Wearable Monitoring System Research Articles

Multifunctional, breathable MXene-PU mesh electronic skin for wearable intelligent 12-lead ECG monitoring system

With the advent of an aging society and increasing concern for cardiovascular health, comfortable long-term electrocardiographic (ECG) monitoring systems in daily life are much in demand. If the system can give real-time feedback on cardiovascular status and offer timely personalized advice like doctors, it will be beneficial to promote cardiovascular health and alleviate the imbalance of medical resources. Herein, we report a multifunctional, conformal, and air-permeable electronic skin (e-skin) based on MXene-Polyurethane mesh (MPM). When used as ECG electrodes, the e-skin shows ultra-low electrode–skin contact impedance (4.68 kΩ at 1 kHz), high signal-to-noise ratio (16.5 dB), and high breathability (2.1838 kg/m2/day), and can be continuously worn for at least 7 days. As a strain sensor, the e-skin exhibits a large measurement range (>40 %) and unique segmental sensitivity (GF = 172.54 in 0 ∼ 18.64 % and 79.38 in 18.64 ∼ 50 %), which is suitable for detecting both subtle and large physiological signals. More significantly, by combining the MPM e-skin with a flexible printed circuit board (FPCB) and a merged convolutional neural network & long-short-term memory (CNN & LSTM) intelligent algorithm, the acquired wearable intelligent e-skin system can realize daily 12-hour 12-lead ECG monitoring with diagnostic accuracy over 99 % for four arrhythmias. These results show that the MPM e-skin has huge potential in multifunctional intelligent healthcare systems. In particular, an intelligent ECG system similar to a “daily doctor” is proposed for daily long-term cardiovascular health monitoring.

Motion Artifact Reduction in Electrocardiogram Signals Through a Redundant Denoising Independent Component Analysis Method for Wearable Health Care Monitoring Systems: Algorithm Development and Validation.

The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices. We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method's performance and to compare its performance to that of existing approaches. We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient's chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis. The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG. Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices' cost-effectiveness and contributes to their widespread application.

Ultra-fast self-healable stretchable bio-based elastomer/graphene ink using fluid dynamics process for printed wearable sweat-monitoring sensor

The practical application of wearable real-time monitoring systems is currently restricted by the irreversible mechanical damage and fracture of their sensors during physical activity, which has inspired the development of self-healable ink materials. However, progress in this field is limited by the difficulty of combining high mechanical strength with high conductivity and realizing scalable and efficient fabrication. Herein, a fluid dynamics process is used to prepare a self-healable, robust, and conductive ink through the incorporation of graphene into a self-healing polymer, namely poly(1,4-cyclohexanedimethanol succinate-co-citrate). The corresponding screen-printed serpentine-structured electrode exhibits spontaneous conductivity self-healing during 10 cut-heal cycles and at a tensile strain of 200% under ambient conditions. These unique properties are used to fabricate a highly stretchable electrochemical Na+ sensor with a high self-healing efficiency (average sensitivity retention of 99%). On-body tests reveal that this wearable sensor is well suited for the real-time monitoring of sweat during physical exercise and is capable of autonomous repair after mechanical cutting, showing a healing time of ∼12 s. Thus, our work paves the way to the commercialization of devices typically requiring a combination of mechanical resilience and stable electrical performance, e.g., e-skin, sweat sensors, soft robotics, and biofuel cells.

A flexible, stretchable and triboelectric smart sensor based on graphene oxide and polyacrylamide hydrogel for high precision gait recognition in Parkinsonian and hemiplegic patients

Intelligent gait recognition system plays an important role in the field of identity recognition, physical training and medical diagnostics. In clinical medicine, no definitive diagnostic tool has been developed for the diagnosis of Parkinson’s disease and hemiplegia. Thus, there is an urgent need to develop an effective and portable human-machine interaction system to monitor and recognize these symptoms. Herein, a self-powered strain sensor based on graphene oxide-polyacrylamide (GO-PAM) hydrogels is reported to monitor subtle human motions, including gait movements. The sensor can be used as a triboelectric nanogenerator (TENG) to collect mechanical energy. The output power of the TENG based on the 0.02 wt% GO-PAM hydrogel was up to 26 mW, which was 2.2 times that of the pure PAM hydrogel film. The capability of the TENG in powering electrical devices was demonstrated by lighting up 353 light-emitting diodes (LEDs) and powering an electronic thermometer. Besides, a wearable in-shoe monitoring system was designed which includes a flexible insole, a data processing module and a PC interface developed using Python. Among the models with different algorithms, the system with the artificial neural network (ANN) exhibits the highest recognition accuracy of 99.5 % and 98.2 % for human daily-life gait and pathological gait, respectively. This system provides a more convenient option for human gait monitoring and recognition, which can be used for a wide range of medical applications such as early diagnosis, rehabilitation evaluation and treatment of patients.

A Flexible Optoelectronic Device for Continuous Cerebral Blood Flow Monitoring.

Human cerebral oxygenation and hemodynamics can be estimated by cerebral oxygenation parameters. Functional near-infrared spectroscopy (fNIRS) can be used to measure the hemoglobin concentration index of brain tissue noninvasively and in real time. However, limited by cumbersome equipment, high price and uncomfortable wear, conventional fNIRS monitoring systems still cannot achieve continuous and long-term monitoring. In this work, a flexible and wearable long-term monitoring system is developed featured with cost efficiency, simple preparation and light weight (only 1.6 g), which consists of a pair of light-emitting diodes (LEDs) and a photodetector (PD). Triangular serpentine interconnectors are introduced to connect the functional elements, enabling the device to be stretched in multiple directions. The device can continuously work for 7 h and be subjected to 2000 cycles of bending loading, with less than 3% change in voltage value, 1.89% and 1.9% change in LED luminous power and 0.9% change in voltage value. Furthermore, the hand-gripping and breath-holding experiments show that the system can accurately measure the changes in hemoglobin concentration in accordance with the commercial device. The flexible fNIRS system presented here not only provides a simple preparation process but also offers new ideas for daily cerebral state monitoring and prolonged clinical monitoring.

Correlation between corneal and contact lens deformation with changes in intraocular pressure for wearable monitoring systems.

The aim of this work is to evaluate the extent to which the eye's curvature deformation, due to changes in the intraocular pressure (IOP), can be directly tracked by an overlying contact lens. In this experimental study, using 12 cadaveric eyes, the IOP was increased from 10 to 36 mmHg, while video imaging was used to capture the three experimental variations. The deformation of the bare eye was used as a control, while the deformation of an overlying silicone grided contact lens and an overlying microfluidic IOP-sensing contact lens were examined and compared. The relation between the slope of the radius of corneal curvature versus the IOP for both the bare eye and the marker contact lens yielded a linear relationship with a R2 value of 0.83. The microfluidic contact lens resulted in an average performance of 0.40 mm indicator movement/mmHg (SD 0.006). Comparing the slope of the marker contact lens deformation, to the performance of the microfluidic contact lens resulted in a R2 value of 0.78. The strain map of the overlaying grided contact lens showed most deformation occurring along the outer edge of the lens with increased deformation as increase IOP occurs; as well as with some negative, compressive movement near the central points. The deformation from the curvature of the eye is significant enough from 10 to 36 mmHg that a silicone contact lens can capture and mimic those changes. The results show promise for optimization in contact lens-based IOP monitoring.

Design of a wearable multi-point temperature monitoring system – practical application in waist temperature monitoring

PurposeHuman skin temperature data can provide reference advice for diagnosing many diseases, but current wearable skin temperature monitoring systems have few points and may miss temperature information in critical areas. This paper aims to develop a wearable system that can be used for local temperature monitoring and restore better the actual state of local human temperature by exploring ways to extend more temperature measurement points. This will provide better assistance in developing medical targeting and intelligent clothing.Design/methodology/approachThe temperature measurement system contains modules for temperature monitoring, digital-to-analog conversion and regulated power supply, enabling fast reading of 12 channels of monitoring data. The microprocessor unit is the STM32F407. The instructions and control modes are written in C. The waist area was chosen as the monitoring area because it is susceptible to temperature, and many diseases are associated with skin temperature. Twelve points, including temperature-related acupuncture points and heat-sensitive points, were selected for testing and the data results agreed well with the infrared imaging results.FindingsThe waist is selected as the monitoring object, and an easy-to-wear waist temperature monitoring belt is designed to verify the application value of the system. The development of the system provides reference suggestions for the exploration of multi-point temperature monitoring systems and the integration capabilities of temperature measurement modules in wearable multifunctional systems.Practical implicationsIn addition to waist temperature monitoring, the wearable temperature measurement system developed in this study can also be applied to other body parts. In addition, the system can be efficiently and effectively combined with various garments, making it a useful tool for researching human skin temperature.Originality/valueThe wearable temperature monitoring system designed in this paper extends the number of temperature test points to 12. The number of test points covers as many localized body areas as possible to indeed reproduce the temperature distribution of the human body. In addition, the selection of test points combines medically relevant body points with physiologically relevant heat-sensitive areas, which makes the temperature measurement data more valuable.

A Nanoporous Carbon‐MXene Heterostructured Nanocomposite‐Based Epidermal Patch for Real‐Time Biopotentials and Sweat Glucose Monitoring

AbstractDespite substantial progress in the development of wearable and flexible monitoring systems that conform to the epidermis, most designs focus on either physiological signs such as the electrocardiogram (ECG) results, respiration rate, or metabolites, and ignore the dynamic fluctuations of pH and temperature in sweat during on‐body tests. An advanced butterfly‐inspired hybrid epidermal biosensing (bi‐HEB) patch is presented here, which is interfaced with a custom‐developed miniaturized monitoring system. The patch incorporates a novel transducing layer of nanoporous carbon and MXene (NPC@MXene) for sensitive and durable detection of biomarkers in sweat. The bi‐HEB patch is composed of a glucose biosensor accompanied by pH and temperature sensors to precisely quantify glucose as well as two biopotential electrodes, allowing real‐time recording of electrophysiological (EP) signals. The NPC@MXene‐based glucose biosensor demonstrates an excellent sensitivity of 100.85 µAmm−1 cm−2 within physiological levels (0.003−1.5 mm), and variations in pH and temperature during on‐body perspiration monitoring are calibrated by employing a correction approach. In parallel, the EP electrodes exhibit skin‐electrode contact impedance and biopotential signals similar to those of conventional Ag/AgCl electrodes. Finally, the bi‐HEB patch integrated wearable system is used to accurately monitor sweat glucose and ECG of human subjects participating in indoor physical activities.

Design of pulse and respiration monitoring system based on fiber optic sensing and VMD-FPR processing algorithm

We propose a pulse and respiration monitoring system based on fiber optic sensing, to achieve accurate monitoring of human pulse and respiration rate with wearable and high comfort. We designed a flexible packaging structure -- to embed the fiber Bragg grating sensors (FBGs) fiber optic sensor into a nylon bandage, which constitutes a wearable real-time monitoring system that can acquire pulse and respiration rates simultaneously. The system uses the Variational Mode Decomposition - Feature Point Recognition (VMD-FPR) signal processing algorithm to demodulate and analyze the FBG echo signal to invert the pulse and respiration rate. The experiments were conducted on human heart rate and respiration rate in different states. The experimental results showed that the error range of respiration rate was within ±0.2 bpm, and the accuracy of the respiration experiment feature point recognition algorithm was 93.29 %. The error range of heart rate was within ±2 bpm, and the accuracy of the pulse experiment main wave feature point recognition algorithm was 99.28 %. Thus, the system can accurately collect and analyze the feature points in the respiratory and pulse wave signals. The information obtained from the experiments is used to build an information base, which provides data support for establishing the analysis model of pulse characteristics and respiratory characteristics of the detectors, and to achieve the function of real-time monitoring of human physiological parameters.

Wearable system for outdoor air quality monitoring in a WSN with cloud computing: Design, validation and deployment

Breathing poor-quality air is a global threat at the same level as unhealthy diets or tobacco smoking, so the availability of affordable instrument for the measurement of air pollutant levels is highly relevant for human and environmental protection. We developed an air quality monitoring platform that comprises a wearable device embedding low-cost metal oxide semiconductor (MOS) gas sensors, a PM sensor, and a smartphone for collecting the data using Bluetooth Low Energy (BLE) communication. Our own developed app displays information about the air surrounding the user and sends the gathered geolocalized data to a cloud, where the users can map the air quality levels measured in the network. The resulting device is small-sized, light-weighted, compact, and belt-worn, with a user-friendly interface and a low cost. The data collected by the sensor array are validated in two experimental setups, first in laboratory-controlled conditions and then against referential pollutant concentrations measured by standard instruments in an outdoor environment. The performance of our air quality platform was tested in a field testing campaign in Barcelona with six moving devices acting as wireless sensor nodes. Devices were trained by means of machine learning algorithms to differentiate between air quality index (AQI) referential concentration values (97% success in the laboratory, 82.3% success in the field). Humidity correction was applied to all data.

Wearable Exoskeleton System for Energy Harvesting and Angle Sensing Based on a Piezoelectric Cantilever Generator Array.

Wearable exoskeletons are developing rapidly due to their superiority in improving human ability and efficiency. The construction of a multifunctional exoskeleton system relies on an efficient continuous energy supply and various high-performance sensors. Here, a magnetic-driven piezoelectric cantilever generator (MPCG) array is designed for energy harvesting and angle sensing of joint motions. Combining theoretical derivation and experimental characterization, it is found that the nonlinear magnetic force acting on the cantilever structure will cause the phenomenon of frequency upconversion, which greatly improves the output of the MPCG. The experiment successfully proves the feasibility of using the MPCG array as an energy-harvesting module to collect energy from human joint motions and power an RH/temp sensor. Furthermore, the MPCG array can also be used to sense the rotation angle and angular velocity. By integrating with a wireless data acquisition and transmission module and supporting software, a wearable joint rehabilitation monitoring and assessment system is built, which can measure the activities of the joint in real time and evaluate the flexion degree. The demonstrated wearable exoskeleton system for joint motion energy harvesting and joint angle sensing is of great value for the construction of a multifunctional exoskeleton system and wearable smart rehabilitation equipment.

3차원 구조를 갖는 유연 및 신축 소자

Developments of ways to fabricate complex three-dimensional (3D) structures enable the controlling of physical and chemical properties of the electrical systems. Especially, owing to the rapid developments of the fabrication processes (e.g., the 3D printing, origami, and mechanical buckling process), researchers have integrated multifunctional and sophisticated 3D structures with flexible and stretchable substrates for high electrical, mechanical, and optical performances. In this review, we highlight the latest research on flexible and stretchable electric systems integrated with 3D structures such as a super-sensitive pressure sensor, a high-performance wearable monitoring system, a multi-functional cell scaffold, a foldable thermoelectric generator, a wearable energy harvesting system, a hemispherical photodetector array, and a projection screen with the reversible state changes. In subsequent sections, we summarize the advanced research results and provide future strategies for flexible and stretchable 3D electronic devices.

Development of a Wireless Wearable Body Temperature Measurement System Based on NTC

Body temperature is an important physiological parameter of the human body and is used in medicine to reflect the physiological state and health status of the human body. At present, the commonly used clinical thermometers on the market are mainly divided into contact and non-contact types. Most of them are used for rapid body temperature measurement, and it is not easy to monitor body temperature changes in real time. This article introduces a new wearable wireless body temperature monitoring system based on NTC, which senses through NTC. The temperature changes are amplified and filtered, zeroed, and calibrated, and then the temperature data is uploaded to the mobile phone APP via Bluetooth in real time to achieve real-time accurate measurement of body temperature.

Smartphone-Based Electrochemical Systems for Glucose Monitoring in Biofluids: A Review.

As a vital biomarker, glucose plays an important role in multiple physiological and pathological processes. Thus, glucose detection has become an important direction in the electrochemical analysis field. In order to realize more convenient, real-time, comfortable and accurate monitoring, smartphone-based portable, wearable and implantable electrochemical glucose monitoring is progressing rapidly. In this review, we firstly introduce technologies integrated in smartphones and the advantages of these technologies in electrochemical glucose detection. Subsequently, this overview illustrates the advances of smartphone-based portable, wearable and implantable electrochemical glucose monitoring systems in diverse biofluids over the last ten years (2012–2022). Specifically, some interesting and innovative technologies are highlighted. In the last section, after discussing the challenges in this field, we offer some future directions, such as application of advanced nanomaterials, novel power sources, simultaneous detection of multiple markers and a closed-loop system.

Quantitative analysis of heart rate variability parameter and mental stress index.

BackgroundCardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.MethodsHRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.ResultsExperimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.ConclusionOur algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

Design of a multi-channel high precision wearable temperature collection system based on negative temperature coefficient thermistor

<p>Body temperature is often used to screen infectious diseases and monitor treatment. Through the method of measuring the resistance of constant voltage temperature measuring circuit, a wearable multi-point body temperature monitoring system is researched and designed to determine skin surface temperature. The STM32F103C8T6 chip is used as the core processor, and the negative temperature coefficient thermistor (NTC) as the temperature sensing component. ADS1256 chip is a temperature signal conditioner, Bluetooth module is a wireless transmission unit, and LABVIEW is used to design the host computer interface. The constant voltage bridge circuit composed of thermistor and resistor voltage divider to carry out the acquisition of 8 channels of temperature data, and the 24bits ultra-high-precision analog-to-digital conversion module is configured with differential inputs to amplify, filter and convert analog signals; the converted data is processed and calculated in the single-chip microcomputer; finally, the data is transmitted to the host computer via Bluetooth. The thermistor is linearly compensated using the fourth-order formulation of the Stein-hart formula. Reduce the impact of environmental interference and uneven body temperature distribution from software and hardware. The error during the temperature measurement of temperature sensor is analyzed. The experimental results showed that the resolution of measurement system reached 0. 01 , and the temperature measurement accuracy was up to ± 0. 02 . This design scheme has high stability and accuracy; and the circuit is simple in structure, small in size, and low power consumption which can be used in occasions requiring precise body temperature measurement.</p>

Recent Advances toward Wearable Sweat Monitoring Systems

AbstractWearable biosensors are playing an increasingly important role in society. Compared with traditional wearable biosensors for detecting blood pressure, blood oxygen, or pulse conditions, which can only access information from the physical level, biosensors for testing body fluids can provide more details on health conditions through the analysis of biochemical criteria. Sweat secreted from glands distributed throughout the body contains abundant biochemical information and is an indicator of the physical conditions. Because of the noninvasive and safe sampling method, wearable sweat monitoring systems have the potential to realize long‐term and wearable detection. In this review, the current situation of wearable sweat monitoring systems is summarized from three critical parts: the sweat collection method, the sweat analysis method, and the energy supply. Finally, based on the existing drawbacks of wearable sweat monitoring systems in previous studies, the authors creatively propose droplet‐based detection, triboelectric nanogenerator‐based detection, and self‐powered systems as new directions for future research. The proposed approaches are expected to promote the commercialization process of wearable sweat monitoring systems.

Design Strategies and Prospects in Developing Wearable Glucose Monitoring System Using Printable Organic Transistor and Microneedle: A Review

Diabetes mellitus is a worldwide leading disease that brings about fatal complications due to poor body glucose management which requires regular screening. With progressive evolution of healthcare monitoring system, high demand for wearable biosensors has efficiently bridged the gap between organic semiconductor devices and biological systems due to their flexibility and biocompatibility as enzyme-based transducing mechanism. In this work, significant features of existing glucose monitoring systems and integration components are evaluated to extend the fully integrated sensing platform with exciting potentials of organic thin-film transistor (OTFT) as electrochemical glucose sensor at low manufacturing process <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 200 ^{\circ }\text{C}$ </tex-math></inline-formula> . This review highlights the design strategies and challenges in developing printable OTFT devices with low voltage and high on/off-current ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt; 10^{6}$ </tex-math></inline-formula> by considering material configuration including nanomaterials, advanced printing techniques and functionalization for highly sensitive performance based on amperometric or potentiometric measurements. For sample extraction, the role of microneedle is critically discussed with their prospects to provide real-time diagnosis of interstitial fluid through correlation with blood glucose for long wear period. It can be concluded that this paper accentuates the potentials of printable OTFT biosensors and ISF sampling system based on microneedle by adapting the key design requirements and characteristics presented here for novel breakthrough of cost-effective, minimally invasive and self-monitoring flexible integrated glucose sensor system that conform to skin.

Acquisition and Automated Segmentation of Inertia Sensor Data for Mobile Camptocormia Assessment.

The camptocormia angle has been established as a strong indicator for evaluating the progress of Parkinson's disease and the efficacy of therapeutical approaches. A wearable setup is proposed to measure the camptocormia angle with the perpendicular method using five inertial sensors. This study identifies suitable inertial measurement unit sensors for mobile long-term measurement. Moreover, a machine-learning approach is presented for segmenting the recorded data into periods with different dominant activities. An artificial neural network was the better classifier compared to a support vector machine to recognize certain common activities in patients with camptocormia. The artificial neural network's accuracy, sensitivity, and F1-score were 92.4 %, 82.9 %, and 82.1 %, respectively. Clinical Relevance- The presented approach is expected to lead to a wearable system for long-term monitoring of the progress of camptocormia, yielding improved parameters compared to the conventional static photo method.

A proof-of-concept real-time processing to characterize vascular flow.

In dialysis patients, monitoring vascular flow of the surgically created arteriovenous fistula (AVF) is critical to indicate the success of the AVF as a dialysis access site. Current gold standard to quantify vascular flow involves external doppler evaluation which requires frequent visits to the clinic. In this paper, we present a proof-of-concept cost-efficient vascular flow monitoring system towards a wearable and robust blood flow monitoring system. The proposed system captures beat-to-beat blood flow from impedance plethysmography (IPG) signal and performs embedded computing to robustly map the changes in the IPG to peripheral blood flow. We present the proof-of-concept results for the embedded real-time blood flow computing from measurements obtained using a custom electrical bioimpedance hardware presented previously elsewhere. We anticipate the results serving as the first step towards potentially eliminating the need for using expensive and bulky systems that require specialized personnel to operate for peripheral blood flow monitoring. Clinical relevance-The study paves the way to engineering a ubiquitous blood flow monitoring system for patients who have a surgically created Arteriovenous fistula (AVF) for dialysis vascular access.