Signal Acquisition Research Articles

Advancing Wearable Cardiovascular Monitoring: Integration of Flexible Piezoresistive Sensors for Robust Pulse Waveform Detection

Wearable, flexible piezoresistive pressure sensors have diverse applications, spanning electronic skin, robotic limbs, and cardiovascular monitoring. Recent advancements in wearable health monitoring devices utilizing these sensors show promise for continuous cardiovascular monitoring. However, a substantial gap exists between laboratory development and market availability for pressure sensor based pulse monitoring. Challenges include the lack of effective testing schemes ensuring robust wearable operation and the necessity of bulky signal acquisition equipment, limiting adaptability. This study addresses these challenges by integrating the outputs of multiple flexible, skin-mountable piezoresistive sensors to create a robust wearable platform. This platform encompasses both sensing and miniaturized signal acquisition for arterial pulse waveform monitoring. The single piezoresistive sensor in our design exhibits sensitivity (1.4 kPa-1 at 2 – 8 kPa). This robust signal allows for the use of simple, compact signal collection devices mounted on a wristband. Moreover, the capability of the the system allows for detection of the three distinct peaks associated with the full pulse waveform, including the systolic, diastolic, and reflected diastolic peaks. In validation, a comparison between the wrist pulse waveform obtained with the compact signal collection device and a standard sourcemeter (Keithley 2460) confirms the capability of our wearable system to resolve the three distinct peaks. This work contributes to bridging the gap between research and practical applications, offering a potential solution for widespread adoption of continuous cardiovascular monitoring technology in diverse settings.

Fractional Diversity Entropy: A Vibration Signal Measure to Assist a Diffusion Model in the Fault Diagnosis of Automotive Machines

Real-world vibration signal acquisition of automotive machines often results in imbalanced sample sets due to restricted test conditions, adversely impacting fault diagnostic accuracy. To address this problem, we propose fractional diversity entropy (FrDivEn) and incorporate it into the classifier-guided diffusion model (CGDM) to synthesize high-quality samples. Additionally, we present a corresponding imbalanced fault diagnostic method. This method first converts vibration data to Gramian angular field (GAF) image samples through GAF transformation. Then, FrDivEn is mapped to the gradient scale of CGDM to trade off the diversity and fidelity of synthetic samples. These synthetic samples are mixed with real samples to obtain a balanced sample set, which is fed to the fine-tuned pretrained ConvNeXt for fault diagnosis. Various sample synthesizers and fault classifiers were combined to conduct imbalanced fault diagnosis experiments across bearing, gearbox, and rotor datasets. The results indicate that for the three datasets, the diagnostic accuracies of the proposed CGDM using FrDivEn at an imbalance ratio of 40:1 are 91.22%, 87.90%, and 98.89%, respectively, which are 7.32%, 11.59%, and 3.48% higher than that of the Wasserstein generative adversarial network (WGAN), respectively. The experimental results across the three datasets validated the validity and generalizability of the proposed diagnostic method.

A Complementary Dual-Mode Ion-Electron Conductive Hydrogel Enables Sustained Conductivity for Prolonged Electroencephalogram Recording.

Conductive gel interface materials are widely employed as reliable agents for electroencephalogram (EEG) recording. However, prolonged EEG recording poses challenges in maintaining stable and efficient capture due to inevitable evaporation in hydrogels, which restricts sustained high conductivity. This study introduces a novel ion-electron dual-mode conductive hydrogel synthesized through a cost-effective and streamlined process. By embedding graphite nanoparticles into ionic hyaluronic acid (HAGN), the hydrogel maintains higher conductivity for over 72h, outperforming commercial gels. Additionally, it exhibits superior low skin contact impedance, considerable electrochemical capability, and excellent tensile and adhesion performance in both dry and wet conditions. The biocompatibility of the HAGN hydrogel, verified through in vitro cell viability assays and in vivo skin irritation tests, underscores its suitability for prolonged skin contact without eliciting adverse reactions. Furthermore, in vivo EEG tests confirm the HAGN hydrogel's capability to provide high-fidelity signal acquisition across multiple EEG protocols. The HAGN hydrogel proves to be an effective interface for prolonged high-quality EEG recording, facilitating high-performance capture and classification of evoked potentials, thereby providing a reliable conductive medium for EEG-based systems.

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems.

Research on wireless monitoring system and algorithms for preload force utilizing machine learning and electromechanical impedance

The root mean square deviation is a common way to measure the electromechanical impedance of piezoceramic transducers that are used for traditional preload monitoring. However, due to the impedance signal’s high sensitivity to temperature, most current research is carried out under the same temperature conditions to avoid its effects. Even so, it is impossible to ignore the temperature factor in practical engineering, which hinders the application of impedance technology. Therefore, this paper proposes a novel approach for preload monitoring in actual engineering: using a wireless impedance signal acquisition platform to collect long-distance impedance signals and then establishing a predicted model based on machine learning (ML) considering the temperature. The wireless impedance signal acquisition platform includes a smart washer, a piezoelectric impedance wireless sensing device, and the host computer software. This test established a dataset under various operating conditions and developed a machine-learning-based predictive model. After comparing five typical ML algorithms, it was discovered that XGBoost performed better in prediction accuracy and generalization ability. Moreover, the Shapley additive explanation method is utilized to further analyze and interpret the XGBoost model. It indicates that ML primarily relies on numerical features (such as the area of each subinterval) to identify the impedance signal and predict the prestress, whereas information features (such as temperature value, peak, etc.) have little influence on the model’s output. Finally, the results above demonstrate that the ML-based models can predict the preload at different temperatures, effectively reducing temperature interference.

Fast ultrasonic ablation monitoring: An innovative approach using ultrasound RF signals and singular value decomposition

Microwave ablation (MWA) can rapidly lead to tumor cell necrosis by heating local tissues using electromagnetic field energy, making it a highly efficient modality widely utilized in clinical ablation surgeries. However, the lack of timely and accurate intraoperative monitoring methods is a critical issue that urgently needs to be addressed in MWA. This article introduces an innovative approach for real-time monitoring of ablation therapies by the combination of ultrasound radio frequency (RF) signal acquisition and singular value decomposition (SVD), which breaks through the limitations of current monitoring techniques during MWA procedures. Due to the difference in acoustic scattering properties between the gas generated in high-temperature ablated region and the non-ablated tissues, the current new method aimed to apply SVD analyses to ultrasound RF signals scattered from the tissues, through which the range of gas generation could be determined and then the ablation area could be estimated. Eventually, high-precision differentiation between ablated and non-ablated tissues could be achieved, enabling real-time adjustment and improvement of MWA strategies. Validated through ex vivo experiments on pig liver, this technique demonstrated a relative error in monitoring the size of ablation region of approximately 7%, indicating a strong correlation with actual ablation outcomes. The study indicated the proposed method should have great potential to improve the precision and safety of MWA therapy.

AISClean: AIS data-driven vessel trajectory reconstruction under uncertain conditions

In maritime transportation, intelligent vessel surveillance has become increasingly prevalent and widespread by collecting and analyzing high massive spatial data from automatic identification system (AIS). The state-of-the-art AIS devices contain various functionalities, such as position transmission, tracking navigation, etc. Widely equipped shipboard AIS devices provide a large amount of real-time and historical vessel trajectory data for maritime management. However, the original AIS data often suffers from unwanted noise (i.e., poorly tracked timestamped points for vessel trajectories) and missing (i.e., no data is received or transmitted for a long term) data during signal acquisition, transmission, and analog-to-digital conversion. This degradation in data quality poses significant risks, including potential miscalculations in vessel collision avoidance systems, inaccuracies in emission calculations, and challenges in port management. In this work, a data-driven vessel trajectory reconstruction framework considering historical features is proposed to enhance the reliability of vessel trajectory. Specifically, a series of statistical methods are proposed to identify noisy data and missing data. Then, a model combining Geohash and dynamic time warping algorithms is developed to restore the trajectories degraded by random noise and missing data in vessel trajectories. Comparative experiments with baseline methods on multiple datasets verify the effectiveness of the proposed data-driven model.

Wearable gait recognition system based on time domain features of Bluetooth inertial sensor network

Abstract As an important part of medical monitoring, gait recognition has been used in lower limb rehabilitation diagnosis and gait assessment. To meet practical requirements, we designed a wearable gait recognition system based on time domain features of Bluetooth inertial sensor network, which can be used for real-time acquisition and evaluation of gait signals. In this research, we used 6 sensor units to integrate 10 kinds of gait information, and designed a gait monitoring system covering the main joints of lower limbs based on Bluetooth network. The gait monitoring system can collect the inertial sensing data of lower limbs at a maximum rate of 90 Hz through the 6 sensor units, and has good electrical performance. In daily use, each unit can work for more than 3 hours and maintain a received signal strength indicator (RSSI) of more than -90 dBm at a spatial distance of 10 meters. On the gait dataset collected in the experiment, we used sliding window to segment the time domain signals and extract 10 kinds of time domain features for classification algorithm. We achieved 97.1% average identification accuracy of 7 different gait patterns by support vector machine (SVM).

An 112-Ch Neural Signal Acquisition SoC With Full-Channel Read-Out and Processing Accelerators

An 112-Ch Neural Signal Acquisition SoC With Full-Channel Read-Out and Processing Accelerators

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Design of diesel engine vibration signal acquisition system based on Zynq

Design of diesel engine vibration signal acquisition system based on Zynq

Extraction of the Jugular Venous Pulse and carotid profile using a cervical contact plethysmography system

The Jugular Venous Pulse (JVP) is considered a reliable parameter for the assessment of Central Venous Pressure (CVP). Here, the functionality of a cervical contact plethysmography system designed for non-invasive and operator-independent acquisition of the JVP signal, is shown. To validate the signal, it was recorded in supine and sitting positions, together with the reference Electrocardiography (ECG), on 26 healthy subjects. In the supine acquired signal, the characteristic JVP waves (a, c, v) and the negative deflections (x, y) are well recognizable. In the sitting recorded signal, the systolic peak b and the d incisura of the Common Carotid Artery (CCA) waveform are recognized. For each signal, we calculated the Fraction of the Cardiac Cycle (ccf) represented by the time intervals between the JVP peaks and the ECG peaks, in the form: ΔtaP, ΔtcR, ΔtxP, ΔtvT, Δtyv, Δtvx, and Δtxa. The same was done for the CCA waveform, in the form: ΔtbS, ΔtbT, Δtdb, ΔtdS, and ΔtdT. This system could mitigate risks and costs associated with central venous catheterization and its potential extends to applications in telemedicine, sports medicine, and space medicine.

Three-dimensional MXene/carbon nanotube composite electrodes in flexible 64-channel arrays for noninvasive electromyography signal acquisition

Three-dimensional MXene/carbon nanotube composite electrodes in flexible 64-channel arrays for noninvasive electromyography signal acquisition

Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques

Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques

Mica/nylon composite nanofiber film based wearable triboelectric sensor for object recognition

The wearable sensors have essential impacts on the progress of intelligent technology, while the traditional sensors cannot meet the sensitivity requirements. Here, we propose a high performance intelligent triboelectric wearable sensor (HITWS) by Mica/Nylon composite nanofiber film (MCNF), which is fabricated by the electrospinning and dielectric particle doping process. The addition of Mica particles effectively improves the dielectric constant, surface charge density and functional group properties of MCNF and further optimizes its positive triboelectric properties. Therefore, the HITWS demonstrates significantly higher signal-to-noise ratio, sensitivity and power density (11.82 W/m2) compared to previous similar studies. Furthermore, based on HITWS, 4-channel STM32 signal acquisition and deep learning algorithm, an intelligent tactile sensing system is achieved. Combined with the structurally optimized VGG16 network model, the recognition of 7 kinds of objects can reach an average accuracy of 96.57 %. This work provides new idea and method for the development of applications based on object recognition, such as remote interaction, medical prosthetics and smart robots.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Exploring the viability of combined laser-induced breakdown spectroscopy and Raman spectroscopy for stratigraphic analysis of murals containing isomeric pigments: a case study on realgar and orpiment

A novel combined measurements techniques has been designed in this work, enabling the acquisition of laser-induced breakdown spectroscopy (LIBS) and Raman spectral signals at the same point on a sample. The application of this combined technique to the analysis of multi-layered mock-up blocks painted with orpiment and realgar pigments has yielded significant insights. By correlating variations in the emission line intensity of characteristic elements within the LIBS spectra with depth-specific Raman spectra, the number of laser pulses that penetrated the pigment layers has been accurately determined, thereby establishing a method to measure layer thickness. Finally, the technique wasto analysis the actual mural fragment from Mogao Cave 196, determining the types of pigment and the thickness of the pigment layers.Graphical

Self-powered system for real-time wireless monitoring and early warning of UAV motor vibration based on triboelectric nanogenerator

Unmanned aerial vehicle (UAV) is widely used in various industries due to their high flexibility and large maneuverable space. Abnormal vibration of UAV motors can prevent it from working properly due to the abnormal state of the motors and the harsh external environment. There is no suitable method to monitor the UAV motors vibration in real time. At the same time, commercial sensors require an additional power supply to power the sensor, which indirectly limited their application in UAVs. A vibration sensor (AV-TENG) with magnetic spring structure was proposed in this paper for monitoring the vibration of UAV motors. The experimental results show that the magnetic spring has excellent stability. AV-TENG can achieve frequency detection in a wide frequency band. It also has excellent linearity with a maximum error of only 0.0062 %. Simultaneously, to verify the performance of AV-TENG in various environments and simulate its loading, the results demonstrate that AV-TENG can adapt well to the working conditions of UAV. Finally, this paper constructs a low-cost (less than $7), easy-to-manufacture signal acquisition system, and conducts a field demonstration, realizing the monitoring of the vibration frequency and acceleration of the UAV motors, and realizing the early warning, which is expected to promote the development of the field of UAV as well as the practical application of the triboelectric nanogenerator.

Acquisition and Analysis of Facial Electromyographic Signals for Emotion Recognition.

The objective of the article is to recognize users' emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions.

Practical deployment of subsampling-based high-frequency signal acquisition system

Practical deployment of subsampling-based high-frequency signal acquisition system