Remote Health Monitoring Research Articles

Location-Independent Intelligent Interface Customized to the Aid and Response of the Differently Abled

Today, the most feasible and easy form of Human-Computer Interaction (HCI) is the use of mobile applications. Mobile applications have become an integral part of our day-to-day lives reaching out to each corner of society and catering to the problems. The applications available are customized, but the incorporation of intelligence especially for the disabled to assist their lives is absent. This motivates the research work specified in this paper is non-commercial application development for society. Designing applications that are highly scalable and intelligent to minimize problems faced by differently abled, is explicitly covered here. The proposed system makes use of Speech-To-Text and Text-To-Speech conversion techniques for the back end and provides enhanced scalability by allowing users to regulate the functionality of the application, customized based on their preferred region, topic, and time stamps. Applications and devices meant for the differently abled do exist, however, most of them have unidirectional and singular functionality with minimum customizability. In most cases, very complicated hardware systems are associated with the proposed work which in turn makes it expensive to maintain. The proposed application aims to offer voice-based functionality automation, remote health monitoring, and pattern recognition for recommendation systems, explicitly for the visually impaired and the solitary elderly. The reading of remote health monitoring is close to the actual value with a standard deviation of 1.45% or 0.76 on the average which validates the working methodology adopted.

Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)

Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.

Spo2 Analysis on Development of IoT-Based Lung Function and Spo2 Measuring Device

Pulmonary dysfunction is a widespread issue, particularly in developing nations. It encompasses restrictive, obstructive, and mixed pulmonary function disorders that lead to a decrease in vital lung capacity, an increase in functional residual capacity, and a decline in blood oxygen concentration and saturation. This study aims to combine oximetry and spirometry into a single device, using the Internet of Things (IoT) technology to display results via a smartphone app. The focus is on analyzing oxygen saturation, with normal levels ranging from 96% to 100% in adults, alongside a heart rate of 60-100 beats per minute. The MAX30102 sensor measures oxygen saturation, and the Arduino Pro Mini and D1 Mini ESP32 microcontrollers process data. The Android-based app, developed using Kodular platform, integrates a MySQL database and connects to the device module via Wi-Fi. Ten respondents underwent five measurements, revealing an average error of ±0.88% for oxygen saturation (SpO2) and ±2.82% for heart rate measurements. The average data loss rate during transmission was ±0.66% for SpO2 and ±0.89% for heart rate. These findings highlight existing errors in the module. The research aims to facilitate remote health monitoring for healthcare professionals, improving accessibility and healthcare provision.

A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

ABSTRACT Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.

A Robust and Secure Electronic Internet of Things-Cloud Healthcare Framework for Disease Classification Using Deep Learning

In the modern era, the Internet of Things (IoT) based nano-electronic devices are essential to provide practical patient information through monitoring performance in the healthcare system. Moreover, remote health monitoring is becoming necessary for lowering healthcare expenses and enhancing patient care due to the aging population and the rise in chronic illnesses. Recently, there has been a lot of interest in the IoT based nanoelectronics devices as a potential solution for remote health monitoring. An extensive range of physiological data, including heart rates, blood oxygen levels, body temperatures, ECG signals, etc., can be collected and analyzed by IoT-based sensors devices, giving medical practitioners real-time feedback so they can respond appropriately. Therefore, in this paper, a novel Functional neural network-based Aquila optimization (FNN-AO) algorithm is developed to classify the various types of diseases. Furthermore, an Identity-based Encryption (IbE) algorithm is adapted to secure the healthcare data. Initially, IoT based electronic sensors collect and store healthcare data in the cloud storage system. This process was done with the help of the MATLAB platform, and parameters were analyzed and compared with existing techniques in terms of performance metrics. From the comparison, the proposed framework has a 2.34% improvement over the other models.

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Objectives and Methodology: However, the Internet-of-Things(IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results and Conclusions: This study also contributes in two ways to the ongoing effort to investigate security-privacy concerns in the circumstance of smart cities for healthcare purposes. Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.

Examining medical urgent and patient precedence within the telemedicine landscape with its infrastructure and policies: A comprehensive review

Medical facilities are confronted with grave issues, including a population that is aging and a physician shortage. In an effort to address these issues, telehealth and remote health monitoring systems (RHMS) aim to reduce hospital visits by a small amount. RHMS lessens the workload for primary care patients and enhances inter-unit communication, which lessens the strain on emergency rooms. Due to significant advancements in mobile information transfer and processing of signal technologies, some healthcare researchers have made efforts to employ RHMS in place of hospital visits to provide triage and prioritization for individuals. Prioritization is a strategy for giving people urgent medical care in an attempt to save their lives, while clinical examination determines the severity of a sickness or damage. To emphasize the disadvantages of the present patient screening and prioritization system over the telehealth environment, a crucial inquiry is needed. Based on two crucial axes, an in-depth examination of patient prioritization and medical crisis assessment in a videoconferencing setting was provided in this research. First, a collection, analysis, and classification of earlier research on triage of patients and prioritization in this kind of setting was done. Second, a variety of priorities and standards, as well as various approaches and procedures for prioritization, were provided and examined. The subsequent outcomes were attained: The shortcomings and issues with the current patient prioritization and assessment methods were highlighted. The assessment and priority for individuals who have cardiac conditions were not presented. In the years to come, a structure based on the theory of evidence, as well as the incorporation of a multiple-layer analytical hierarchy approach and method for ranking preference due to resemblance to perfect solutions, can be used to prioritize the care of several patients with ongoing cardiovascular disease by triaging them based on various emergencies dimensions.

Soft, full Wheatstone bridge 3D pressure sensors for cardiovascular monitoring

Variations in parameters associated with the ambient environment can introduce noise in soft, body-worn sensors. For example, many piezoresistive pressure sensors exhibit a high degree of sensitivity to fluctuations in temperature, thereby requiring active compensation strategies. The research presented here addresses this challenge with a multilayered 3D microsystem design that integrates four piezoresistive sensors in a full-Wheatstone bridge configuration. An optimized layout of the sensors relative to the neutral mechanical plane leads to both an insensitivity to temperature and an increased sensitivity to pressure, relative to previously reported devices that rely on similar operating principles. Integrating this 3D pressure sensor into a soft, flexible electronics platform yields a system capable of real-time, wireless measurements from the surface of the skin. Placement above the radial and carotid arteries yields high-quality waveforms associated with pulsatile blood flow, with quantitative correlations to blood pressure. The results establish the materials and engineering aspects of a technology with broad potential in remote health monitoring.

Development of an IoT-Based Automatic Remote Health Monitoring System

A well-planned health monitoring system is essential for the modern health center, and a portable remote health monitoring system with many intellectual features is becoming a great improvement in healthcare arrangements. The proposed system includes various micro-electronics medical devices and applications that come through network-connected devices and help to monitor patients' real-time medical data. In this paper, the patient's health condition has been monitored by using the four types of major health parameters temperature sensor, heart pulse rate sensor, blood pressure sensor module, and blood oxygen (SpO2) sensor. All the sensor nodes are connected to the Raspberry Pi-based embedded system. The real-time health data are recorded, stored, and transmitted to the doctor via the internet with the help of a Raspberry Pi-based embedded system and IoT server. The system will benefit the patient from remote areas with quick diagnosis, remote observation, home observation, and a medical data storage system. Journal of Engineering Science 14(2), 2023, 21-30

Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect

Rotational machinery is a common presence in dust still production, and the occurrence of operational failures in components like engines and turbine blades necessitates effective measures. To solve this challenge, remote structural health monitoring using energy harvesting and wireless sensors has been widely employed to realize self-powered sensing. This study proposes a magnet-induced bi-stable rotational energy harvester (REH), which utilizes the centrifugal effect to broaden the effective frequency bandwidth, enabling efficient energy harvesting in complex environments. A comprehensive mathematical model has been established to facilitate the dynamic characteristics of the bi-stable system, taking into account the centrifugal effect. The theoretical results demonstrate that the gap distance of magnetic configuration has great effects on the bi-stable system. Additionally, the centrifugal effect decided by the centrifugal radius and rotational speeds also affects the stable high-energy orbit oscillations. Furthermore, experimental results indicate that the proposed REH can effectively operate within the frequency range of 230–290 rpm, with a maximum RMS voltage of 780 mV and corresponding power of 4.35 mW. These findings validate the performance of the bi-stable magnetostrictive REH with the centrifugal effect and indicate its potential to effectively address the power supply challenges for wireless sensors. Overall, this study presents a promising solution for enhancing the energy harvesting performance of REH and also provides insights into the design of high-efficiency REH by magnet-induced nonlinearity and the centrifugal effect.

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.

Cushy and Cheap Smart Health Monitoring System Incorporating a Ventilator

The handiness of mercantile mechanical ventilators is highly limited in low-income countries due to high rate in the price of its affordability. This factor affects the treatment of so many patients suffering from chronic respiratory syndrome. In this research, a low- cost and easy to use pressure ventilator coupled with a remote health monitoring system to assist the doctors check up on the patients is developed. There is need to maximize the availability of the ventilators in low-income countries at an affordable price tag. This system was developed with the aid of an Arduino nano, ESP8266 based NodeMcu microcontroller, high pressure blower, pressure transducers, humidity and temperature sensor. The developed system was evaluated and compared with existing one, using an actively breathing patient simulator as it mimics through a range of respiratory diseases. In the end, we were able to design a low-cost smart health monitoring system that incorporates ventilator and health vitals monitor. Our Design was implemented and verified to be working and equally satisfying all the specified requirements.

Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused.

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects’ true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques.

Model for recognizing human behavior via feature and classifier selection

Motion or inertial sensors, like the accelerometer and gyroscope that are frequently found in smartphones and smartwatches, can measure the acceleration and angular velocity of bodily movements and be used to teach bots so that they may guess human activities. These models can be selected to a variety of fields, including biometrics and remote patient health monitoring. Because deep learning-based methods employ representing teaching methods, those may automatically identify hidden patterns in data and generate optimal objects from basic information generated from sensors without human intervention, they got popular in earlier in recognizing human activities. Along with recognize human activity, this paper suggests a novel called HDDN-model called CNN-GRU, which combines convolutional units. This model exhibited accuracy that is suggestively better than other state-of-the-art DNN models like Inception Time and Deep Conv LSTM developed using Auto ML, and was successfully verified on the WISDM dataset.

Blockchain-Based Logging to Defeat Malicious Insiders: The Case of Remote Health Monitoring Systems

Blockchain-Based Logging to Defeat Malicious Insiders: The Case of Remote Health Monitoring Systems

Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications.

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

Design and Implementation of an IoT Based Remote Health Monitoring System

Design and Implementation of an IoT Based Remote Health Monitoring System