An enhanced healthcare system in mobile cloud computing environment

Abstract Mobile cloud computing (MCC) is a new technology for mobile web services. Accordingly, we assume that MCC is likely to be of the heart of healthcare transformation. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. To this end, we implement a medical cloud multi-agent system (MCMAS) solution for polyclinic ESSALEMA Sfax—TUNISIA, using Google's Android operating system. The developed system has been assessing using the CloudSim Simulator. This paper presents initial results of the system in practice. In fact the proposed solution shows that the MCMAS has a commanding capability to cope with the problem of traditional application. The performance of the MCMAS is compared with the traditional system in polyclinic ESSALEMA which showed that this prototype yields better recital than using usual application

A mobile application for ECG detection and feature extraction

This paper presents a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easy wearable and mobile ECG biosensor, a cloud based data center, smartphone and web application. A significant part in the system is the 24h health monitoring and care provided by expert cardiac physicians. The system predicts potential heart attack and sends risk alerts to the medical experts for assessment. If a potential heart attack risk exists, ambulance is being called with the coordinates of the cardiac patient wearing the sensor. The timely reaction can prevent serious tissue damage or even death to the users of the system

PATIENTS’ ACCEPTANCE AND RESISTANCE TOWARD THE HEALTH CLOUD: AN INTEGRATION OF TECHNOLOGY ACCEPTANCE AND STATUS QUO BIAS PERSPECTIVES

The latest technological trends such as health cloud provide a strong infrastructure and offer a true enabler for healthcare services over the Internet. Despite its great potential, there are gaps in our understanding of how users evaluate change related to the health cloud and decide to resist it. According to the technology acceptance and status quo bias perspectives, this study develops an integrated model to explain patients’ intention to use and resistance to health cloud services. A field survey was conducted in Taiwan to collect data from patients. The structural equation model was used to examine the data. The results showed that patient resistance to use was caused by inertia, perceived value, and transition costs. Perceived usefulness (PU) and perceived ease of use (PEOU) have positive and direct effects on behavioral intention to use, and PEOU appears to have a positive direct effect on PU. The results also indicated that the relationship between intention to use and resistance to use had a significant negative effect. Our study illustrates the importance of incorporating user resistance in technology acceptance studies in general and health technology usage studies in particular, grounds for a resistance model of resistance that can serve as the starting point for future research in this relatively unexplored yet potentially fertile area of research

Medical Big Data Analysis in Hospital Information System

The rapidly increasing medical data generated from hospital information system (HIS) signifies the era of Big Data in the healthcare domain. These data hold great value to the workflow management, patient care and treatment, scientific research, and education in the healthcare industry. However, the complex, distributed, and highly interdisciplinary nature of medical data has underscored the limitations of traditional data analysis capabilities of data accessing, storage, processing, analyzing, distributing, and sharing. New and efficient technologies are becoming necessary to obtain the wealth of information and knowledge underlying medical Big Data. This chapter discusses medical Big Data analysis in HIS, including an introduction to the fundamental concepts, related platforms and technologies of medical Big Data processing, and advanced Big Data processing technologies

Global Differential Privacy for Distributed Metaverse Healthcare Systems

Metaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial intelligence (ML/AI) techniques can be widely utilized in metaverse healthcare systems, such as virtual clinics and intelligent consultations. In such scenarios, the key challenge is that data privacy laws might not allow virtual clinics to share their medical data with other parties. Moreover, clinical AI/ML models themselves carry extensive information about the medical datasets, such that private attributes can be easily inferred by malicious actors in the metaverse (if not rigorously privatized). In this paper, inspired by the idea of "incognito mode", which has recently been developed as a promising solution to safeguard metaverse users' privacy, we propose global differential privacy for the distributed metaverse healthcare systems. In our scheme, a randomized mechanism in the format of artificial "mix-up" noise is applied to the federated clinical ML/AI models before sharing with other peers. This way, we provide an adjustable level of distributed privacy against both the malicious actors and honest-but curious metaverse servers. Our evaluations on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility trade-off (PUT) in terms of diagnosis accuracy and loss function for different levels of privacy. We also compare our private scheme with the non-private centralized setup in terms of diagnosis accuracy

Human Respiration Rate Measurement with High-Speed Digital Fringe Projection Technique

This paper proposes a non-contact continuous respiration monitoring method based on Fringe Projection Profilometry (FPP). This method aims to overcome the limitations of traditional intrusive techniques by providing continuous monitoring without interfering with normal breathing. The FPP sensor captures three-dimensional (3D) respiratory motion from the chest wall and abdomen, and the analysis algorithms extract respiratory parameters. The system achieved a high Signal-to-Noise Ratio (SNR) of 37 dB with an ideal sinusoidal respiration signal. Experimental results demonstrated that a mean correlation of 0.95 and a mean Root-Mean-Square Error (RMSE) of 0.11 breaths per minute (bpm) were achieved when comparing to a reference signal obtained from a spirometer

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.publishedVersio

Vastasyntyneen ja imeväisikäisen vauvan unenaikaisen hengitys- ja syketaajuuden tarkkailu puettavalla liikeanturilla

Vastasyntyneelle ja imeväisikäiselle nukkuminen on elintärkeä toiminto, ja se on välttämätöntä aivoverkkojen kehitykselle. Tiedetään, että huono unenlaatu aiheuttaa pitkällä tähtäimellä muun muassa kasvun hidastumista ja käyttäytymisongelmia. Imeväisikäisillä melko yleisesti esiintyvät unihäiriöt, kuten yöheräily ja nukahtamisvaikeudet aiheuttavat merkittävää rasitusta ja huolta vanhemmille. Objektiivisen mittausmenetelmän puutteen vuoksi ei ole kuitenkaan voitu selvittää imeväisikäisen unen kehittymistä kotiolosuhteissa. Tässä tutkimuksessa tarkasteltiin puettaviin pöksyihin kiinnitetyn liikeanturin ja EKG-kangaselektrodien soveltuvuutta vastasyntyneiden ja imeväisikäisten vauvojen unenaikaisen hengityksen ja sykkeen tarkkailuun. Tutkimuksen ensimmäisessä vaiheessa päiväaikaisten uni-EEG-tutkimuksien yhteydessä verrattiin liikeanturin mittauskanavien rekisteröimiä mittauskäyriä pietsoanturilla varustettuun hengitysvyöhön. Saatujen tutkimustuloksien perusteella liikeanturin gyroskooppi osoittautui tarkimmaksi hengitystaajuutta mittaavaksi parametriksi, kun taas anturin välittämä EKG-signaali oli tulkintakelpoisin osin luotettavaa. Tutkimuksen toisessa vaiheessa vauvaperheille annettiin unipöksyt ja älypuhelimet kotiin arvioidaksemme yön yli kestävää kotikäyttöä. Tutkimustulokset viittaavat siihen, että eri unitilojen tunnistaminen hengityksen vaihtelusta olisi todennäköisesti mahdollista gyroskooppisignaalista. Vanhemmilta saadun palautteen perusteella unipöksyjä pidettiin käytännöllisinä ja helppokäyttöisinä. Tulevissa tutkimuksissa tulisi keskittyä liikeanturin validointiin kliinisesti hyväksyttyjen mittausparametrien avulla, jotta algoritmeja voisi opettaa tunnistamaan eri uni-valve rytmejä automaattisesti. Näin puettava liikeanturi voisi tarjota tietoa vauvan luonnollisen unirakenteen kehittymisestä pitkällä aikavälillä. Lisäksi anturin kliininen validointi voisi mahdollistaa imeväisikäisten kardiorespiratoristen ongelmien ja liikehäiriöiden diagnostisen lisätyökalun kehittämisen.Sleep is one of the most vital functions of newborns and infants, and it is essential for neuronal network development. Therefore, long-term sleep disturbances have been associated with growth delays and behavioral disorders. Commonly reported infant sleep disturbances, such as night awakenings and difficulties falling asleep, cause distress to parents. Yet, the development of infant sleep in the home environment has not been fully elucidated due to lack of objective measurement parameters. In the current study, we assessed the feasibility of a motion sensor, attached to wearable pants, and ECG textile electrodes to monitor sleep-related respiration and heart rate of newborns and infants. First, we compared signals recorded by the motion sensor’s measurement channels to the standard respiratory piezo effort belt’s signal during daytime EEG recordings. According to our results, the motion sensor’s gyroscope proved to measure respiratory rate most accurately, while the ECG signal transmitted by the sensor was reliable in interpretable sections. We then provided wearable garments and smartphones to families with infants to assess overnight home-use. Our results indicate that different sleep states could likely be identified based on respiration fluctuation visible in the gyroscope’s signals. Moreover, the wearable system was considered practical and easy to use by the parents. Future studies should focus on validating the sensor with clinically approved measures, in order to train the algorithms to automatically identify different sleep-wake states. By doing so, the wearable sensor could provide information on natural infant sleep structure development over long time periods. Additionally, clinical validation of the sensor may result in the development of a companion diagnostic tool for infant cardiorespiratory and movement disorders