Search Results (35)

The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm³ and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals’ daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals’ physiological signals and assessing exercise intensity. Full article

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO₂) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO₂, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO₂, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems. Full article

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare. Full article

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm² with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively. Full article

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. Full article

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. Full article

Skin-based wearable devices have gained significant attention due to advancements in soft materials and thin-film technologies. Nevertheless, traditional wearable electronics often entail expensive and intricate manufacturing processes and rely on metal-based substrates that are susceptible to corrosion and lack flexibility. In response to these challenges, this paper has emerged with an alternative substrate for wearable electrodes due to its cost-effectiveness and scalability in manufacturing. Paper-based electrodes offer an attractive solution with their inherent properties of high breathability, flexibility, biocompatibility, and tunability. In this study, we introduce carbon nanotube-based paper composites (CPC) electrodes designed for the continuous detection of biopotential signals, such as electrooculography (EOG), electrocardiogram (ECG), and electroencephalogram (EEG). To prevent direct skin contact with carbon nanotubes, we apply various packaging materials, including polydimethylsiloxane (PDMS), Eco-flex, polyimide (PI), and polyurethane (PU). We conduct a comparative analysis of their signal-to-noise ratios in comparison to conventional gel electrodes. Our system demonstrates real-time biopotential monitoring for continuous health tracking, utilizing CPC in conjunction with a portable data acquisition system. The collected data are analyzed to provide accurate heart rates, respiratory rates, and heart rate variability metrics. Additionally, we explore the feasibility using CPC for sleep monitoring by collecting EEG signals. Full article

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network. Full article

In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient monitoring of electrocardiogram (ECG). The incorporation of Raspberry Pi allows for both wireless and wired interfaces, facilitating the creation of an ECG diagnostic embedded system capable of real-time detection and immediate response to any abnormalities in heart functionality. The system presented in this research encompasses a comprehensive electronic circuit comprising analog and digital components to measure and display the ECG signal. Within the analog section, the circuit performs essential signal conditioning tasks, such as signal amplification and noise filtering, ensuring a clean signal within the desired frequency range. The entire system is powered using a power bank. The digital segment incorporates an analog-to-digital converter necessary for converting the received analog signal into a digital format compatible with Raspberry Pi. A graphical liquid-crystal display is utilized to display the measured signal. The device successfully measures ECG signals at various heart rates, capturing all crucial peaks that can be used as indicators of an individual’s health condition. By comparing the signals obtained from healthy individuals with those exhibiting heart arrhythmias, valuable insights can be gained regarding their health status. The proposed system aims to be portable, cost-effective, and user-friendly in different environments. Full article

This paper presents the operation of an innovative left ventricular assist device under various resistances and operating frequencies. The operating principle of the device is based on pulsatile blood flow, which is forced by a suction–discharge device pumping helium into a set of intra-cardiac balloons. In this way, the ejection fraction of the left ventricle is increased, and the mitral valve is additionally occluded. What is more, the suction–discharge device is part of a portable pumping system that is synchronized with the heart cycle by monitoring the ECG signal. The device is implanted in a minimally invasive manner and is suitable for patients with stage D heart failure accompanied with residual mitral regurgitation. A model of the heart was built on the basis of a realistically reconstructed heart geometry and is part of an overall test stand that allows for realistic conditions in the heart of patients with end-stage heart failure to be reproduced. In the following sections, example measurements of the pressures in the heart chambers and balloons are shown, demonstrating that the device works correctly at least on a laboratory scale. The entire device, including the pumping system, is portable and powered by a set of lithium-ion batteries. From the measurements, it was observed, for example, that the flow rate varies with the frequency of the portable external balloon pumping system, up to 2.5 kg/min for 100 cycles/min at low flow resistance. As the flow resistance of the hydraulic system increases, the pressure in the heart chamber and aorta increases while the flow rate decreases. Full article

With the growing incidence of cardiovascular disease (CVD) in recent decades, the demand for out-of-hospital real-time ECG monitoring is increasing day by day, which promotes the research and development of portable ECG monitoring equipment. At present, two main categories of ECG monitoring devices are “limb lead ECG recording devices” and “chest lead ECG recording devices”, which both require at least two electrodes. The former needs to complete the detection by means of a two-hand lap joint. This will seriously affect the normal activities of users. The electrodes used by the latter also need to be kept at a certain distance, usually more than 10 cm, to ensure the accuracy of the detection results. Decreasing the electrode spacing of the existing ECG detection equipment or reducing the area required for detection will be more conducive to improving the integration of the out-of-hospital portable ECG technologies. Therefore, a single-position ECG system based on charge induction is proposed to realize ECG detection on the surface of the human body with only one electrode with a diameter of less than 2 cm. Firstly, the ECG waveform detected in a single location is simulated by analyzing the electrophysiological activities of the human heart on the human body surface with COMSOL Multiphysics 5.4 software. Then, the hardware circuit design of the system and the host computer are developed and the test is performed. Finally, experiments for static and dynamic ECG monitoring are carried out and the heart rate correlation coefficients are 0.9698 and 0.9802, respectively, which proves the reliability and data accuracy of the system. Full article

Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of studies assessing the effectiveness of clinical guidelines, mostly in muscular and therapeutic healthcare. In scientific research, surface electromyography (sEMG) is prevalent but underutilized as a valuable tool for physical medicine and rehabilitation. Other electrophysiological signals (e.g., from electrocardiogram (ECG), electroencephalogram (EEG), and needle EMG) are regularly monitored by medical specialists; nevertheless, the sEMG technique has not yet been effectively implemented in practical medical settings. However, sEMG has considerable clinical promise in evaluating muscle condition and operation; nevertheless, precise data extraction requires the definition of the procedures for tracking and interpreting sEMG and understanding the fundamental biophysics. This review is centered around the application of sEMG in rehabilitation and health monitoring systems, evaluating their technical specifications, including wearability. At first, this study examines methods and systems for tele-rehabilitation applications (i.e., neuromuscular, post-stroke, and sports) based on detecting EMG signals. Then, the fundamentals of EMG signal processing techniques and architectures commonly used to acquire and elaborate EMG signals are discussed. Afterward, a comprehensive and updated survey of wearable devices for sEMG detection, both reported in the scientific literature and on the market, is provided, mainly applied in rehabilitation training and physiological tracking. Discussions and comparisons about the examined solutions are presented to emphasize how rehabilitation professionals can reap the aid of neurobiological detection systems and identify perspectives in this field. These analyses contribute to identifying the key requirements of the next generation of wearable or portable sEMG devices employed in the healthcare field. Full article

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay. Full article

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant. Full article

Background: Athletes can experience exercise-induced transient arrythmias during high-intensity exercise or competition, which are difficult to capture on traditional Holter monitors or replicate in clinical exercise testing. The aim of this study was to investigate the reliability of a portable single channel ECG sensor and data recorder (PluxECG) and to evaluate the confidence and reliability in interpretation of ECGs recorded using the PluxECG during remote rowing. Methods: This was a two-phase study on rowing athletes. Phase I assessed the accuracy and precision of heart rate (HR) using the PluxECG system compared to a reference 12-lead ECG system. Phase II evaluated the confidence and reliability in interpretation of ECGs during ergometer (ERG) and on-water (OW) rowing at moderate and high intensities. ECGs were reviewed by two expert readers for HR, rhythm, artifact and confidence in interpretation. Results: Findings from Phase I found that 91.9% of samples were within the 95% confidence interval for the instantaneous value of the changing exercising HR. The mean correlation coefficient across participants and tests was 0.9886 (σ = 0.0002, SD = 0.017) and between the two systems at elevated HR was 0.9676 (σ = 0.002, SD = 0.05). Findings from Phase II found significant differences for the presence of artifacts and confidence in interpretation in ECGs between readers’ for both intensities and testing conditions. Interpretation of ECGs for OW rowing had a lower level of reader agreement than ERG rowing for HR, rhythm, and artifact. Using consensus data between readers’ significant differences were apparent between OW and ERG rowing at high-intensity rowing for HR (p = 0.05) and artifact (p = 0.01). ECGs were deemed of moderate-low quality based on confidence in interpretation and the presence of artifacts. Conclusions: The PluxECG device records accurate and reliable HR but not ECG data during exercise in rowers. The quality of ECG tracing derived from the PluxECG device is moderate-low, therefore the confidence in ECG interpretation using the PluxECG device when recorded on open water is inadequate at this time. Full article