Wearable Biomedical Research Articles

Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects.

Due to the rapid developments in materials science and fabrication techniques, wearable devices have recently received increased attention for biomedical applications, particularly in medical ultrasound (US) imaging, sensing, and therapy. US is ubiquitous in biomedical applications because of its noninvasive nature, nonionic radiating, high precision, and real-time capabilities. While conventional US transducers are rigid and bulky, flexible transducers can be conformed to curved body areas for continuous sensing without restricting tissue movement or transducer shifting. This article comprehensively reviews the application of flexible US transducers in the field of biomedical imaging, sensing, and therapy. First, we review the background of flexible US transducers. Following that, we discuss advanced materials and fabrication techniques for flexible US transducers and their enabling technology status. Finally, we highlight and summarize some promising preliminary data with recent applications of flexible US transducers in biomedical imaging, sensing, and therapy. We also provide technical barriers, challenges, and future perspectives for further research and development.

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

A Reconfigurable Bidirectional Wireless Power and Full-Duplex Data Transceiver IC for Wearable Biomedical Applications.

This paper presents a reconfigurable bidirectional wireless power and data transceiver (RB-WPDT) integrated circuit (IC) for wearable biomedical applications. The proposed transceiver can be reconfigured as a differential class-D power amplifier or a full-wave rectifier depending on the mode signal to facilitate power transfer between devices. Additionally, the RBWPDT system supports full-duplex (FD) data transmission via a single inductive link, enabling real-time control and monitoring between devices. The proposed FD method utilizes frequency shift-keying pulse-width modulation (FSK-PWM) for downlink and load shift-keying (LSK) for uplink, achieving simultaneous bidirectional data transmission by ensuring that the FSK-PWM downlink and LSK uplink data channels operate independently with minimal interference. The measured downlink and uplink data rates are 250 kb/s and 67 kb/s, respectively. The measured overall DC-to-DC efficiency is 49%, while the power delivered to the load (PDL) is 120 mW at a 5 mm distance. The proposed chip is fabricated using a 180-nm BCD CMOS process.

Low Profile Orthomode Full-Duplex Antenna for Wearable Biomedical Communication

Low Profile Orthomode Full-Duplex Antenna for Wearable Biomedical Communication

Synthesis of Power Line Notch Filter in Wearable Biomedical Devices for Wireless Body Area Network

Synthesis of Power Line Notch Filter in Wearable Biomedical Devices for Wireless Body Area Network

Erratum to "Single Wire Capacitive Wireless Power Transfer System for Wearable Biomedical Sensors based on Flexible Graphene Film Material".

In [1], this paper was submitted for the Special Issue on Flexible Biomedical Sensors for Healthcare Applications. The paper was instead published in Volume 16, Issue 6, 2022.

Revolutionizing Healthcare through Health Monitoring Applications with Wearable Biomedical Devices

The Internet of Things (IoT) has revolutionized the connectivity and communication of tangible objects, and it serves as a versatile and cost-effective solution in the healthcare sector, particularly in regions with limited healthcare infrastructure. This research explores the application of sensors such as LM35, AD8232, and MAX30100 for the detection of vital health indicators, including body temperature, pulse rate, electrocardiogram (ECG), and oxygen saturation levels, with data transmission through IoT cloud, offering real-time parameter access via an Android application for non-invasive remote patient monitoring. The study aims to expand healthcare services to various settings, such as hospitals, commercial areas, educational institutions, workplaces, and residential neighborhoods. After the COVID-19 pandemic, IoT-enabled continuous monitoring of critical health metrics such as temperature and pulse rate has become increasingly crucial for early illness detection and efficient communication with healthcare providers. Our low-cost wearable device, which includes ECG monitoring, aims to bridge the accessibility gap for people with limited financial resources, with the primary goal of providing efficient healthcare solutions to underserved rural areas while also contributing valuable data to future medical research. Our proposed system is a low-cost, high-efficiency solution that outperforms existing systems in healthcare data collection and patient monitoring. It improves access to vital health data and shows economic benefits, indicating a significant advancement in healthcare technology.

Adaptive Algorithm for Motion Artifacts Removal in Wearable Biomedical Sensors During Physical Exercise

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Motion artifacts (MA) are significant sources of noise in the wearable devices that are used to collect biological information from the human body. Photoplethysmography (PPG) is an example of the sensors embedded in many of these devices. PPG signals are highly influenced by MA, especially when the motion involves the organ that the PPG sensor is attached to. The aim of this paper is to build an adaptive algorithm and multiresolution analysis technique to denoise the raw PPG signals, suppress the MAs and accurately estimate the HR as an example of MA suppression in the wearable devices. The challenge here is to find the right selection considering high accuracy and low-computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Discrete wavelet transform recursive inverse (DWT-RI) adaptive filter algorithm in addition to multiresolution analysis is implemented to suppress MAs and estimate the HR using the simultaneously recorded accelerations in the three dimensions as reference signals for the adaptive algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The proposed algorithm has a low computational cost and results in an absolute average error of 1.17 beats per minute (BPM) when tested on 12 subjects from a well-known database for this purpose. The performance of the proposed algorithm is compared to the other existing methods in literature used for noise removal and motion artifact suppression. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DWT-RI adaptive algorithm can successfully suppress motion artifacts in PPG signals with a low computational complexity and outperforms several alternatives in terms of the average absolute error.

MULTIBAND FLEXIBLE ULTRA-WIDEBAND ANTENNA FOR WEARABLE ELECTRONICS AND BIOMEDICAL APPLICATIONS

This article proposes a flexible ultra-wideband (UWB) antenna for wearable electronics and biomedical applications. The antenna presented for this work is fabricated on a Kapton polyimide (PI) substrate, and the operating frequency ranges from 2.4 GHz to 7.1 GHz. The footprint of the proposed antenna is 35 &amp;times; 45 mm with an elliptical radiating element fed by coplanar waveguide (CPW), which achieves improved impedance matching characteristics. The choice of substrate, design constraints, fabrication process, and bending test of the proposed flexible UWB antenna are explained in detail in this paper. A comparative study on the choice of substrate is performed and the Kapton PI is chosen as the substrate material, which makes the antenna less susceptible to degradation in antenna performance that arises due to bending effects. The measurement and simulated results of the fabricated antenna show a good degree of agreement. The return loss performance of the proposed antenna is greater than -10 dB in all the operating bands with improved gain and directivity. The flexible nature with multiple frequency bands of operation and good bending performance makes the proposed antenna appropriate for wearable electronic and biomedical applications.

Metaverse-based Healthcare and Physiological Response Monitoring Systems, Deep and Machine Learning-based Medical Imaging Data, and Wearable Biomedical Sensors in Virtual Healthcare Environments

Metaverse-based Healthcare and Physiological Response Monitoring Systems, Deep and Machine Learning-based Medical Imaging Data, and Wearable Biomedical Sensors in Virtual Healthcare Environments

Graphene and Two-Dimensional Materials-Based Flexible Electronics for Wearable Biomedical Sensors

The use of graphene and two-dimensional materials for industrial, scientific, and medical applications has recently received an enormous amount of attention due to their exceptional physicochemical properties. There have been numerous efforts to incorporate these two-dimensional materials into advanced flexible electronics, especially aimed for wearable biomedical applications. Here, recent advances in two-dimensional materials-based flexible electronic sensors for wearable biomedical applications with regard to both materials and devices are presented.

Single Wire Capacitive Wireless Power Transfer System for Wearable Biomedical Sensors Based on Flexible Graphene Film Material.

This paper provides a special flexible graphene film based capacitive wireless power transfer (FGCPT) system for powering biomedical sensors of smart wearable devices. The graphene conductive material is flexible, transparent, highly conductive, and impermeable to most gases and liquids. Generally, the coupling structure of capacitive wireless power transfer (CPT) system is consisted of metal plates. However, it is hard to use for the biomedical sensors as the low power density and big volume. The shape of graphene conductive material could be easily built and changed according to the application requirements. In this paper, the power supply of biomedical sensing system could be accomplished by a single graphene film which is acted as the receiver of FGCPT system. The 200 mW power level is achieved with the maximum 9 V output voltage. The theory and calculation are verified by the simulated and experimental results.

Assessment of Compact Circular Microstrip Antenna using Taguchi’s Optimization for Wearable Biomedical Applications

This work presents a methodical and effective means of evaluate and choose design constraints using Taguchi’s method in order to design compact and high gain antenna. This method is applied for inset feed circular microstrip patch antenna with different geometries to tune the impedance to the desired value and the prototype is fabricated using the optimized results so as to validate the technique for wearable devices in biomedical applications with 2.45 GHz. The main contribution is towards miniaturization and design simplicity. It is observed that the optimized antenna design provides improved performance with an operational bandwidth of around 100 MHz and is compact in size measuring 45 x 40mm mm2 having a circular patch of radius 16.65 mm fabricated with economic and easily available substrate FR-4 with a 4.4 dielectric constant, 1.6 mm thickness, and a 0.02 loss tangent. Results from simulation and measurement exhibit good agreement and Taguchi’s method is found to be effective for antenna size reduction by 34.53% .

Tiny CNN for Seizure Prediction in Wearable Biomedical Devices.

Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations. In this work, a tiny one-dimensional stacked convolutional neural network (1DSCNN) is proposed based on short-time Fourier transform (STFT) to predict epileptic seizure. The results demonstrate that the proposed method obtains better performance compared to recent state-of-the-art methods, achieving an average sensitivity of 94.44%, average false prediction rate (FPR) of 0.011/h and average area under the curve (AUC) of 0.979 on the test set of the American Epilepsy Society Seizure Prediction Challenge dataset, while featuring a model size of only 21.32kB. Furthermore, after adapting the model to 4-bit quantization, its size is significantly decreased by 7.08x with only 0.51% AUC score precision loss, which shows excellent potential for hardware-friendly wearable implementation.

A Flexible Metamaterial Based Printed Antenna for Wearable Biomedical Applications.

This paper presents a microstrip antenna based on metamaterials (MTM). The proposed antenna showed several resonances around the BAN and ISM frequency bands. The antenna showed a suitable gain for short and medium wireless communication systems of about 1 dBi, 1.24 dBi, 1.48 dBi, 2.05 dBi, and 4.11 dBi at 403 MHz, 433 MH, 611 Mz, 912 MHz, and 2.45 GHz, respectively. The antenna was printed using silver nanoparticle ink on a polymer substrate. The antenna size was reduced to 20 × 10 mm2 to suit the different miniaturized wireless biomedical devices. The fabricated prototype was tested experimentally on the human body. The main novelty with this design is its ability to suppress the surface wave from the patch edges, significantly reducing the back radiation toward the human body when used close to it. The antenna was located on the human head to specify the specific absorption rate (SAR). It was found in all cases that the proposed antenna showed low SAR effects on the human body.

Structural Design Optimization of Micro-Thermoelectric Generator for Wearable Biomedical Devices

Wearable sensors to monitor vital health are becoming increasingly popular both in our daily lives and in medical diagnostics. The human body being a huge source of thermal energy makes it interesting to harvest this energy to power such wearables. Thermoelectric devices are capable of converting the abundantly available body heat into useful electrical energy using the Seebeck effect. However, high thermal resistance between the skin and the device leads to low-temperature gradients (2–10 K), making it difficult to generate useful power by this device. This study focuses on the design optimization of the micro-thermoelectric generator for such low-temperature applications and investigates the role of structural geometries in enhancing the overall power output. Electroplated p-type bismuth antimony telluride (BiSbTe) and n-type copper telluride (CuTe) materials’ properties are used in this study. All the simulations and design optimizations were completed following microfabrication constraints along with realistic temperature gradient scenarios. A series of structural optimizations were performed including the thermoelectric pillar geometries, interconnect contact material layers and fill factor of the overall device. The optimized structural design of the micro-thermoelectric device footprint of 4.5 × 3.5 mm2, with 240 thermoelectric leg pairs, showcased a maximum power output of 0.796 mW and 3.18 mW when subjected to the low-temperature gradient of 5 K and 10 K, respectively. These output power values have high potential to pave the way of realizing future wearable devices.

Assessment of Denim and Photo Paper Substrate-Based Microstrip Antennas for Wearable Biomedical Sensing

The medical field has witnessed an exponential growth of wearable devices mainly due to the advancement in wireless communication and antenna technology. There is a demand for wearable antennas, which are lightweight, flexible, and ease of integration into the fabric which suits on-body applications. Therefore, this work presents the design, fabrication, and analysis of two microstrip antennas using denim cloth and stacked photo paper as substrate material. The novelty of these antennas is the use of silver fabric as ground and radiating patch-conducting layer, which has resulted in significant improvement in overall antenna performance. It is observed that our fabricated antennas have exhibited a gain of 8.71 dB with VSWR of 1.32 for denim and gain of 2.45 dB with VSWR of 1.03 for photo paper substrates respectively.

Evaluation of NFC-Enabled Devices for Heterogeneous Wearable Biomedical Application

Biomedical systems that aim at monitoring parameters over a long period of time will require non-invasive wearable devices. A wide range of applications, including continuous workers monitoring or daily monitoring of patients, is expected to exploit wearable devices, each one characterized by different requirements. Such devices will adopt a design that minimizes discomfort and does not limit users' mobility to allow round-the-clock wearability. In order to ensure a significant reduction in the size of wearable devices, the next generation of this technology will need no batteries or at least very small ones. In this paper we analyze the use of Near Field Communication (NFC) for ultra-low-power communication in wearable devices for biomedical applications. Our goal is to verify whether NFC technology can support the heterogeneous requirements of different biomedical use-cases. For this purpose, we will first provide a review of the current state of the art in NFC-Enabled solutions. Then, we will evaluate NFC capabilities through a set of experiments, using off-the-shelf devices. We will pose particular focus on the energy harvesting capabilities to evaluate the feasibility of designing battery-less devices. Our results show that NFC adoption in different biomedical applications is possible, as they can ensure proper reading frequency and distance. Finally, we further demonstrate this through a proof-of-concept implementation: an NFC-based sensorized glove for work safety that is able to monitor the external temperature in a continuous manner.

A 2.55 NEF 76 dB CMRR DC-Coupled Fully Differential Difference Amplifier Based Analog Front End for Wearable Biomedical Sensors.

High input impedance, low noise, high common mode rejection ratio (CMRR), and ultralow power are the most important performance indicators in the design of analog front end (AFE) for wearable biomedical sensors. This paper presents a fully differential difference amplifier based AFE that employs dc-coupled input stage to increase the input impedance and improve CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body dc bias scheme is introduced to deal with the input dc offset. Implemented in 0.35μm CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9μW at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtain the distinct α-wave after eye blink from EEG recording.

Wearable Biomedical Devices: State of the Art, Challenges, and Future Perspectives

Wearable Biomedical Devices: State of the Art, Challenges, and Future Perspectives