Wearable Biomedical Sensors Research Articles

Janus Membrane-Based Wearable pH Sensor with Sweat Absorption, Gas Permeability, and Self-Adhesiveness.

Wearable biomedical sensors have enabled noninvasive and continuous physiological monitoring for daily health management and early detection of chronic diseases. Among biomedical sensors, wearable pH sensors attracted significant interest, as pH influences most biological reactions. However, conformable pH sensors that have sweat absorption ability, are self-adhesive to the skin, and are gas permeable remain largely unexplored. In this study, we present a pioneering approach to this problem by developing a Janus membrane-based pH sensor with self-adhesiveness on the skin. The sensor is composed of a hydrophobic polyurethane-polydimethylsiloxane porous hundreds nanometer-thick substrate and a hydrophilic poly(vinyl alcohol)-poly(acrylic acid) porous nanofiber layer. This Janus membrane exhibits a thickness of around 10 μm, providing a conformable adhesion to the skin. The simultaneous realization of solution absorption, gas permeability, and self-adhesiveness makes it suitable for long-term continuous monitoring without compromising the comfort of the wearer. The pH sensor was tested successfully for continuous monitoring for 7.5 h, demonstrating its potential for stable analysis of skin health conditions. The Janus membrane-based pH sensor holds significant promise for comprehensive skin health monitoring and wearable biomedical applications.

3D printed electret as self-powered wearable sensor and futuristic implementation for on-spot bone injury

The importance of wearable pressure sensors extends to the progress of modern society and personal healthcare. The utility of currently available self-powered sensors is restricted in complex areas due to their limited geometrical configurations. In this study, an electret composed of polylactic acid (PLA) has been developed by 3D printing technology, where the pressure sensors with the desired complicated structure are possible to design. We demonstrate that the 3D-printed electret-based pressure sensor (3D-EPS) exhibits outstanding sensitivity (∼178 mV/kPa) with ultrafast response time (6 ms). It enables the capture of the biomechanical signals from different parts of bone-joint motions, which could be utterly suitable for monitoring injury recovery. We show that if the electret structure is printed as a shoe sole shape, then it generates 65 V of open circuit voltage and 12 μA of short circuit current during the human running action, indicating to use as an effective wearable energy harvester. It is also capable of detecting different motion and foot pressure distributions, which makes it a potential candidate as a wearable biomedical sensor. To demonstrate its futuristic potential applications in patient-specific bone damage recovery monitoring, the feasibilities of electrets with commonly desired structures have been shown to print.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Surface potential modulation for improved mechanical energy harvesting and sensing in 3D printed biopolymer thermoelectret

This study presents the development of a special class of electret known as thermoelectret, made with polylactic acid (PLA), prepared via 3D printing technology followed by a heat-assisted corona discharged technique. We show that the bimodal surface polarity (i.e., ± 1 kV) is possible to achieve over the top and bottom surfaces of 3D printed PLA thermoelectret that enable a wide range of potential applications, such as mechanical energy harvesting to wearable biomedical sensors. Besides this, the enhanced surface potential leads to a giant longitudinal piezoelectric coefficient (d33) of 350 ± 15 pC/N, which is typically 10 times higher than the best-known piezoelectric polymer, polyvinylidene fluoride-trifluoro ethylene (PVDF-TrFE). To utilize this benefit, a 3D-printed thermoelectret-based mechanical energy harvester (3D-TEH) has been developed that delivers an outstanding power density of ⁓ 25 mW/m2, which makes it a suitable candidate for the self-power pressure sensor. As a proof of concept, we have demonstrated that 3D-TEH is capable of recording the bone-joints motions, coughing action, and human foot-pressure distribution that signify its potential application as a self-powered bio-medical sensor.

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.

Optimization of Microstructure Patterning for Flexible Bioelectronics Application

Recent advancements in flexible electronics and wearable sensors have given biomedical technology a new edge overcoming the limitations of traditional rigid silicon-based electronics. Furthermore, high flexibility of these wearable sensors enables it to conformally sit over any uneven surface helping in accurate determination of any physical, chemical, or physiological parameter associate with the surface. Conventionally expensive micro/nano photolithography techniques under strict clean room conditions are used for the development of these flexible and wearable biomedical sensors with high degree of accuracy and sensitivity. However, the developed wearable sensors need not only be extremely sensitive, but also cost effective for its successful usage. To address this, the present work discusses the use of a photo-patternable UV sheet for realization of micro patterns over flexible copper cladded surface eliminating the need of costly clean room facilities. It demonstrates the standardization of various design geometries using the photo-patternable UV sheet over the flexible surface similar to photolithography process and involves optimization of the exposure timing of the UV sheets and their development time towards various design patterns over different thick film metal surfaces. Finally, patterned micro devices like micro-electrodes were successfully realized using the above process to ascertain its efficacy.

A wearable inertial system to evaluate Tai Chi training motor effects in patients with Parkinson's disease.

Physical therapy is strongly recommended for patients with neurological disorders. Tai Chi-based treatments seem to improve physical functions like gait speed and balance. However, assessments after treatment rely on semi-quantitative clinical scales affected by subjectivity with controversial results. This study aims at investigating whether Tai Chi could be a valid alternative to traditional physiotherapy rehabilitation. We propose a wearable system composed of two inertial devices able to objectively measure the effect of the rehabilitation treatment on the range of movement of the trunk. Seventeen patients with Parkinson's Disease (PD) were recruited and assessed. They have been randomly divided into two groups: group 1 followed a Tai Chi-based treatment, while group 2 underwent a traditional physiotherapy rehabilitation. The two groups have been assessed before (t0) and after the treatment (t1). No statistical differences have been found in the relative range of motion between the upper and lower sensors between the two groups at the baseline. Both treatments resulted in a significant improvement in the trunk range of movement (on the right side). Notably, the improvement in the effect size of the treatment was greater in group 1 than in group 2. In fact, even if both the groups benefited from their treatment group 1 gained larger mobility of the trunk if compared to group 2. Interestingly, no differences have been accounted adopting the traditional UPDRS III for motor symptoms of PD, strengthening the idea that objective measurement coming from wearable biomedical sensors could detect information otherwise neglected by traditional clinical tools.Clinical Relevance- This study preliminary confirms that beneficial motor effects after a Tai Chi rehabilitation program are comparable and quite better than after traditional physiotherapy, promoting Tai Chi as a valid alternative treatment for PD patients.

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.

Internet of (Healthcare) Things Based Monitoring for COVID-19+ Quarantine/ Isolation Subjects Using Biomedical Sensors, A Lesson from the Recent Pandemic, and an Approach to the Future.

The COVID-19+ pandemic has brought into keen focus the necessity to utilize and enforce our digital infrastructure for remote patient monitoring based on IoT (Internet of Things) technology since quarantines and isolations are playing a vital role in containing its spread. As of date, many viral tests and vaccines are in use while few drugs are in experimental stages, but there is always need for possibilities for increasing reliability of disease detection and monitoring at both levels of individual and society, and such aim can be supported by wearable biomedical sensors devices. Previously, wearable devices have been used to monitor physiological parameters during daily human living activities. Still, the investment of such technologies toward predicting infection by COVID-19+ remains essential to alert potential patients and start sequence of health systems intervention. It was found that wearable devices increased patients’ compliance to healthcare advice. Thus, in this perspective review, we have proposed an IoT based system to monitor the quarantine / isolation subjects during COVID-19+ and similar pandemic and quarantine observation. This wearable prototype, associated with the bundled mobile app, act to reports and tracks/monitoring the quarantined individuals. IoT based quarantine/isolation monitoring system is contact-free that could benefit especially healthcare professionals to lower the risk of exposure to infective pathogens. Current manuscript describes clinically relevant physiological human parameters that can be measured by wearable biomedical sensors and monitored based on IoT technology and their role in health tracking, stability, and recovery of COVID-19++ve individuals and front-line health workers. This paper aimed at initiation of an approach among front-line healthcare workers as well as biomedical engineers for developing digital healthcare platforms of monitoring and managing such pandemic.

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.

A Review on Epidermal Nanogenerators: Recent Progress of the Future Self‐Powered Skins

Epidermal nanogenerators (ENGs), defined as nano‐enabled, thin, soft, and flexible skin‐like devices that can convert mechanical and thermal energy into electricity, are emerging as a promising self‐powered technology for wearable biomedical devices. This review covers the discussion of working mechanisms, design strategy, and major requirements of the state‐of‐the‐art ENGs including piezoelectric, triboelectric, pyroelectric, and hybrid. In addition, their representative applications in human–machine interface (HMI), wound healing, and wearable biomedical sensors are described. In particular, a focus has been on the narration of self‐powered ENG‐based biosensors for detecting physical, biochemical, and physiological signals. Despite the encouraging advances over the past decade, the field of ENGs remains in the early stage of development with limited real‐world adoption. There remain a number of challenges such as insufficient energy and power density for powering wearable devices, poor air permeability, limited washability, and durability under severe conditions. The future opportunity lies at the development of better materials and/or designs to tackle these challenges to generate real‐world impact.

Effects of Touchscreen Media Use on Toddlers' Sleep: Insights from Longtime ECG Monitoring.

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable device. The study further reports the application of this technique to the investigation of potential impacts of early touchscreen media use. Toddlers’ touchscreen media use is of strong interest for parents, educators, and researchers. Mostly, negative effects of media use are assumed, among them, disturbances of sleep and impairments of learning and development. In 55 toddlers (32 girls, 23 boys; 27.4 ± 4.9 months; range: 16–37 months), ECG monitoring was conducted for a period of 30 (±3) h. Parents were asked about their children’s touchscreen media use and they rated their children’s sleep quality. The use of touchscreen media predicted the physiologically determined quality of sleep but not parent-reported sleep quality (such as sleep onset latency). Greater heart rate differences between restless sleep phases and restful sleep indicated poorer nighttime recovery in children with more frequent use of touchscreen media. The study demonstrates that the expert analysis of the ECG during sleep is a potent tool for the estimation of sleep quality in toddlers.

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.

Development of Nanosensors Based Intelligent Packaging Systems: Food Quality and Medicine.

The issue of medication noncompliance has resulted in major risks to public safety and financial loss. The new omnipresent medicine enabled by the Internet of things offers fascinating new possibilities. Additionally, an in-home healthcare station (IHHS), it is necessary to meet the rapidly increasing need for routine nursing and on-site diagnosis and prognosis. This article proposes a universal and preventive strategy to drug management based on intelligent and interactive packaging (I2Pack) and IMedBox. The controlled delamination material (CDM) seals and regulates wireless technologies in novel medicine packaging. As such, wearable biomedical sensors may capture a variety of crucial parameters via wireless communication. On-site treatment and prediction of these critical factors are made possible by high-performance architecture. The user interface is also highlighted to make surgery easier for the elderly, disabled, and patients. Land testing incorporates and validates an approach for prototyping I2Pack and iMedBox. Additionally, sustainability, increased product safety, and quality standards are crucial throughout the life sciences. To achieve these standards, intelligent packaging is also used in the food and pharmaceutical industries. These technologies will continuously monitor the quality of a product and communicate with the user. Data carriers, indications, and sensors are the three most important groups. They are not widely used at the moment, although their potential is well understood. Intelligent packaging should be used in these sectors and the functionality of the systems and the values presented in this analysis.

Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

AbstractTo provide multiple options for specific application in biosignal processing, the programmable Gaussian‐derived Gm‐C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow‐the‐leader feedback Gm‐C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh‐order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a = 0.1, with dynamic range of 42.6 dB and figure‐of‐merit of 2.05 × 10−13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.

Health Monitoring System for Elderly Patients Using Intelligent Task Mapping Mechanism in Closed Loop Healthcare Environment

The ageing population’s problems directly impact countries’ socio-economic structure, as more resources are required to monitor the aged population’s health. The growth in human life expectancy is increasing due to medical technologies and nutritional science innovations. The Internet of Things (IoT) is the connectivity of physical objects called things to the Internet. IoT has a wide range of health monitoring applications based on biomedical sensing devices to monitor health conditions. This paper proposes elderly patients’ health monitoring architecture based on an intelligent task mapping approach for a closed-loop IoT healthcare environment. As a case study, a health monitoring system was developed based on the proposed architecture for elderly patients’ health monitoring in the home, ambulance, and hospital environment. The system detects and notifies deteriorating conditions to the authorities based on biomedical sensors for faster interventions. Wearable biomedical sensors are used for monitoring body temperature, heart rate, blood glucose level, and patient body position. Threshold and machine learning-based approaches were used to detect anomalies in the health sensing data. The proposed architecture’s performance analysis is evaluated in terms of round trip time, reliability, task drop rate, and latency performance metrics. Performance results show that the proposed architecture of the elderly patient health monitoring can provide reliable solutions for critical tasks in IoT environments.

Planar Heterojunction Organic Photodetectors Based on Fullerene and Non-fullerene Acceptor Bilayers for a Tunable Spectral Response.

Planar heterojunction (PHJ) organic photodetectors are potentially more stable than traditional bulk heterojunction counterparts because of the absence of uncontrolled phase separation in the donor and acceptor binary blend system. This work reports a new class of PHJ organic photodetectors based on the medium-band gap fullerene C60 and low-band gap fused-ring non-fullerene acceptor ID-MeIC bilayer structure, which allows a wide range of spectral response tuning across the UV-visible-near-infrared (UV-vis-NIR) region by tailoring individual layer thickness. The C60 layer not only increases the external quantum efficiency at 745 nm by 57% but also reduces dark currents by two orders of magnitude. More importantly, the p-type poly[N,N'-bis(4-butylphenyl)-N,N'-bis(phenyl)-benzi] is found to be the key compound to conduct the layer-by-layer fabrication as combined with n-type ID-MeIC for higher charge extraction efficiency. In light of the above information, PHJ organic photodetectors exhibited a specific detectivity of 6.5 × 1010 Jones to detect NIR light at 745 nm under -0.1 V. The linear dynamic range was estimated to be 80 dB. This work has demonstrated a feasible approach to develop a PHJ architecture with tunable spectral response in the UV-vis-NIR range toward long-term stable organic photodetectors for potential applications in flexible and wearable biomedical sensors.

A new approach for an ultra-thin piezoresistive sensor based on solidified carbon ink film

Conventional flexible piezoresistive strain sensors that use conductive particles polymer composites exhibit thick structures with a low sensitivity to external tension. This paper presents a cost-effective method to fabricate ultra-thin and highly sensitive piezoresistive strain sensors. In our fabrication steps, carbon ink that is mainly composed of carbon black particles is solidified with a drying process to form a “paperlike,” flexible conductive film. Without any surface modification techniques, the carbon ink film is directly placed onto liquid-state PDMS and then bonded after the drying process. Following the rapid prototyping, different performance metrics of the fabricated sensors, including piezoresistivity, gauge factor, temperature dependency, elastic modulus, and repeatability are measured. Specifically, sensors fabricated with this method show a significantly improved gauge factor (~26) compared to similar flexible sensors fabricated by more complicated micro-fabrication methods. The proposed method of fabrication and the corresponding ultra-thin (~45 μm) sensor prototype may benefit the design and mass production of future wearable biomedical and healthcare sensors.