Signal Monitoring Research Articles

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

A high-adhesive and sensitive hydrogel with interpenetrating and interlocking networked structure for efficient biological signal monitoring

Smart flexible sensing materials and devices for wearable health monitoring are considered as prospective and promising areas in healthcare that are currently in high demand in the market. Conductive hydrogel-based sensors, which closely resemble human tissue, encounter notable challenges in attaining high electronic conduction, robust mechanical properties, biocompatibility, and intimate tissue adhesion simultaneously. In this work, a poly(2-acrylamide-2-methylpropane sulfonic acid-co-acrylamide)/bacterial cellulose (P(AMPS-co-AM)/BC) hydrogel with a particular interpenetrating and interlocking networked structure is reported. This hydrogel possesses all the mentioned intriguing properties plus easy degradability. It is realized by utilizing the interaction between amino, amide, and sulfonic groups on polymer molecular chains and polyhydroxy groups on bacterial cellulose. This hydrogel exhibits high strength (1.28 MPa) and tensile strain (525 %), remarkable tissue adhesion (697 kPa), exceptional antibacterial property and low cytotoxicity. Furthermore, it can degrade in the natural environment when exposed to UV light irradiation. The hydrogel-based strain sensors have demonstrated efficient human motion detection with a broad strain detection range (0–360 %) and a high sensitivity (GF = 11.36). Moreover, this hydrogel can be assembled into Triboelectric Nanogenerator for monitoring biological signals. This work presents a new avenue for human-machine interaction and electronic robotic skins.

Auxiliary management and control channel aided crosstalk online monitoring in multi-core fibers

As a stochastic perturbation, the inter-core crosstalk (IC-XT) severely distorts the signal in multi-core fibers (MCF), especially for long-haul transmission. How to quickly measure and monitor the IC-XT online for an MCF-based space division multiplexing (SDM) system is of special importance. In this paper, we introduce the technology of auxiliary management and control channel (AMCC) to online monitor the IC-XT of MCF, in which the unique advantage of low-frequency auxiliary management and control signal is fully utilized with the limited influence on high-speed data transmission. Specifically, two orthogonal sequences are chosen as monitoring signals for the signal-channel core and the crosstalk-channel core, respectively, followed by digital signal processing (DSP) for the received signal to evaluate the real-time crosstalk accurately. The experimental verifications of XT online monitoring confirm the effectiveness of our proposed method with very small monitoring error (mostly < 0.5 dB) for both heterodyne XT and homodyne XT in the C and L bands, showing its great potential for future SDM systems.

The interaction system of the virtual reality helmet and the neurointerface

Problem statement. Assessing the quality of physiotherapeutic stimulation of virtual reality is complicated by the use of a classical device for recording brain parameters, such as an encephalograph, due to its significant overall dimensions. The work presents a system that includes a mobile device for recording brain parameters, a virtual reality helmet and wireless data transfer interfaces. Work purpose. Assessing the quality of physiotherapeutic stimulation of virtual reality is complicated by the use of a classical device for recording brain parameters, such as an encephalograph, due to its significant overall dimensions. The work presents a system that includes a mobile device for recording brain parameters, a virtual reality helmet and wireless data transfer interfaces. Results. An algorithm has been developed for integrating a neural interface with virtual reality environments to ensure mobility, precise synchronization of events in the virtual world and corresponding brain reactions to create an objective picture of a person’s health status as a result of external influences. Practical significance. A software implementation of a system for interaction between a virtual reality helmet and a neural interface is presented, aimed at increasing the mobility of recording, monitoring, recording and processing of brain signals in conditions of user immersion in virtual space.

Application coherent processing of two frequency-spaced radar signals in target monitoring under conditions of relayed interference

The use of ground-based radar stations for space surveillance makes it possible to solve problem of detecting new and tracing previously known targets around the clock. The quality of operation ground-based radar station for space surveillance can be significantly reduced when solving problems of radar surveillance in complex phono-target and signal-interference environment. To improve effectiveness of radar surveillance it is advisable to carry out joint use of radar station for space surveillance, in particular as part of multi-position complex with possibility of joint coherent radar signal processing, including signals separated by frequency. It is necessary to develop models for evaluating effectiveness joint use of deferent-band radar stations for space surveillance when solving problems of radar surveillance in complex phono-target and signal-interference environment based on implementation of coherent processing two radar signals. The purpose of study is increase potential accuracy of radar measurements target trajectory parameters in conditions of relayed interference based on implementation of coherent processing two radar signals at ground radar systems for space surveillance. A model of target monitoring has been developed in system of two different-range radar stations for surveying space according to probabilistic indicator and dependences on linkage target trajectories were obtained based on implementation of coherent processing two radar signals in various conditions of signal-interference environment. The result of study can be used in evaluating effectiveness joint use of several radar stations for surveying space as part of multi-position radar systems when observing targets in conditions of noise and relayed interference.

A Novel Method for Recognizing Physical Fitness Signals of Basketball Players Based on Photovoltaic Power Supply

This study proposes a novel method for recognizing the physical fitness signals of basketball players using a photovoltaic power supply. The traditional method for measuring physical fitness signals, such as heart rate and oxygen consumption, requires constant monitoring via wired sensors. This method is not suitable for sports like basketball, where player movement can interfere with the sensors and affect the accuracy of the measurements. To address this issue, we developed a small, lightweight device that utilizes a photovoltaic power supply to eliminate the need for wires and allow for seamless monitoring of physical fitness signals. The device can be attached to a player's clothing or equipment, and the energy generated from the photovoltaic power supply is used to power the device and collect data. We conducted a pilot study with professional basketball players and found that our method provided accurate and reliable measurements of physical fitness signals. It opens up new possibilities for sports performance monitoring, as coaches and trainers can now quickly and efficiently track the physical fitness of their players in real time.

1D in-situ convolution system based on vibration signal for real-time structural health monitoring

Structural health monitoring (SHM) of bridges and other building structures has been widely concerned by researchers and engineers. SHM achieved a breakthrough by using convolutional neural networks (CNNs) in deep learning. However, the traditional vision based SHM can not monitor the invisible structural damage. The traditional vibration based SHM needs to convert 1D signals into 2D images, which is easy to lose information in the conversion process, and requires a large scale of hardware circuit. In this paper, a 1D in-situ convolution system (ICS) based on vibration signal is proposed. The SHM is realized by 1D convolution calculation of the structural vibration signal detected by a high-sensitivity triboelectric nanogenerator (TENG) through one in-situ convolution transistor. Based on the natural adaptability of TENG to 1D convolution calculation, and combined with the ability of a single in-situ convolution device to replace more than eight multi conductance devices, ICS can reduce the required SHM computing array area by over 87.5%. Finally, the real-time identification of different positions and different damage levels of the steel bridge was realized by simulating the vibration response of the steel bridge.

Recent Progress in the Development of Flexible Wearable Electrodes for Electrocardiogram Monitoring During Exercise

Electrocardiogram (ECG) monitoring has recently been an important indicator of cardiac health diagnosis. In the past, ECG could be measured under limited conditions in hospitals with 12‐lead electrode systems. Recently, portable and wearable devices have offered continuous, real‐time monitoring of ECG signals in real life. However, developing wearable ECG sensors that provide low‐motion artifacts and high‐quality signals during exercise conditions is still challenging. Herein, this review reports a systematic summary of the key characteristics, properties, and requirements of flexible wearable ECG devices for the early diagnosis of heart dysfunction in dynamic motions, including exercise. In addition, the recent progress in controlling sensor adhesion and novel materials for designing dry electrodes are discussed to improve ECG signal quality in exercise. Finally, various aspects of electrode developmental challenges and limitations are reviewed, and research directions for future studies are discussed.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model

To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning model is proposed in this study. First, various monitoring signals including vibration signals, noise signals, and current signals are collected. Then, the monitoring signals are preprocessed and the time domain, frequency domain, and time–frequency domain feature indices are extracted to construct a multi-dimensional mixed-domain feature set. Finally, the feature set is entered into the constructed DoubleEnsemble–LightGBM model to realize the fault diagnosis of the key components of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the widely used dataset and the feed system test bench dataset, and the average overall accuracy is 91.07% and 98.06%, respectively. Compared with XGBoost and other advanced ensemble learning models, this method demonstrates better accuracy. Therefore, the proposed method provides technical support for the stable operation and intelligence of CNC machines.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

AbstractShell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

Sparse linear parameter-varying autoregressive moving average modelling of non-stationary gearbox vibration signals

Gearboxes frequently operate under variable speed conditions, leading to the non-stationarity of collected monitoring signals. This paper proposes a sparse linear parameter-varying autoregressive moving average (Spa LPV-ARMA) model for fault detection of gearboxes under variable speed conditions. The Spa LPV-ARMA model integrates the advantages of LPV concepts, sparse models, and ARMA models. The proposed model has a more compact structure compared to the reported Spa LPV-AR model, which results in a lower ratio of parameter number to data amount, and the compact model structure also allows the Spa LPV-ARMA model to have higher computational efficiency during the testing phase. A simulation study was conducted to validate the performance of the proposed method. The results demonstrate that the Spa LPV-ARMA model exhibits superior modeling accuracy compared to the reported Spa LPV-AR model. Additionally, the fault detection method based on the Spa LPV-ARMA model achieves a higher fault detection rate when contrasted with a model that solely considers the autoregressive component.

Accurate measurement of a THz beam radius through a knife-edge technique with a photoconductive antenna detector

In a terahertz (THz) time-domain spectroscopic system, the THz beam waist radius can be measured by eclipsing the beam waist with a semi-infinite mask while monitoring signal variation (knife-edge technique). However, the obtained beam waist radius is affected by the spatial distribution of the detector sensitivity. This effect was confirmed by calculating the refractive-index corrections required when inserting a sample into the beam waist with and without consideration of the detector sensitivity distribution and comparing them to the experimental results. The real beam waist radius was 2 times larger than that obtained by simply differentiating the signal variation in a system comprising optics symmetrical about the beam waist. This effect must be considered in obtaining the correct THz beam profile by the knife-edge technique.

Wireless Technologies in Flexible and Wearable Sensing: From Materials Design, System Integration to Applications.

Wireless and wearable sensors attract considerable interest in personalized healthcare by providing a unique approach for remote, noncontact, and continuous monitoring of various health-related signals without interference with daily life. Recent advances in wireless technologies and wearable sensors have promoted practical applications due to their significantly improved characteristics, such as reduction in size and thickness, enhancement in flexibility and stretchability, and improved conformability to the human body. Currently, most researches focus on active materials and structural designs for wearable sensors, with just a few exceptions reflecting on the technologies for wireless data transmission. This review provides a comprehensive overview of the state-of-the-art wireless technologies and related studies on empowering wearable sensors. The emerging functional nanomaterials utilized for designing unique wireless modules are highlighted, which include metals, carbons, and MXenes. Additionally, the review outlines the system-level integration of wireless modules with flexible sensors, spanning from novel design strategies for enhanced conformability to efficient transmitting data wirelessly. Furthermore, the review introduces representative applications for remote and noninvasive monitoring of physiological signals through on-skin and implantable wireless flexible sensing systems. Finally, the challenges, perspectives, and unprecedented opportunities for wireless and wearable sensors are discussed.

Design and Implementation of FIR Filters for Biomedical Applications

Abstract: Digital signal processing (DSP) greatly facilitates the continuous capture, monitoring, processing, and analysis of signals in real-world applications, particularly in biomedical or wearable devices. Within DSP systems, the design of Finite Impulse Response (FIR) filters is pivotal. In scenarios demanding intricate calculations and high precision, higher-order filters are employed. Multipliers are crucial components in filter design, consuming significant chip space and introducing extra computation overhead. Designers strive to optimize multipliers to enhance performance. In this project we are going to design pipelined based FIR filter by applying pipelining concept to the FIR filter and following the previous implementations such as the FIR filter with high accuracy compared to existing method. The proposed system architectures are known to be efficient for real signal processing, offering advantages over conventional based designs. The proposed design is anticipated to yield benefits in terms of area, power consumption, and timing performance. The synthesis and simulation of the existing and proposed designs are implemented using Cadence Virtuoso.

Dual-channel sensing strategy for melatonin detection based on iron-cobalt oxide nanosheets with peroxidase-like activity and gold nanoclusters

This work developed a sensitive fluorescence and colorimetric dual-channel sensing system for melatonin determination based on iron-cobalt oxide nanosheets (FeCo-ONSs) and gold nanoclusters (AuNCs). FeCo-ONSs exhibited excellent peroxidase (POD)-like activities, that could catalyze H2O2 to produce reactive oxygen species and further catalyze N-ethyl-N-(3-sulfopropyl)-3-methylaniline sodium (TOPS) to couple with 4-amino-antipyrine (4-AAP) to generate purple compounds (PCs), which had absorption at 549 nm. The generated PCs can quench the fluorescence of AuNCs at 577 nm via fluorescence resonance energy transfer (FRET). The process of the generation of purple compounds would be restrained in existence of melatonin due to its scavenging capacity for free radicals. As a consequence, the absorption at 549 nm decreased, while the fluorescence quenching of AuNCs was weakened, and emission intensity at 577 nm progressively recovered. The recovery of fluorescence and the decrease in absorption depended on the concentration of melatonin. Therefore, by employing fluorescence and colorimetric dual signal monitoring, the linear ranges of melatonin were 5–300 μM and 5–800 μM, and the LODs were 1.17 μM and 2.13 μM, respectively.

Real-Time Multi-Sensor Joint Fault Diagnosis Method for Permanent Magnet Traction Drive Systems Based on Structural Analysis.

Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage-Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value.

A reusable, healable, and biocompatible PEDOT:PSS hydrogel-based electrical bioadhesive interface for high-resolution electromyography monitoring and time–frequency analysis

As the most ideal interface between bioelectronic devices and biological tissues, the electrical bioadhesive interface (EBI) holds significant potential for various applications, including soft bioelectronics, wearable devices, and electrophysiological signal monitoring. However, most current EBIs are disposable products that lack repeatable adhesion and self-healing properties, thus hindering practical applications. Herein, we develop a reusable EBI by incorporating a dynamic non-covalent interaction enhancer polydopamine (PDA) and conductive PEDOT:PSS into an adhesive hydrogel matrix polyacrylamide (PAM). The EBI exhibits intriguing overall electrical and mechanical properties, including high conductivity (5.57 S m−1), tissue-like Young’s modulus (2.7 kPa), low interfacial impedance (1 kΩ), and excellent adhesion strength (46.45 ± 1.75 kPa on porcine skin) with excellent retention (87 %) after 200 adhering/detaching cycles, high self-healing efficiency of 94.11 %, and outstanding biocompatibility in HDF-a and HaCaT cell models. Benefiting from these superior properties, we further demonstrate the fabrication and application of an EBI-based reusable electromyography monitoring patch (EMP). The reusable EMP enables high-resolution electromyography signal monitoring capability with a higher signal-to-noise ratio (SNR) of 30.05 dB than commercial electrodes (12.06 dB). Remarkably, such reusable EMP maintains very stable SNR of 21.82 dB after 200 adhering/detaching cycles (3.8 times higher than commercial electrodes, 5.74 dB after 30 cycles), and showcases typical time and frequency-domain characteristics. These findings demonstrate a promising reusable EMP to enhance the accuracy and convenience of electromyography monitoring.

Layer-by-layer self-assembly of durable, breathable and enhanced performance thermoelectric fabrics for collaborative monitoring of human signal

Thermoelectric devices have received widespread attention in next-generation flexible electronics due to their excellent self-powered characteristics, multifunctionality and high integrability. However, current thermoelectric devices suffer from drawbacks such as poor durability, complex preparation processes, and terrible air permeability which is the necessary metric in wearing comfort. Here, we propose a layer-by-layer (LBL) self-assembly strategy to prepare multifunctional thermoelectric fabrics with enhanced thermoelectric performance and durability. Based on the self-assembly effect, the interfacial bonding between the thermoelectric material and the fabric substrate is effectively improved, so that the thermoelectric properties of the fabrics can still be maintained stable after 2000 bending cycles and 600 washing cycles. Moreover, this strategy increases the electrical conductivity of the fabric by a factor of 4 without affecting the fabric’s air permeability (up to 400 mm s−1 at 50 Pa) and tensile properties (strains exceeding 100 %). Due to its wearability and multifunctionality, the fabric was designed as a synergistic monitoring system that includes facial respiration monitoring and knee joint motion monitoring for continuous monitoring of the athlete’s dynamic body condition. This thermoelectric fabric shows amazing potential for advanced smart textiles, soft robotics and human–computer interaction.