. The optimized serpentine structures with marvelous stretchability and improved fill ratio, provide lower impedance and high-quality sEMG signals. Moreover, the easy-to-use airhole method further ensures stable and long-term contact with the skin for recording. In addition, integrated with a customized flexible wireless data acquisition system, the capability for real-time 8-channel sEMG monitoring is developed, and taking together with the CNN-based algorithm, the system can automatically and reliably realize the 7 kinds of hand gestures with an accuracy of 81.02%. Moreover, the low-cost yet high-performance epidermal sEMG sensor system demonstrated its conceptual feasibility in quantitatively evaluation of stroke patient’s hand and facilitating human-robot collaboration in hand rehabilitation by proof-of-the-concept clinical testing.

Ventilation during cardiopulmonary resuscitation with mechanical chest compressions: How often are two insufflations being given during the 3-second ventilation pauses?

BackgroundMechanical chest compression devices in 30:2 mode provide 3-second pauses to allow for two insufflations. We aimed to determine how often two insufflations are provided in these ventilation pauses, in order to assess if prehospital providers are able to ventilate out-of-hospital cardiac arrest (OHCA) patients successfully during mechanical chest compressions. MethodsData from OHCA cases of the regional ambulance service of Utrecht, The Netherlands, were prospectively collected in the UTrecht studygroup for OPtimal registry of cardIAc arrest database (UTOPIA). Compression pauses and insufflations were visualized on thoracic impedance and waveform capnography signals recorded by manual defibrillators. Ventilation pauses were analyzed for number of insufflations, duration of the subintervals of the ventilation cycles, and ratio of successfully providing two insufflations over the course of the resuscitation. Generalized linear mixed effects models were used to accurately estimate proportions and means. ResultsIn 250 cases, 8473 ventilation pauses were identified, of which 4305 (51%) included two insufflations. When corrected for non-independence of the data across repeated measures within the same subjects with a mixed effects analysis, two insufflations were successfully provided in 45% of ventilation pauses (95% CI: 40–50%). In 19% (95% CI: 16–22%) none were given. ConclusionProviding two insufflations during pauses in mechanical chest compressions is mostly unsuccessful. We recommend developing strategies to improve giving insufflations when using mechanical chest compression devices. Increasing the pause duration might help to improve insufflation success.

Saccharomyces Cerevisiae as a Model Organism for Retrospective Impedance Biodosimetry.

Previous studies have shown that measuring changes in electrical impedance that follow radiation-induced suppression of metabolic activity in irradiated yeast cells can be used to determine radiation dose. The current work investigates the radiation response of Saccharomyces cerevisiae cells by using metabolic activity of cells as a damage indicator. Impedance biodosimetry was examined as a method to evaluate the radiation response of yeast cells. Active lab-grade dry yeast cells were used as the biological material as these samples are simple to handle and have a long shelf-life. A novel dosimeter design has been developed with a strict fabrication method and measurement procedure to ensure reproducible measurements are possible. Prepared yeast samples were irradiated to doses from 0.5 to 8 Gy using a 137Cs source, and a dose response curve was developed that showed a linear relationship of dose with changes in impedance measurements. Fading of the impedance signal was also investigated, and it was shown that there was no noticeable fading of the impedance signal over a period of 7 mo. Finally, the lowest detectable limit measured using this methodology was determined to be 300 mGy. This work presents an alternative retrospective dosimetry technique that can be used at a high scale and low cost following large-scale radiological accidents.

Constructing core-shell biomass carbon@Fe3O4 composites for enhanced mid-to-low frequency electromagnetic wave absorption

Presently, the escalating issue of electromagnetic pollution, propelled by the rapid advancement in electronic information technology, is attracting heightened attention. This problem is particularly pronounced within the 2–10 GHz frequency range, where electronic devices make a substantial contribution to electromagnetic pollution. Research suggests that carbon-based magnetic composites are a viable solution for mitigating this pollution. In our study, we synthesized magnetic composites composed of Fe3O4 particles and biomass carbon (C) derived from corn silk, using a hydrothermal method. The low conductivity of Fe3O4 particles plays a key role in hindering microcurrent transmission, thus reducing the excessive dielectric properties of the carbon material. Additionally, the formation of multiple heterogeneous interfaces between Fe3O4 particles and biomass carbon significantly enhances interfacial polarization. Importantly, adjusting the Fe3O4 particle content in these composites enables modulation of their dielectric and magnetic properties, thus improving impedance matching and signal attenuation. Notably, at a C to Fe3O4 mass ratio of 1:1, the C@Fe3O4 composites exhibit outstanding wave-absorbing efficiency, characterized by an effective bandwidth of 3.3 GHz at a thickness of 2.13 mm. Most significantly, they achieve a reflection loss of -57.86 dB at 7.36 GHz, representing a considerable breakthrough in high-efficiency microwave absorption.

Local environment in yeast-based impedance biodosimeters strongly influences the measurable dose

This work investigates the feasibility of yeast-based impedance measurements for retrospective dosimetry applications. The local environment around yeast cells in a previously developed film-badge was modeled using Geant4. A greater dose response was observed when yeast cells were surrounded by an aluminum-polymer structure, which acted as a conversion layer. Bench-top experiments were conducted using a jar-based dosimeter design that directly combined a finely-ground aluminum conversion medium with yeast powder. It was shown when irradiated in the presence of aluminum grains, yeast cells yielded a higher impedance signal, thereby indicating greater radiation-induced damage. Finally, in separate irradiation experiments, lead and aluminum sheets were placed behind yeast samples and the dosimeters were irradiated to 1 Gy. A 2-fold increase in the impedance signal was shown when samples were positioned in close contact with the lead sheet compared to the aluminum sheet. In all experiments, it was shown that the local environment significantly influences radiative energy deposition in yeast cells.

ENERGY TECHNOLOGY OF DAIRY PRODUCTS QUALITY ASSESSMENT USING IMPEDANCEMETRY

The article examines the peculiarities of milk quality research using the impedance method. Impedance is a complex parameter that characterises the interaction of the electric current with milk. The authors have developed a methodology based on the specified method to assess the quality of dairy products and have created a hardware and software complex that makes it possible to analyse the quality of dairy products. The research, conducted using the developed hardware and software complex, revealed several regularities characterising different types of milk. These regularities demonstrate the relationship between the impedance signal and the milk fat index. Analysis of the obtained data allowed us to identify differences in impedance between samples with varying fat content and origin from different manufacturers. The developed experimental setup prototype enabled the following research stages: checking the change in voltage during the passage of an alternating electric current through milk. This work used the AFS Sensor DAQ data acquisition system, designed for sensor control projects and which runs with NI LabVIEW software, for data output. The authors tested the installation while investigating the impedance of dairy products with different fat content and from multiple manufacturers. During the experiment, we measured the impedance and capacitance of the samples, and the obtained data became available on a PC for further analysis. The aim was to study the electrical properties of dairy products and their dependence on the composition and manufacturer. The obtained results represent valuable information for dairy product production and quality control. It is possible to use them to optimise production processes, ensure stable product quality, and improve consumer characteristics of dairy products. The obtained data contribute to a deeper understanding of the relationship between the impedance signal and the composition of dairy products, which can lead to the development of new methods of analysis and evaluation of the quality of food products. In addition, these studies have implications for food science and nutrition. Keywords: dairy products, milk quality, impedancemetry, electrical measurements, hardware and software complex.

A computer-based examination of the quality of resuscitation measures based on device data

Sudden cardiac arrest represents a critical medical emergency where the quality of resuscitation measures is crucial for favorable outcomes[1,2]. The quality of cardiopulmonary resuscitation (CPR) can be assessed through the analysis of data recorded by the defibrillator during CPR. The data is routinely recorded at the base of the emergency medical vehicles at the University Hospital of Graz.
 To assess CPR quality, algorithms were implemented to identify chest compressions based on the chest impedance and ventilations using the capnography signals. With these algorithms, parameters for CPR quality can be determined. A dataset with 522 cases was used, of which 97 cases were automatically discarded as they contained no or no relevant impedance signals.
 The mean chest compression rate of the cases is 116.3 ± 9.1 compressions per minute, which is within the target range of 90 – 120. However, in 31% of cases, the rate was found to be too high. The proportion of minutes without an adequate mean chest compression rate was 29.2 ([6.2; 60.4]) %.
 The mean ventilation frequency of all cases with capnography signal (150 cases) was 13.5 ± 3.6 ventilations per minute and, therefore, within the target range of a maximum of 15 ventilations. However, in 34% of cases, the ventilation frequency was too high.
 This work shows that a systematic and automated evaluation of defibrillator recordings is suitable for assessing the quality of resuscitation measures. The determined parameters produced plausible results and are consistent with the results of preliminary studies at the Medical University of Graz, which were carried out manually on significantly smaller datasets.

Accuracy of Automatic Chest Compression Detection of Different Manufacturers

Research question The detection of single chest compressions (CCs) in defibrillator records is crucial to evaluate CPR quality parameters like longest pause duration.1 Currently, defibrillators detect CCs automatically and provide performance feedback via their proprietary software. While some manufacturers (e.g. Stryker) report the accuracy of their CC detection algorithm and allow to manually revise the automatically detected CCs to improve accuracy, others (e.g. ZOLL) do not offer this option.2 Recent works further suggest that using automatically detected CCs without revision or other open source methods is sufficient to compute quality markers.3,4 We aim to compare the accuracy of the automatic CC detection of two defibrillators. Methodology 131 defibrillator recordings from ZOLL’s X-Series devices with an applied feedback sensor and 70 recordings of Stryker’s LIFEPAK 15 devices were exported. ZOLL detects CC based on accelerometry data from its CC feedback sensor, while Stryker uses the thoracic impedance signal. Each set of recordings was annotated by a single annotator by adding missing CCs and deleting excess CCs, forming a ground truth. The results are reported as median, (10th percentile, 90th percentile) and were tested on statistical significance with a Mann-Whitney U test. Results Per case, the device by ZOLL detects in median 99.6, (97.8,99.9) % of all CCs correctly. 0.4, (0.1,2.3) % are deleted and 0.4, (0.1,2.3) % are added during the annotation process. For Stryker’s LIFEPAK 15 the respective numbers are: correctly detected: 96.7, (81.5,99.2) %, deleted: 1.8, (0.2,10.9) %, added: 3.3, (.8,18.5) %. The difference between the correctly identified CCs is significant (p<0.0001). The distribution of missing and excess CCs for all cases is shown in Figure 1. Interpretation It appears that ZOLL’s CC detection via an accelerometry based feedback sensor is more accurate than Stryker’s method using thoracic impedance. However, Stryker’s accuracy exceeds 95% as well, providing a reasonably reliable basis for CPR quality marker calculations.

Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state.

This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.

A temperature-decoupled impedance-based mass sensing using CBAM-CNN and adaptive weighted average preprocessing with high accuracy

Microcantilevers are widely adopted in various fields for micro-mass sensing due to their simple structure and high sensitivity. The conventional microcantilever-based measurement methods required ultra-precision temperature control. However, such a requirement could hardly be satisfied and the temperature shifting would deteriorate the accuracy of mass sensing. In this research, a new temperature decoupling method for mass sensing is proposed by taking advantage of the relative relationship of the peaks in the impedance signals. Besides, a CBAM-CNN network with an adaptive weighted average preprocessing method is developed for the evaluation of the relative relationship of the peaks. This method can enhance the features of the impedance signal and highlight critical information with limited samples. Experiments showed that robust mass sensing with accuracy up to 99.70% can be achieved over a wide temperature range from 25 to 55 ℃. In addition, the performance of the proposed network was compared to SVM, DNN, and CNN. This method offers a new mechanism for eliminating the influences of temperatures for precision cantilever-based mass sensing.

Determining mammalian cells state by fractal micromotion

We applied four fractal dimension estimation algorithms on the temporal electrical impedance signal of normal MDCK type II cell cultures monitored by ECIS technique and showed that the fractal dimension due to micromotion allows discriminating processes not sensed by the spectral impedance of the culture. In this work we subjected cell cultures to electric current damage and drug exposure to analyze the changes in the fractal structure of the temporal signal. Among the changes presented and detected are the differentiation between a healthy monolayer and one exposed to a drug, as well as the distinction between a seeding process and a wound-healing process performed by electric current. The four algorithms used were validated by applying them on topological functions of known fractal dimension, a study that determined the necessary conditions for a correct estimation.

Monitoring of crack repair in concrete using spherical smart aggregates based on electromechanical impedance (EMI) technique

Cracks will inevitably occur in concrete structures or members during the construction process and service life due to aging, environmental factors, external loads, etc. To improve the strength and stability of the cracked concrete structures, many methods have been proposed to repair the cracks. However, the monitoring of the repairing process and repair quality has not been fully studied. The previous studies have proved that the spherical smart aggregates (SSAs) based on the electromechanical impedance (EMI) technique have outperformed the traditional smart aggregates (SAs) based on the EMI technique in structural health monitoring of civil structures, however, SSAs have not been applied to the monitoring of the concrete crack repair. In this work, the monitoring of the concrete crack repair using the SSAs based on the EMI technique was explored. A total of eight concrete specimens were prepared, and cracks in the concrete specimens were simulated by manually cutting under laboratory conditions. According to the principle of grouting method, two repair agents including cement paste and cement mortar were used to repair the cracks. The impedance signals of 28 d were measured, and three quantitative indicators, namely root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation were used to evaluate the quality of the concrete repair effect. The results indicate that the SSAs show excellent sensitivity and stability over the traditional SAs. In addition, the normalized values of the quantitative indicators were analyzed to distinguish the types of repair agents. A mathematical expression of exponential function was also proposed by fitting the experimental data to quantitatively evaluate and predict the repair effect of concrete cracks. Further, the influences of temperature, humidity, crack depth and width on the experimental results were analyzed and discussed. The numerical simulation was also presented to validate the reasonability of the experimental results.

A proof of concept study on reliability assessment of different metal foil length based piezoelectric sensor for electromechanical impedance techniques

Lead zirconate titanate (PZT) patches gained popularity in structural health monitoring (SHM) for its sensing and cost effective. However, a robust installation of PZT patches is challenging due to the often-complex geometry and non-accessibility of structural parts. For tubular structures, the curved surface can compromise the perfect bonding of PZT patches. To alleviate the above-mentioned challenges, the non-bonded and reusable configuration of sensor received considerable interest in the field of SHM. However, ensuring the repeatability and reproducibility of Electro-Mechanical Impedance (EMI) measurements is crucial to establish the reliability of these techniques. This work investigated the repeatability and reproducibility measures for one of non-bonded configuration of PZT patch i.e., Metal Foil Based Piezo Sensor (MFBPS). In addition, the concept, application, and suitability of MFBPS for impedance-based monitoring technique of Civil infrastructure are critically discussed. This study evaluates the effect of length of MFBPS on piezo coupled admittance signature. Also, this study evaluates repeatability and reproducibility of EMI measurements via statistical tools such as ANOVA and Gage R&R analysis. The statistical index CCDM was used to quantify the deviations of impedance signals. The overall result shows that the repeatability of the EMI measurements improves with a metal foil length of 500 mm. Overall, this investigation offers a useful point of reference for professionals and scholars to ensure the reliability of MFBPS for EMI techniques, a variant of piezoelectric sensor for SHM applications.

Automatic muscle impedance and nerve analyzer (AMINA) as a novel approach for classifying bioimpedance signals in intraoperative pelvic neuromonitoring

Frequent complications arising from low anterior resections include urinary and fecal incontinence, as well as sexual disorders, which are commonly associated with damage to the pelvic autonomic nerves during surgery. To assist the surgeon in preserving pelvic autonomic nerves, a novel approach for intraoperative pelvic neuromonitoring was investigated that is based on impedance measurements of the innervated organs. The objective of this work was to develop an algorithm called AMINA to classify the bioimpedance signals, with the goal of facilitating signal interpretation for the surgeon. Thirty patients included in a clinical investigation underwent nerve-preserving robotic rectal surgery using intraoperative pelvic neuromonitoring. Contraction of the urinary bladder and/or rectum, triggered by direct stimulation of the innervating nerves, resulted in a change in tissue impedance signal, allowing the nerves to be identified and preserved. Impedance signal characteristics in the time domain and the time–frequency domain were calculated and classified to develop the AMINA. Stimulation-induced positive impedance changes were statistically significantly different from negative stimulation responses by the percent amplitude of impedance change Amax in the time domain. Positive impedance changes and artifacts were distinguished by classifying wavelet scales resulting from peak detection in the continuous wavelet transform scalogram, which allowed implementation of a decision tree underlying the AMINA. The sensitivity of the software-based signal evaluation by the AMINA was 96.3%, whereas its specificity was 91.2%. This approach streamlines and automates the interpretation of impedance signals during intraoperative pelvic neuromonitoring.

A new immunosensing platform based on conjugated Poly(ThidEp-co-EDOT) copolymer for resistin detection, a new obesity biomarker.

Thedesign of a novel electrochemical impedimetric biosensor for label-free analysis of resistin, a biomarker for obesity, is reported. For the fabrication of the immunosensor, a novel approach composed of electrochemical copolymerization of double epoxy groups-substituted thiophene (ThidEp) and 3,4-Ethylenedioxythiophene (EDOT) monomers was utilized. Anti-resistin antibodies were covalently attached to the copolymer-coated electrode. The capture of resistin antigens by anti-resistin antibodies caused significant variations in charge transfer resistance (Rct) because of the immunoreactions between these proteins. Under optimum experimental variables, the changes in impedance signals were employed for the determination of resistin antigen concentration, and the prepared immunosensor based on conjugated copolymer illustrated a wide linear range between 0.0125 and 22.5 pg/mL, a low detection limit (LOD) of 3.71 fg/mL, and a good sensitivity of 1.22 kΩ pg-1mL cm2. The excellent analytical performance of the resistin immunosensor in terms of selectivity, sensitivity, repeatability, reproducibility, storage stability, and low detection limit might be attributed to the conductive copolymer film layer generation on the disposable indium tin oxide (ITO) platform. The capability of this system for the determination of resistin in human serum and saliva samples was also tested. The immunosensor results were in accordance with the enzyme-linked immunosorbent assay (ELISA) results. The matrix effects of human serum and saliva were also investigated, and the proposed immunosensor displayed good recovery ranging from 95.91 to 106.25%. Theengineered immunosensor could open new avenues for obesity monitoring.

Machine learning-enhanced gesture recognition through impedance signal analysis.

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.