Signal Monitoring Systems Research Articles

A Low-Cost Smart Wearable Glove for Non-Invasive Health Monitoring

Wearable physiological signal monitoring systems hold notable potential in the future of personal healthcare by seamlessly integrating into daily life, providing continuous monitoring, and aiding in the early detection of health issues. This research presents a wearable health monitoring glove, with a focus on cost-effectiveness while maintaining efficiency for developing countries. The wearable glove can track vital physiological indicators like Blood Pressure, Body Temperature, Glucose level, Blood Oxygen Saturation, Hemoglobin level, ECG, Room Temperature, Humidity, and Motion Tracking. A user-friendly interface facilitates easy interaction, while efforts in energy-efficient design and power management aim to prolong battery life. Also, real-time data monitoring ensures precision in signal analysis and the extraction of vital health data of individuals. The proposed wearable hand glove utilizes a collection of sensors and integrates them towards the diverse detection of skin humidity, temperature, blood oxygen, hemoglobin, etc. non-invasively. Apart from its technical features, the research explores potential applications in healthcare, fitness tracking, and research fields, presenting a versatile solution. Beyond its technical attributes, the research explores potential applications in medical and personal healthcare, fitness tracking, sports, etc. Collaborative efforts with regulatory bodies assess the feasibility of obtaining necessary approvals or certifications, while scalability considerations pave the way for potential mass production and market deployment.

Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results

Real-time vital signs monitoring, particularly heart rate, is essential in today's medical practice and research. Heart rate detection allows the doctor to monitor the patient's health status to provide immediate action against possible cardiovascular diseases. We present a possible alternative to traditional heart rate signal monitoring systems, a cardiac pulse system using low-cost piezoelectric signal identification. This system could benefit health care and develop continuous pulse waveform monitoring systems. This paper introduces a heartbeat per minute (BPM) cardiac pulse detection system based on a low-cost piezoelectric ceramic sensor (PCS). The PCS is placed under the wrist and adjusted with a silicone wristband to measure the pressure exerted by the radial artery on the sensor and thus obtain the patient's BPM. We propose a signal conditioning stage to reduce the sensor's noise when acquiring the data and make it suitable for real-time BPM visualization. As a comparison, we performed a statistical test to compare the low-cost PCS with types of traditional sensors, along with the help of 21 volunteers. Experimental results show that the data collected by the PCS, when used for heart rate detection, is highly accurate and close to traditional sensor measurements. Therefore, we conclude that the system efficiently monitors the cardiac pulse signal in BPM. Keywords: Heart rate; Piezoelectric, BPM; Pulse Detection.

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.

A Bootstrapped Pseudo Resistor by Reusing OTA-C Filter for Neural Signal Processing

In integrated neural signal monitoring systems, pseudo resistor (PR) is widely used to form very large time constant filters, due to its large impedance within an acceptable die area. However, its linearity (the dependency of the resistance to applied voltage) and impedance are limited by the nonlinear MOS transistors in weak inversion, and suffers from process, voltage and temperature (PVT) variations. In this brief, a PR bootstrapped by reusing the operational transconductance amplifier-capacitor (OTA-C) filter is presented, it substantially increases the impedance and linearity to dynamically support lower frequency neural signal applications. It has no need for extra bias scheme on the gate or bulk, which decreases the circuit complexity, die area and power consumption. The proposed circuit implemented in a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process occupies an area of 0.026 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the impedance of the bootstrapped PR is approximately 94 G <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> , and the high-pass cut-off frequency is reduced to 0.5 mHz.

Biopotential Signal Monitoring Systems in Rehabilitation: A Review.

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Synthesis Algorithm for Effective Detection of GNSS Spoofing Attacks

As the strength of global navigation satellite system (GNSS) signals is very low, they are vulnerable to interference and susceptible to attacks motivated by economic, military, and security reasons. These threats are gradually increasing. The most common attack is jamming, in which a strong signal is used to make a receiver miss the GNSS signal. As attacks become more sophisticated, they are expected to evolve to spoofing interference, in which the receiver is deceived. Spoofing interference is a larger threat because the receiver cannot recognize that they are being targeted by an attacker. For this reason, it is becoming more important to monitor GNSS signals so that they can be evaluated in terms of reliability. In this paper, we analyze spoofing detection methods with respect to the navigation solution, measurements, and navigation messages obtained by a receiver. We propose an advanced detection method for overcoming the limitations of each individual detection algorithm. The proposed methods enhance the performance of spoofing detection and reduce the false alarm rate. This research can be applied directly to GNSS signal monitoring systems and will be helpful for enhancing the stability of satellite navigation systems.

Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion.

Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood pressure (BP) has recently been suggested. Therefore, estimation of heartbeat location requires information fusion from multiple signals. However, reported efforts in this direction often obtain multimodal estimates somewhat indirectly, by voting among separately obtained signal-specific intermediate estimates. In contrast, we propose to directly fuse information from multiple signals without requiring intermediate estimates, and thence estimate heartbeat location in a robust manner. We propose as a heartbeat detector, a convolutional neural network (CNN) that learns fused features from multiple physiological signals. This method eliminates the need for hand-picked signal-specific features and ad hoc fusion schemes. Furthermore, being data-driven, the same algorithm learns suitable features from arbitrary set of signals. Using ECG and BP signals of PhysioNet 2014 Challenge database, we obtained a score of 94%. Furthermore, using two ECG channels of MIT-BIH arrhythmia database, we scored 99.92%. Both those scores compare favorably with previously reported database-specific results. Also, our detector achieved high accuracy in a variety of clinical conditions. The proposed CNN-based information fusion (CIF) algorithm is generalizable, robust and efficient in detecting heartbeat location from multiple signals. In medical signal monitoring systems, our technique would accurately estimate heartbeat locations even when only a subset of channels are reliable.

CORDIC-Based VLSI Architectures of Running DFT with Refreshing Mechanism

Running discrete Fourier transform (running DFT) is being used to overcome the drawbacks of ping pong buffer technique by employing fast Fourier transform (FFT) for real time spectrum analyzer, However, the major drawback of existing MAC or CORDIC (CO-ordinate Rotational DIgital Computer) based computation of running DFT is error accumulation due to finite precision machine and iterative computation which deteriorate the output in long run. Hence, two new alternative CORDIC based architectures with trade-off area and throughput for the computation of running DFT with refreshing mechanism are proposed in this paper to circumvent the problem of error accumulation. The novelty of these two proposed architectures is low hardware complexity or high throughput and provides the spectrum updates on sample-by-sample basis within tolerable error limit. The proposed designs are implemented using verilogHDL and synthesized using commercially available 0.18μ m CMOS technology to estimate its computation performances. The first order hardware complexities of these proposed architectures are compared with existing architectures. The proposed designs in this paper have potential applications for continuous monitoring of Fourier spectrum in the domain of power spectrum analysis, biomedical signal monitoring systems and communication system.

Self-Powered Pulse Sensor for Antidiastole of Cardiovascular Disease.

Cardiovascular diseases are the leading cause of death globally; fortunately, 90% of cardiovascular diseases are preventable by long-term monitoring of physiological signals. Stable, ultralow power consumption, and high-sensitivity sensors are significant for miniaturized wearable physiological signal monitoring systems. Here, this study proposes a flexible self-powered ultrasensitive pulse sensor (SUPS) based on triboelectric active sensor with excellent output performance (1.52 V), high peak signal-noise ratio (45 dB), long-term performance (107 cycles), and low cost price. Attributed to the crucial features of acquiring easy-processed pulse waveform, which is consistent with second derivative of signal from conventional pulse sensor, SUPS can be integrated with a bluetooth chip to provide accurate, wireless, and real-time monitoring of pulse signals of cardiovascular system on a smart phone/PC. Antidiastole of coronary heart disease, atrial septal defect, and atrial fibrillation are made, and the arrhythmia (atrial fibrillation) is indicative diagnosed from health, by characteristic exponent analysis of pulse signals accessed from volunteer patients. This SUPS is expected to be applied in self-powered, wearable intelligent mobile diagnosis of cardiovascular disease in the future.

A preliminary study of muscular artifact cancellation in single-channel EEG.

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

Reliability of Monitoring Signals for Estimation of Surface Roughness in Metallic Turned Parts

Current trends in machining processes are focused in three goals: to increase the productivity and the reliability and to minimize costs. In this context, the development of signal monitoring systems is of vital importance for surface roughness inspection. One of the research lines associated to this context is oriented to predict surface roughness using indirect signal analysis, such as cutting forces or vibrations in the machining process. This paper analyzes the results obtained when comparing different nature signals combined with cutting parameters. The final goal is to quantify the deviations obtained with different monitoring signals for establishing the best ones to use as roughness evaluators. The best predictions were obtained when force and cutting conditions were combined together. The absolute error values remains always below 1.28 and 1.11 µm when using the median and root mean square (RMS) as descriptors, respectively.

재긴장과 무선 모니터링이 가능한 유압식 케이블 접합부시스템의 실험에 대한 연구

케이블 구조시스템의 경우 자기 평형 상태를 유지하기 때문에 장력이 손실된 특정 케이블을 재긴장을 하지 않으면 구조엔지니어가 요구한 하중보다 더 큰 하중이 다른 케이블에 전달되어 손상을 야기 할 수 있다. 또한, 턴버클을 이용한 재긴장 방법이 기존에 널리 적용되고 있지만 정확한 장력조절과 대구경 케이블에는 적절하지 못하고 인장재의 하중의 크기를 측정하는 것이 어렵다. 따라서 효과적으로 재긴장 할 수 있는 유압식 볼팅 접합부를 개발하고 인장력을 실시간으로 확인할 수 있는 모니터링 시스템을 적용하였다. 본 논문에서는 개발된 시스템의 현장 적용성 실험과 결과를 제시하였다. Due to the self-equilibrium status of the cable system, the loss of the tensioning in the cable system results in other cables carrying larger tension forces than those initially calculated by structural engineers. Also, turn-buckle systems, which have been widely used to pre-tension and/or re-tension the cables, are limited to use for small cables and to provide a rough estimation for tension forces. In this study, the re-tensioning cable connection systems were developed to overcome the problems mentioned above. The main objective of the proposed system is to re-tension large cables and measure the exact amount of tension forces of the cable systems. This connection system is also combined with the wireless signal monitoring module so that engineers are able to measure the tension forces any place where the internet is available. This paper presents the development of the re-tensioning cable connection systems and experiment using the real-scale cable systems to verify the fe-tensioning and signal monitoring systems.

Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and its Application on Real-Time Driver's Drowsiness Detection and Warning

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

Functional source separation and hand cortical representation for a brain-computer interface feature extraction.

A brain-computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features - spatial and time-frequency properties - are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients.

New iontophoretic transdermal administration of the beta-blocker metoprolol

The beta-blocker, metoprolol, was introduced transdermally into the veins from a small, square electrode pad (50 cm 2) on the forearm by a newly developed iontophoretic device without causing any detectable skin damage. The appliance generates a sophisticated pulse of 12 V at 50 kHz with 20% duty (4 μs) followed by an 80% depolarizing period (16 μs) which may avoid skin irritation caused by polarization. The drug concentration in plasma increased quickly to the therapeutic level which was maintained for a longer period within one session compared with conventional oral administrations. A substantial amount of the drug was also detected in the urine during iontophoresis as well as after the cessation of iontophoresis. These results suggest a possible application of the iontophoretic device for the controlled administration of metoprolol and probably other beta-blockers in combination with cardiographic signal-monitoring systems such as Hotler systems (ambulatory electro-cardiographs) or programmes designed for special modes.