Portable Medical Devices Research Articles

Empowering Healthcare: TinyML for Precise Lung Disease Classification

Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients.

Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. The great majority of algorithms can only distinguish “AF rhythm in AF patients” from “sinus rhythm in healthy individuals” . However, AF patients do not always exhibit AF rhythm, most of the time they present with sinus rhythms, and there is also a potential risk of AF in sinus rhythms. How to detect AF from sinus rhythm is a challenge. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish “sinus rhythm in AF patients” and “sinus rhythm in healthy individuals” in beat-level. We cut 1 s of sinus beats from single-lead Electrocardiogram (ECG) data and fed them to the Net1d model, a deep learning model that processes one-dimensional data, to obtain the risk probability of each beat. Besides, we have also introduced the beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314, with an average accuracy of 0.6606 and an F1 score of 0.6470. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591, while the average accuracy and F1 score improve to 0.6887 and 0.6749. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels. Our code is publicly available at https://github.com/leijsen/ECGBeat4AFSinus.

A novel metamaterial superstrate array integrated flexible UWB antenna with improved performance for applications in portable medical devices

A novel metamaterial superstrate array integrated flexible UWB antenna with improved performance for applications in portable medical devices

Application of NGO-BP Neural Network in Battery Life Prediction of Portable Medical Devices

The development of portable medical devices cannot be separated from safe and efficient batteries. Accurately predicting the remaining life of batteries can greatly improve the reliability of batteries, which is of great significance for portable medical devices. This article focuses on the high dependence of the BP neural network algorithm on initial weights and thresholds, as well as its tendency to fall into local minima. The Northern Goshawk Optimization (NGO) algorithm is used to optimize the BP neural network and to test the 18650 lithium battery data under different ambient temperatures (4, 24, 43°C) typical of medical equipment. The experimental results show that the NGO algorithm can significantly improve the prediction accuracy of the BP neural network under various temperature conditions, achieving accurate and effective prediction of the remaining battery life.

A novel method for chronic obstructive pulmonary disease detection using comprehensive respiratory impedance and electrocardiogram-derived respiration

Currently, pulmonary function test is the only standard method for diagnosing COPD, but it often involves high testing costs, poor compliance, and a high dependence on patient cooperation. Other new detection methods, such as CT and FOT, also have disadvantages such as radiation exposure and high usage costs. Existing non-invasive COPD detection methods consider the impact of COPD on the lungs or heart, thus they can only roughly detect COPD in individuals, without the ability to grade COPD. Therefore, this study proposed a method for COPD detection using comprehensive respiratory impedance (CRI) and electrocardio-derived respiration (EDR) signals. The method measured both respiratory impedance and ECG synchronously, and the CRI and EDR signals were obtained through an algorithm. The effects of COPD on respiratory pattern were studied by taking CRI and EDR signal morphology as objects, from which characteristic parameters were extracted and input into the supervised classifier to fuse the information of COPD on the changes of respiratory signal morphology pattern in the heart and lungs. The final result is accurate detection of normal, mild COPD and moderate COPD. The experimental results of 46 subjects showed that the accuracy, sensitivity and specificity of CRI and EDR signal characteristics for COPD detection by different classifiers were up to 93.5 %, 90.5 % and 96.5 %, and the AUC values of different classifiers were all > 0.88. Therefore, the method can not only effectively distinguish COPD patients from healthy people, but also provide differentiated detection capabilities for different COPD clinical grades, providing strong support for the early diagnosis and treatment of COPD, and can be applied to the development of portable medical devices for the detection of COPD.

Paper Battery: The Future and Solution for Traditional Battery

Today is the biggest problems are faced by the electronics industry size of batteries gadgets ultra-thin and small day by day.Paper batteries will the replace the li-ion battery and paper battery. Paper-based batteries have emerged as a promising solution for sustainable energy storage owing to their eco-friendly nature, lightweight, flexibility, and cost-effectiveness. This paper presents a comprehensive overview of recent advancements in the field of paper-based batteries, focusing on their fabrication techniques, materials selection, and performance optimization strategies. Various types of paper substrates, conductive materials, and electrolytes employed in paper-based batteries are discussed, along with their impact on the overall performance and environmental sustainability. Additionally, this paper highlights the potential applications of paper-based batteries in portable electronics, medical devices, and wearable technologies, emphasizing their role in promoting the development of green energy storage solutions. Finally, challenges and future directions for the research and development of paper-based batteries are outlined, aiming to inspire further innovation and integration of these eco-friendly energy storage devices into real-world applications. This biodegrable and CNT. Electronics device are classified into two types primary and secondary. The paper are shown in SWCNT and MWCNT are the contain in this type. Title: Advancements in Paper-Based Batteries: A Sustainable Approach towards Energy Storage Keywords: CNT (carbon Nano tube), SWCNT (singled walled carbon Nano tube), MWCNT (multi walled carbon Nano tube, Paper Batteries), Flexibility.

Degradable piezoelectric biomaterials for medical applications

The energy harvesting technology based on piezoelectricity promises to achieve a self-powered mode for portable medical electronic devices. Piezoelectric materials, as crucial components in electromechanical applications, have extensively been utilized in portable medical electronic devices. Especially, degradable piezoelectric biomaterials have received much attention in the medical field due to their excellent biocompatibility and biosafety. This mini-review mainly summarizes the types and structural characteristics of degradable piezoelectric biomaterials from degradable piezoelectric small-molecule crystals to piezoelectric polymers. Afterward, medical applications are briefly introduced, including energy harvester and sensor, actuator and transducer, and tissue engineering scaffold. Finally, from a material perspective, some challenges currently faced by degradable piezoelectric biomaterials are proposed.

Impact of medical innovations on quality of care in low income settings

Access to high-quality healthcare remains a challenge in low-income settings, primarily due to resource constraints and limited access to medical innovations. This systematic review examines the impact of medical innovations on the quality of care in low-income settings. Through a comprehensive analysis of relevant literature, this paper highlights the various medical innovations that have been implemented in low-income settings and evaluates their effectiveness in improving healthcare outcomes and patient satisfaction. The review identifies three broad categories of medical innovations: technological innovations, innovative healthcare delivery models, and novel treatment modalities. Technological innovations encompass a wide range of advancements, including telemedicine, mobile health (mHealth) applications, and point-of-care diagnostics. These innovations have shown promise in improving access to healthcare services, enhancing diagnostic accuracy, and facilitating remote monitoring and follow-up care in low-resource settings. Innovative healthcare delivery models, such as community health worker programs, task shifting, and mobile clinics, have also been implemented to overcome barriers to healthcare access in low-income settings. These models prioritize community engagement, decentralization of services, and task delegation to non-physician healthcare providers to expand coverage and reach underserved populations. Furthermore, novel treatment modalities, including affordable and portable medical devices, point-of-care testing kits, and low-cost medications, have been developed to address the specific healthcare needs of low-income populations. These innovations aim to improve treatment efficacy, reduce treatment costs, and minimize the burden of disease in resource-limited settings. The review synthesizes evidence from a diverse range of studies to assess the impact of these medical innovations on healthcare outcomes and patient satisfaction. Key findings suggest that while medical innovations hold promise in enhancing quality of care, several challenges including limited resources, infrastructure constraints, and training requirements need to be addressed to maximize their impact. The paper concludes by emphasizing the importance of tailored approaches and sustainable strategies for implementing medical innovations in low-income settings. By fostering partnerships, leveraging existing infrastructure, and investing in capacity building initiatives, stakeholders can effectively integrate medical innovations into healthcare delivery systems and improve access to high-quality care for underserved populations. This systematic review contributes to the growing body of literature on healthcare innovation and provides valuable insights for policymakers, healthcare providers, and researchers seeking to address the healthcare needs of low-income populations and achieve health equity on a global scale.

Design and implementation of a hybrid piezoelectric, radio frequency, solar, and thermal energy‐harvesting system for portable medical devices

AbstractToday, energy harvesting has developed into a technique from which various aspects are still hidden. By extracting energies from the environment and even the human body, and converting them into electrical energy, we can supply power to low‐consumption electronic devices. Energy harvesting works by inhibiting small amounts of ambient energies; otherwise, it is wasted in other forms such as heat, vibration, and light. In this paper, we aim to eliminate or minimize the dependence of portable electronic devices to the batteries, which have limited life times, service costs, handling, and environmental problems. This is accomplished via incorporating the hybrid energy‐harvesting techniques and designed to power (bio)medical devices in order to measure vital signs. In this circuit, by simultaneously harvesting RF, light, thermal, and solar (natural and artificial) energies, we were able to increase the reliability of continuous supply of electrical energy in energy‐harvesting systems by generating and storing maximum power from the environment. Supercapacitors are used as the energy storage elements so that there is no energy waste, and each system is constantly storing the electrical energy with the aid of a diode block. In addition, with the help of supercapacitors, this device is able to transmit a maximum of 420 mW to the load and the total efficiency is equal to 74%.

Blockchain and machine learning in the internet of things: a review of smart healthcare

<span lang="EN-US">The healthcare sector has benefited from digital transformation and modern technology. As well is expected to rely even more on the internet of things (IoT) technologies in the near future. Due to the availability of portable medical devices, applications, and mobile health services, all of which have contributed to the development of innovative features for the delivery of healthcare services. With the large number of data issued from the IoT and the importance of using data to benefit from contained in diagnosing diseases, medical records, or monitoring. Furthermore, the expansion of emerging technologies such as robots and machine learning (ML) is supported by the ease with exchanged and shared medical information. Moreover, Blockchain technology enables the creation of secure records for storing medical data in a safe and timely manner. The paper reviews various IoT, Blockchain, and ML applications and systems in the smart healthcare sector to discover many challenges, consequently, it will be easy for researchers who have an interest in these fields to find today and future solutions. This, in turn, will help to enhance the technical services depending on the IoT in ML and Blockchain in the smart healthcare field.</span>

A Lightweight and Low-Voltage-Operating Linear Actuator Based on the Electroactive Polymer Polypyrrole.

In recent decades, significant research efforts have been devoted to studying various types of actuators. Of particular interest are soft actuators based on electroactive polymers, which offer low operating voltage, light weight, and fast response. In this study, we demonstrate the feasibility of a soft linear actuator fabricated from polypyrrole (PPy), an electroactive polymer that is easy to synthesize, cost-effective, and biocompatible. By optimizing the polymerization conditions, the operation condition and environment, we were able to achieve improved and stable actuator performance. Furthermore, we developed a new actuator-contained component with a flexible counter electrode to build an actuator that operates in air. This approach enabled us to build small and lightweight actuators that operate in air, with a diameter of 5 mm, resembling artificial muscles. Our resulting miniaturized and integrated linear PPy-based actuators can be driven at low voltages (±1.5 V), making them suitable for use in various parts of the body. As such, this actuator holds significant potential for a wide range of applications in the fields of soft electronics, drug delivery, artificial organs, and muscles, as well as a component material for portable medical sensors and devices.

Performance of single-layer paper-based co-laminar flow microbial fuel cells

This paper presents a novel design and operation of microbial fuel cells (ΜFCs), which contain monolayer paper-based substrate/electrodes and microchannels with co-laminar flow. The electrodes with multi-wall carbon nanotubes are fabricated by the screen-printing method and the microchannels are patterned using photo-lithography. A double-inlet and diverging channel design is incorporated in the fuel cell configuration and demonstrated significantly improved performance. The fluid flows of electrolytes through the porous paper media are simulated using steady-state and transient computational fluid dynamics The best performance is achieved under the following conditions: an electroactive microbial (S. oneidensis) concentration of OD6001.5, 50 mM electron donor (lactate), and direct immobilized of the inoculum on the anode surface. The developed MFCs achieves a peak power density of 19.4 ± 0.23 μW cm−2 and maximum current density of 190.4 ± 1.39 μA cm−2, surpassing the performance of all previously reported paper-based single MFCs that utilize paper-based electrodes. In addition, hybrid-type MFCs that containing enzymatic air-breathing cathodes are investigated to enhance their performance. The novel paper based self-pumping MFC has the potential to make lab-on-a-chip type portable medical diagnosis devices with integrated power sources practical and feasible.

Improved reliability single loop single feed 7T SRAM cell for biomedical applications

Portable biomedical devices are born to reach a maximum number of people at an effective cost, and because of their small size and battery operation, the impact of portable medical devices is huge. For biomedical image processing devices, it is very important to store pixel information in embedded memory, because pixel values contain critical information about the image. For this critical information storage, most embedded memories consist of static random access memory (SRAM). SRAM, which stores critical information must have a high level of stability and reliability with low power dissipation. This paper proposes a single loop single-feed 7T (SLSF7T) SRAM cell that operates in the sub-threshold region (reducing the supply voltage to reduce power dissipation) and attains a high read static margin. To evaluate the read-and-write stability of the SRAM cell, the N-curve method is adopted in this work.The proposed SLSF7T SRAM cell design offers several improvements over existing Biomedical Transmission Gate 8T (BT8T) and 9T SRAM cells. Specifically, the SLSF7T SRAM cell design shows an increase in static voltage noise margin (SVNM) by 75.86% and 75.34%, reduction in delay by 37.86% and 58.52%, and also offers less leakage power dissipation by 72% and 23.29% as compared to the BT8T and 9T cells, respectively. Along with the low power and high stability, the other most significant feature of the proposed work is its area efficiency because the proposed memory cell only consists of 7 transistors, it requires only 1.1X area overhead compared to the conventional 6T memory cell. The calculated performance matrix of the proposed cell is the highest among the considered SRAM cells for compression. The proposed cell operates in the sub-threshold region and achieves the best performance parameters for memory design for biomedical devices and applications at a 300 mV supply voltage.

Time Series Classification for Portable Medical Devices

INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module; (2) Training module; (3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% and 99.32% respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.

A Smart Ambulance With Information System and Decision-Making Process for Enhancing Rescue Efficiency

The system presented in this article is part of a research project to enhance the rescue efficiency of an ambulance or a rescue car using information technology and the decision-making process. It aims to support emergency medical services (EMSs) by improving the communication between staff at the incident location and competence centers such as hospitals. The increasing number of missions, especially, car accidents, in Thailand has created huge problems for the public health care system. Higher cost efficiency and treatment quality could be achieved by sharing both tactical and medical data. Medically relevant data, such as medical signals, auscultation, and video material, are transferred from the emergency site to the competence center via the telematic support system. Also, traveling distance relative to traffic management can improve patient survival during transfer using global positioning system (GPS) tracking. In this article, the tracking of incident locations and the monitoring of the patient’s biomedical signals are achieved using portable GPS and medical devices. Smart glasses are employed in the video conferencing system. Application software, including the decision tree algorithm, has been developed to be used with cloud computing to manage video material, GPS, medical devices, and decision making for transferring a patient in this prototype system. The telematic support system in this research project can utilize both Wi-Fi and cell phone systems. The crucial requirements and the proposed hardware and software system architecture of the system for EMS are summarized.

Preliminary Usability Evaluation of Personal Medical Devices towards Better Home Healthcare

Portable medical devices have been playing an important role in improving health outcomes across the socio-economic spectrum. In the recent decades, substantial population have started using such devices to monitor important vital signs. Significant proportion of these users are older patients with chronic diseases, declined physical and/or cognitive capabilities. Furthermore, these portable devices are often used in unguided environments, that can present unpredictable use contexts. In this preliminary study, we conducted usability inspection on five portable medical devices. Cognitive walkthrough followed by Nielson’s ten Usability Heuristic evaluation method were utilized for the purpose of usability inspection. Sixteen usability concerns were uncovered in such evaluation study. We also recorded the elapsed time of measuring vital signs with all five devices on a daily basis in a month. The average total completion time after familiarization (after the first week) was 5.17 minutes. Results suggest that ‘non-expert’, independent users may face challenges when using portable medical devices in home care settings. Further, the study results also indicate that unsatisfactory usability of portable medical devices could pose hinderances in technology adoption among low eHealth literate users.

Potential Improvement in a Portable Health Clinic for Community Health Service to Control Non-Communicable Diseases in Indonesia

The COVID-19 pandemic has limited routine community health services, including screening for non-communicable diseases (NCDs). An adaptive and innovative digital approach is needed in the health technology ecosystem. A portable health clinic (PHC) is a community-based mobile health service equipped with telemonitoring and teleconsultation using portable medical devices and an Android application. The aim of this study was to assess the challenges and potential improvement in PHC implementation in Indonesia. This study was conducted in February–April 2021 in three primary health centers, Mlati II in Sleman District, Samigaluh II in Kulon Progo, and Kalikotes in Klaten. In-depth interviews were conducted with 11 health workers and community health workers. At the baseline, 268 patients were examined, and 214 patients were successfully followed-up until the third month. A proportion of 32% of the patients required teleconsultations based on automatic triage. Implementation challenges included technical constraints such as complexity of applications; unstable networks; and non-technical constraints, such as the effectivity of training, the availability of doctors, and the workload at the primary health center. PHCs were perceived as an added value in addition to existing community-based health services. The successful implementation of PHCs should not only be considered with respect to technology but also in terms of human impact, organization, and legality.

Integrated Circuits for Biomedical Applications [Guest Editorial

Biomedical systems that interface with the body and nervous system in wearable and implantable formats are becoming an ever more important tool in our quest to enhance wellness and improve health care. The global medical electronics market was estimated to be worth US$6.3 billion in 2021 and is expected to reach US$8.8 billion by 2026. This growth is driven by many factors, such as an aging population and increasing life expectancy, adoption of IoT smart medical devices, escalating demand for portable medical devices, and ever more sophistication in diagnosing and treating diseases <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> .

Energy Harvesting in Implantable and Wearable Medical Devices for Enduring Precision Healthcare

Modern healthcare is transforming from hospital-centric to individual-centric systems. Emerging implantable and wearable medical (IWM) devices are integral parts of enabling affordable and accessible healthcare. Early disease diagnosis and preventive measures are possible by continuously monitoring clinically significant physiological parameters. However, most IWM devices are battery-operated, requiring replacement, which interrupts the proper functioning of these devices. For the continuous operation of medical devices for an extended period of time, supplying uninterrupted energy is crucial. A sustainable and health-compatible energy supply will ensure the high-performance real-time functioning of IWM devices and prolong their lifetime. Therefore, harvesting energy from the human body and ambient environment is necessary for enduring precision healthcare and maximizing user comfort. Energy harvesters convert energy from various sources into an equivalent electrical form. This paper presents a state-of-the-art comprehensive review of energy harvesting techniques focusing on medical applications. Various energy harvesting approaches, working principles, and the current state are discussed. In addition, the advantages and limitations of different methods are analyzed and existing challenges and prospects for improvement are outlined. This paper will help with understanding the energy harvesting technologies for the development of high-efficiency, reliable, robust, and battery-free portable medical devices.