Design and Fabrication of a New Wearable Pressure Sensor for Blood Pressure Monitoring

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Recent Development of Mechanical Stimuli Detectable Sensors, Their Future, and Challenges: A Review

By virtue of their wide applications in transportation, healthcare, smart home, and security, development of sensors detecting mechanical stimuli, which are many force types (pressure, shear, bending, tensile, and flexure) is an attractive research direction for promoting the advancement of science and technology. Sensing capabilities of various force types based on structural design, which combine unique structure and materials, have emerged as a highly promising field due to their various industrial applications in wearable devices, artificial skin, and Internet of Things (IoT). In this review, we focus on various sensors detecting one or two mechanical stimuli and their structure, materials, and applications. In addition, for multiforce sensing, sensing mechanism are discussed regarding responses in external stimuli such as piezoresistive, piezoelectric, and capacitance phenomena. Lastly, the prospects and challenges of sensors for multiforce sensing are discussed and summarized, along with research that has emerged.

Diamond-like Carbon Coatings in the Biomedical Field: Properties, Applications and Future Development

Repairment and replacement of organs and tissues are part of the history of struggle against human diseases, in addition to the research and development (R&D) of drugs. Acquisition and processing of specific substances and physiological signals are very important to understand the effects of pathology and treatment. These depend on the available biomedical materials. The family of diamond-like carbon coatings (DLCs) has been extensively applied in many industrial fields. DLCs have also been demonstrated to be biocompatible, both in vivo and in vitro. In many cases, the performance of biomedical devices can be effectively enhanced by coating them with DLCs, such as vascular stents, prosthetic heart valves and surgical instruments. However, the feasibility of the application of DLC in biomedicine remains under discussion. This review introduces the current state of research and application of DLCs in biomedical devices, their potential application in biosensors and urgent problems to be solved. It will be useful to build a bridge between DLC R&D workers and biomedical workers in order to develop high-performance DLC films/coatings, promote their practical use and develop their potential applications in the biomedical field.

Cuffless blood pressure measuring devices: review and statement by the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability

BACKGROUND

Many cuffless blood pressure (BP) measuring devices are currently on the market claiming that they provide accurate BP measurements. These technologies have considerable potential to improve the awareness, treatment, and management of hypertension. However, recent guidelines by the European Society of Hypertension do not recommend cuffless devices for the diagnosis and management of hypertension.

OBJECTIVE

This statement by the European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability presents the types of cuffless BP technologies, issues in their validation, and recommendations for clinical practice.

STATEMENTS

Cuffless BP monitors constitute a wide and heterogeneous group of novel technologies and devices with different intended uses. Cuffless BP devices have specific accuracy issues, which render the established validation protocols for cuff BP devices inadequate for their validation. In 2014, the Institute of Electrical and Electronics Engineers published a standard for the validation of cuffless BP devices, and the International Organization for Standardization is currently developing another standard. The validation of cuffless devices should address issues related to the need of individual cuff calibration, the stability of measurements post calibration, the ability to track BP changes, and the implementation of machine learning technology. Clinical field investigations may also be considered and issues regarding the clinical implementation of cuffless BP readings should be investigated.

CONCLUSION

Cuffless BP devices have considerable potential for changing the diagnosis and management of hypertension. However, fundamental questions regarding their accuracy, performance, and implementation need to be carefully addressed before they can be recommended for clinical use.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers’ physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers’ physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers’ PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO₂. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers’ physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys’ physical fitness prediction, and 99.26% for girls’ physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers’ physical fitness levels by their running PPG recordings.

Development of a Smart Clinical Bluetooth Thermometer Based on an Improved Low-Power Resistive Transducer Circuit

Smart sensors have been used in many engineering monitoring and control applications. This work focuses on the development of a new type of clinical Bluetooth thermometer, based on an improved low-power resistive transducer circuit. Most existing resistive transducers use relatively complicated circuits with higher cost and power consumption. To tackle these problems, especially in real applications, an improved low-power resistive transducer circuit is proposed in this work and is used to develop smart Bluetooth thermometers. The parameters of the resistive transducer circuit are selected by quantitative analysis and optimization to improve the performance of the low-power resistive transducer circuit. The effectiveness of the proposed design technology was verified by tests. The temperature measurement error of the new smart Bluetooth thermometer is less than 0.1 °C, which can not only meet the clinical use requirements but also has lower cost and power consumption.

Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.