Medical Monitoring Devices Research Articles

Ultralow Power, Cleft Size-Adjustable and pH-Sensitive Hyaluronic Acid (HA) Biodevices for Acid-Sensing Ion Channels Emulation.

The burgeoning implantable biodevices have unlocked new frontiers in healthcare, promising personalized monitoring strategies tailored to specific needs. Herein, hyaluronic acid (HA) is harnessed to create fully biocompatible, acidity-sensitivity and cleft-adjustable neuromorphic devices. These HA-biodevices exhibit remarkable sensitivity to pH variations, effectively mimicking biological acid-sensing ion channels (ASICs) through protonation reactions between electronegative atoms and hydrogen ions, even at ultralow driving voltage (5mV). They can monitor joint cartilage acidity by tracking changes in proton concentration and successfully diagnose the onset of arthritis. Furthermore, by adjusting the synaptic device's cleft distance, which determines responsiveness, power efficiency and plasticity, HA-based neuromorphic devices can be tailored to meet the unique demands of various implantation sites, providing both high-sensitivity and low-heat dissipation, thus broadening their application scopes. Moreover, the HA-biodevices maintain stable performance across various bending degrees, up to a curvature radius of 7.5mm, with flexibility and deformation resilience enabling installation on joints of varying curvatures. The combination of all-biocompatibility, high sensitivity, low heat dissipation, ultralow low power (2 pW), and extraordinary deformation tolerance paves the way for the development of versatile, multipurpose medical monitoring devices with immense potential in the field of healthcare.

Inductive Paper-Based Flexible Contact Force Sensor Utilizing Natural Micro-Nanostructures of Paper: Simplicity, Economy, and Eco-Friendliness.

Inductive contact force sensors, known for their high precision and anti-interference capabilities, hold significant potential applications in fields such as wearable and medical monitoring devices. Most of the current research on inductive contact force sensors employed novel nanomaterials as sensitive elements to enhance their sensitivity and other performance characteristics. However, sensors developed through such methods typically involve complex preparation processes, high costs, and difficulty in biodegradation, which limit their further development. This article introduces a new flexible inductive contact force sensor using paper as a sensitive element. Paper inherently possesses micro- and nanostructures on its surface and interior, enabling it to sensitively convert changes in contact force into changes in displacement, making it suitable for use as the sensor's sensitive element. Additionally, the advantages of paper also include its great flexibility, low cost, wide availability, and biodegradability. Performance testing on this flexible sensor showed good repeatability, hysteresis, sensitivity, and consistency. When used in experiments for monitoring human motion and respiration, this sensor also exhibited great detection performance. The proposed inductive paper-based flexible contact force sensor, with its simple structure, easy manufacturing process, cost-effectiveness, eco-friendliness, and good sensing performance, provides new insights into research for contact force sensors.

Electrochemical sensing of acetaminophen in biofluids, pharmaceutical and environmental samples using cobalt hexacyanoferrate decorated iron terephthalate metal organic framework

Herein, a highly sensitive, cost-effective, and sustainable electrochemical sensor was developed for the selective sensing of acetaminophen (ACP) in actual samples using cobalt hexacyanoferrate (CHCF) incorporated iron terephthalate metal organic framework (FMF). The CHCF-FMF was sustainably synthesized and characterized using FTIR, XRD and SEM with EDX analysis. Using CHCF-FMF, a modified electrode was fabricated by drop-casting it over a glassy carbon, which on voltammetric investigations, revealed the presence of Co2+/3+ redox couple with a formal potential of 0.51 V. Further, the designed sensor was employed for the electrocatalytic oxidation of ACP, which showed a linear detection range from 0.05 to 7.37 mM with a sensitivity and detection limit of 100.64 µA/mM/cm2 and 13 µM, respectively. Furthermore, the developed CHCF-FMF/GCE sensing system is highly facile and robust, and it has demonstrated excellent stability, selectivity, repeatability, and good reproducibility. The superior performance was attributed to the in-situ formation of CHCF over FMF, which efficiently arranged them to augment the electrochemical and electrocatalytic properties. Besides, the sensor was employed to determine ACP in blood serum, commercial tablets, and environmental samples, which showed recovery ranges of 96.2% – 106.2%. This work provides a novel strategy for designing catalytically active sites at the molecular level, which opens a new avenue in fine-tuning MOFs for their applicability in medical diagnostics, point-of-care, industrial and environmental monitoring devices.

Flexible and Wearable Antenna Design for Bluetooth and Wi-Fi Application

The emergence of wearable technology has revolutionized the way we interact with electronic devices, integrating them into our daily routines. From fitness trackers, smart watches to augmented reality glasses and medical monitoring devices, wearable have become increasingly prevalent. Among the most commonly employed wireless technologies within wearables are Bluetooth and Wi-Fi. The design of efficient and reliable antennas for Bluetooth and Wi-Fi applications in wearable is of paramount importance to ensure optimal performance and user satisfaction. In this paper presented a flexible and wearable antenna design for Bluetooth and Wi-Fi application utilizing the Rogers RT5880 flexible substrate, characterized by a dielectric constant of 2.2 and a thickness of 20mil. The study delves into various analyses of the antenna, including its performance concerning on-body and off-body communication, along with an exploration of the antenna's response to different bending effects. Moreover, the research investigates how the antenna interacts with a human tissue model, providing insights into its behavior and performance in diverse usage scenarios.

Analysis of Touchpoints in the Service Design of Type II Diabetes Health Management System

This study focuses on the service touchpoint design of the Type II Diabetes Health Management System, aiming to explore how optimizing these touchpoints can enhance patients' treatment experience and self-management capabilities. The research thoroughly analyzes the roles of interpersonal, physical, digital, and sensory touchpoints in disease management. It points out that interactions with healthcare professionals, the usability of health management applications, the prevalence of medical monitoring devices, and the reinforcement of family and social support significantly impact patient satisfaction and health management efficiency. This study not only provides practical design guidelines for health services but also offers theoretical and empirical support for future service system design, promoting the development of the field of health service design.

Graphene/Carbon Nanotube Conductive Ink-Based Biomimetic Structure for Self-Powered Flexible Medical Monitoring Devices

Graphene/Carbon Nanotube Conductive Ink-Based Biomimetic Structure for Self-Powered Flexible Medical Monitoring Devices

Application of Smart Wearable Devices in Athlete Health Monitoring

Abstract Along with the rapid development of informationization in the medical industry and the increasing awareness of people’s health care, wearable monitoring technology has ushered in a golden period of growth, and in this context, a wearable athlete’s health monitoring device based on a radial basis function-probabilistic hybrid neural network (RBFNN) is designed. In this paper, the RBFNN is first used to identify the parameters and make corrections according to the changes of the controlled system. Then this neural network is optimized by the EM algorithm, and the EM-RBFNN algorithm that can optimize the smart wearable device is proposed. Through experimental comparison, although both RBFNN and EMRBFNN can match each sample to the number corresponding to the human health condition, EM-RBFNN has a higher accuracy in monitoring human health. The accuracy rate increased to 98%. Finally, through the rate analysis, blood oxygen and heart rate number reliability test, and motion misclassification rate test of the smart wearable device equipped with EM-RBFNN system, the smart wearable device installed with EM-RBFNN system is basically the same as the human body’s various data collected by the standard medical monitoring device. At a sampling rate of 50Hz, the real-time data acquisition rate increased by approximately 126%. The data on blood oxygen and heart rate have small errors. Smooth exercise and strenuous exercise have an error rate of between 10-20%, which is within the normal error range. Therefore, the smart wearable device based on EM-RBFNN can comprehensively monitor the health status of athletes.

Highly-sensitive wearable pressure sensor based on AgNWs/MXene/non-woven fabric

Nowadays, wearable medical devices are getting more and more public attention. As a continuous monitoring device in wearable medical devices, flexible pressure sensors have also attracted much attention in the field of wearable electronics. High sensitivity and long-term durability in a wide linear range are key requirements for making reliable and flexible wearable pressure sensors. Here, a comfortable, flexible, environmentally friendly and wearable non-woven fabric is proposed as a substrate for the pressure sensor. Conductive AgNWs/MXene/non-woven fabric is prepared by a simple dip-coating technique and then an overall sandwich structure is formed between two layers of polydimethylsiloxane (PDMS) film and an Interdigitated Array of Electrodes (IDE). The viscose fibres in nonwovens are rich in hydroxyl groups and their hydrophilicity is more conducive to the attachment of conductive MXene nanosheets to the entangled fibre network of nonwovens. The synergistic effect between Ti3C2Tx and AgNWs, and the unique intercalation structure can build a more effective conductive network. The pressure sensor based on AgNWs/MXene/non-woven fabric has a fibrous entanglement structure and a special sandwich structure of the sensor with high sensitivity (0.25–5 kPa pressure range: 14.28 kPa−1), and have a wide sensing range (0.25–400 kPa), rapid response and recovery times (60 ms/120 ms), excellent stability, and long term durability for use in human motion detection applications. In addition, the wearable pressure sensors was used to detect and discriminate between different signals related to human health, including monitoring human movement and breathing, and 4 × 4 arrays was used to detect stress distribution signals. These results suggest that the sensor can be potentially applied in mobile medical monitoring devices, showing broad potential in a new generation of wearable electronic devices.

A vital signsignal noise suppression method for wearable piezoelectric devices.

This paper tackles the problem of noise suppression during vital signsignal monitoring. Physiological signal monitoring is a significant and promising medical monitoring method, and wearable medical monitoring devices based on piezoelectric polymer sensors are a trending way for their advantages of being flexible in the shape, portable to use, and comfortable to wear. However, this raises the question that the measured signal contains much more noise components. To avoid the following shortcoming of low signal to noise ratio (SNR), a noise suppression method based on improved wavelet threshold and empirical mode decomposition combined with singular value decomposition (SVD) screening the intrinsic mode function (IMF) components is proposed. A wavelet transform is first used under the combination of hard and soft thresholds to focus the target range in the low-frequency region where the energy of the physiological signal is concentrated. Then, a complete ensemble empirical mode decomposition is used to decompose the signal effectively, which can resist the influence of random noises. Meanwhile, a SVD decomposition procedure was used to filter out the lower correlated IMF components to retain the validity of the original signal. We verified the effectiveness of the proposed method through simulated and measured experiments as well as the advantages and disadvantages of the algorithm compared with other physiological signal denoising algorithms through SNR filtering results, power spectrum distribution, and other perspectives. The results proved that the proposed method could effectively remove more detailed noise and improve the SNR of the signal efficiently, which is more conducive to the demand for auxiliary medical diagnosis in the future.

Study of coaxial-dual-gap dielectric barrier discharge based on capillary: discharge characteristics and Escherichia Coli decontamination.

The medical capillary catheters occupy a high proportion of medical diagnosis, monitoring and treatment devices, and will cause serious cross-infection without being disinfected adequately. This paper presents a new plasma structure for efficient inactivation of harmful microorganisms in medical capillaries. An innovative coaxial-dual-gap dielectric barrier discharge (CDG-DBD) reactor powered by nanosecond-pulsed power supply was designed for disinfection of Escherichia Coli (E. coli) inside and outside medical capillary catheters in this work. Atmospheric helium plasma (AHP) and atmospheric air plasma (AAP) were successfully obtained inside and outside capillary (0.6mm inner diameter and 1.0mm outer diameter), respectively. The electrical and optical characteristics of AHP and AAP were investigated. As the threshold of applied voltage amplitude (Uamp) was below 7.0kV, only one helium glow discharge was generated inside the capillary at the rising and falling stages of pulse voltage. As the Uamp exceeded the threshold, two helium glow discharges were generated that further caused generation of air discharge. Under the Uamp of 9.0kV, the production of AHP lowered the breakdown voltage in air gap, resulting in the formation of high-volume and uniform AAP, which was conducive to the realization of full inactivation. The inactivation rates of E. coli reached 98.13% and 99.99% by 2min AHP and 0.5min AAP treatment, respectively. The electrical stress of AHP and the reactive oxygen and nitrogen species (RONS) produced by AAP were contributed to the inactivation of E. coli. The results of SEM show that plasma treatment can destroy the cellular structure of E.coli.

Wearable Optical Fiber Sensors in Medical Monitoring Applications: A Review.

Wearable optical fiber sensors have great potential for development in medical monitoring. With the increasing demand for compactness, comfort, accuracy, and other features in new medical monitoring devices, the development of wearable optical fiber sensors is increasingly meeting these requirements. This paper reviews the latest evolution of wearable optical fiber sensors in the medical field. Three types of wearable optical fiber sensors are analyzed: wearable optical fiber sensors based on Fiber Bragg grating, wearable optical fiber sensors based on light intensity changes, and wearable optical fiber sensors based on Fabry-Perot interferometry. The innovation of wearable optical fiber sensors in respiration and joint monitoring is introduced in detail, and the main principles of three kinds of wearable optical fiber sensors are summarized. In addition, we discuss their advantages, limitations, directions to improve accuracy and the challenges they face. We also look forward to future development prospects, such as the combination of wireless networks which will change how medical services are provided. Wearable optical fiber sensors offer a viable technology for prospective continuous medical surveillance and will change future medical benefits.

Blockchain for IoT-enabled Healthcare

Emerging technologies including such Internet of Things (IoT) and blockchain contribute significantly to the improvement of health services. The purpose of this chapter is to achieve and democratize services through the provision of medical care as a service. The result was the development of medical gadgets integrating healthcare sensors. It links medical equipment like the temperature controller to the cloud environment of medical doctors and staff. This study introduced the combination of IoT and Blockchain as a secure platform to reduce the scarcity of nurses. Blockchain was employed for storing and validating patient information in the proposed operating framework. A significant reduction in nursing gaps for large-scale patients has been shown. All technological specifications have been given to allow the prototyping execution of these suggested medical services simply adaptable. This article deals with Blockchain technology inclusion in Remote Medical Monitoring Devices Internet of Things (IoT) security. The document provides the advantages of Blockchain based safety methods and practical barriers in remote health monitoring via IoT devices. The study also examines several cryptographic methods appropriate for IoT implementation.

Enhanced Defensive Model Using CNN against Adversarial Attacks for Medical Education through Human Computer Interaction

The design of human-machine interfaces is more precise and demanding in the medical and healthcare industries. Medical monitoring equipment demands more consistent and effective interpretation, as well as fast and straightforward operation due to its monitoring and reference functions. Consequently, it is crucial to consider how people interact with computers when designing the interface for medical monitoring devices. Nowadays people are giving more importance to health than anything in the world. Therefore, as it is related to peoples’ safety, the architecture of human-computer communication must be carefully considered in the studies and development of high-end medical equipment. The price of training physicians and other medical professionals is rising dramatically. Most of the countries have stepped forward from the traditional medical teaching system to a more human computer interactive teaching and learning environment with innovative technologies. This article focuses on the related researches, existing HCI applications for healthcare and the application of deep neural network for disease classification. The proposed work is to develop a healthcare learning platform to offer healthcare education to both medical practitioners and also for common people. This can be implemented as Mobile apps using human-computer interface technology and also as a website with Artificial Intelligence and Machine Learning Techniques. This proposal’s primary goal is to provide anytime, everywhere access to healthcare education for physiological and medical teaching courses, consequently advancing national health care.

RASCv2: Enabling Remote Access to Side-Channels for Mission Critical and IoT Systems

The Internet of Things (IoT) and smart devices are currently being deployed in systems such as autonomous vehicles and medical monitoring devices. The introduction of IoT devices into these systems enables network connectivity for data transfer, cloud support, and more, but can also lead to malware injection. Since many IoT devices operate in remote environments, it is also difficult to protect them from physical tampering. Conventional protection approaches rely on software. However, these can be circumvented by the moving target nature of malware or through hardware attacks. Alternatively, insertion of the internal monitoring circuits into IoT chips requires a design trade-off, balancing the requirements of the monitoring circuit and the main circuit. A very promising approach to detecting anomalous behavior in the IoT and other embedded systems is side-channel analysis. To date, however, this can be performed only before deployment due to the cost and size of side-channel setups (e.g., and oscilloscopes, probes) or by internal performance counters. Here, we introduce an external monitoring printed circuit board (PCB) named RASC to provide r emote a ccess to s ide- c hannels. RASC reduces the complete side-channel analysis system into two small PCBs (2 \( \times \) 2 cm), providing the ability to monitor power and electromagnetic (EM) traces of the target device. Additionally, RASC can transmit data and/or alerts of anomalous activities detected to a remote host through Bluetooth. To demonstrate RASCs capabilities, we extract keys from encryption modules such as AES implemented on Arduino and FPGA boards. To illustrate RASC’s defensive capabilities, we also use it to perform malware detection. RASC’s success in power analysis is comparable to an oscilloscope/probe setup but is lightweight and two orders of magnitude cheaper.

Construction and Evaluation of Construction Safety Management System Based on BIM and Internet of Things

With the continuous development and maturity of technology, the Internet of Things has gradually been applied to various fields of work and life, for example, the use of the Internet of Things and road traffic, the use of the Internet of Things smart city for vehicle identification and GPS positioning, the use of the Internet of Things smart medical smart heart pulse monitoring device, and so on. And, the construction industry bears important responsibilities for the development of national infrastructure and people’s livelihood. This article aims to study the application of BIM and Internet of Things technology in the safety management of construction sites, to drive technological development with industry needs, help expand the application fields of the Internet of Things, and promote the improvement of the national construction safety management system. This article uses BIM and Internet of Things technology to build a construction safety management system, focusing on the protection of people, through technical means, such as personnel activity control, hazard warning, major hazard monitoring, and multiple institutional measures, such as education, training, supervision, and protection, that collaborate to complete the protection and risk avoidance of operators in the process of building construction. The evaluation index of the safety management system is proposed through the analytic hierarchy process, and the dimension of the safety management system to prevent accidents is evaluated by the analytic hierarchy process. Taking the actual project as an example, the application effect of the remote monitoring system is qualitatively analyzed. On the basis of the analysis, from the three levels of policy, enterprise, and practitioners, several suggested measures that will help new technology to be applied to construction safety management are put forward. In the experiment part, the construction of the safety management system is first explained, then the appropriate system evaluation index is selected, and then the comprehensive ranking is conducted according to the relative importance weight of the highest-level elements in the same level, and the MATLAB software is used to test the consistency. The experimental results show that the construction safety management system constructed in this paper has been well perfected. The calculated weights of people and dangerous areas are as high as 0.578, so that people and dangerous areas can be controlled effectively. Fall accidents from high places can be prevented, full-time monitoring of foundation pit projects can also avoid fall and collapse accidents from high places, improve the comprehensiveness and advancement of construction safety management, and improve the level of construction safety management.

An Application of Machine Learning to Forecast Hypertension Signals in Intracranial Pressure: A Comparison of Various Algorithms

The objective of the work presented in this article is to investigate the applicability of lightweight machine learning (ML) algorithms capable of detecting and forecasting hypertensive (HT) episodes from historical intracranial pressure (ICP) signals. Specifically, we aim at identifying noncomputationally dependent algorithms, which can be supported by lightweight hardware such as medical monitoring devices. We also propose applicable algorithms, which can be trained with a limited number of labeled samples due to the unfeasibility of manually labeling large volumes of ICP signals in most instances.

Remote patient monitoring after cardiac surgery: The utility of a novel telemedicine system.

ObjectiveWe examined cardiac surgery patients who underwent monitoring of postoperative vital parameters using medical monitoring devices which transferred data to a mobile application and a web‐based software.MethodsFrom November 2017 to November 2020, a total of 2340 patients were enrolled in the remote patient monitoring system after undergoing cardiac surgery. The medical devices recorded vital parameters, such as blood pressure, pulse rate, saturation, body temperature, blood glucose, and electrocardiography were measured via the Health Monitor DakikApp and Holter ECG DakikApp devices which reported data to web‐based software and a mobile application (DakikApp Mobile Systems, Remscheid, Germany). During the follow‐up period, patients were contacted daily through text and voice messages, and video conferences. Remote Medical Evaluations (RMEs) concerning patients' medical states were performed. Medication reminders, daily treatment were communicated to the patients with the DakikApp Mobile Systems Software.ResultsDuring a mean follow‐up period of 78.9 ± 107.1 (10–395) days, a total of 135,786 patient contacts were recorded (782 video conferences, 2805 voice messaging, and 132,199 text correspondence). The number of RMEs handled by the Telemedicine Team was 79,560. A total of 105,335 vital parameter measurements were performed and 5024 hospital application requests (6.3% per RME) were addressed successfully and hospitalization was avoided. A total of 144 (6.1%) potentially life‐threatening complications were found to have been diagnosed early using the Telemedicine System.ConclusionRemote Patient Monitoring Systems combined with professional medical devices are feasible, effective, and safe for the purpose of improving postoperative outcomes.

Improvements in Medical System Safety Analytics for Authentic Measure of Vital Signs Using Fault-Tolerant Design Approach.

The study presents a novel design method that improves system availability using fault-tolerant features in a non-invasive medical diagnostic system. This approach addresses the effective detection of functional faults, improves the uninterruptible system operating period with reduced false alarms, and provides an authentic measure of vital cardiac signs using diverse multimodal sensing elements like the photoplethysmogram (PPG) and the ECG. Most systems rely on a 1oo1 (one-out-of-one) design method, which inherently limits accuracy in existing practice. In this proposed approach, the quality of segregated authentic vital sign measured values could tremendously benefit the performance of resourceful nursing with negligible alarm fatigue and predict illness more accurately. The system builds upon the selected 2oo2 (two-out-of-two) safety-related design architecture and is evaluated with implemented functions like the fault detection and identification logic, the correlation coefficient-based safety function, and the fault-tolerant safe degradation switching mechanism for accurate measurements. The system was tested on 50 adults of various age groups. The analyzed captured data showed highly accurate vital sign data in this fault-tolerant approach with reduced false alarms. The proposed design method evaluated safety-related mechanisms along with a combination of the same and diverse sensors in a medical monitoring device, showing more reliable functioning of the system and authentic data for better nursing. This design approach showed a 45–55% increased improvement in system availability, thus allowing for accurate and uninterruptable tracking of vital signs for better nursing during critical times in the ICU.

Effect of Digital Adherence Tools on Adherence to Antiretroviral Treatment Among Adults Living With HIV in Kilimanjaro, Tanzania: A Randomized Controlled Trial.

Lifelong adherence to antiretroviral treatment remains challenging for people living with HIV (PLHIV). The aim of this study was to investigate whether any of 2 digital adherence tools could improve adherence among PLHIV in Kilimanjaro, Tanzania. We performed a parallel 3-arm, nonblinded, randomized controlled trial with 1:1:1 allocation. We included adults aged between 18 and 65 years, living in Kilimanjaro region, and who were on antiretroviral treatment for at least 6 months. Their adherence, as judged by the study nurses, had to be suboptimal. In one arm, participants received reminder short message service (SMS) texts, followed by a question SMS. In the second arm, participants received a real-time medication monitoring (RTMM) device (Wisepill) with SMS reminders. In the third arm, participants received standard care only. The primary outcome of mean adherence over 48 weeks was compared between arms using between-group t tests in a modified intention-to-treat analysis. In each arm, we randomized 83 participants: data of 82 participants in the RTMM arm, 80 in the SMS arm, and 81 in the standard care arm were analyzed. The average (over 48 weeks) adherence in the SMS, RTMM, and control arms was 89.6%, 90.6%, and 87.9% for pharmacy refill; 95.9%, 95.0%, and 95.2% for self-report in the past week; and 97.5%, 96.6%, and 96.9% for self-report in the past month, respectively (P values not statistically significant). Receiving reminder SMS or RTMM combined with feedback about adherence levels and discussion of strategies to overcome barriers to adherence did not improve adherence to treatment and treatment outcome in PLHIV. PACTR201712002844286.

Leveraging Remote Physiologic Monitoring in the COVID-19 Pandemic to Improve Care After Cardiovascular Hospitalizations.

Leveraging Remote Physiologic Monitoring in the COVID-19 Pandemic to Improve Care After Cardiovascular Hospitalizations.