Mobile Health Monitoring System Research Articles

Design of The WBAN in The Mobile Health Monitoring System

WBAN (Wireless Body Area Network) technology is used in this study to provide an integrated health monitoring platform. The device utilizes MLX90615 for measuring body temperature, a Blood Pressure Sensor for measuring blood pressure and pulse rate, and a MAX30102 Sensor for measuring blood oxygen and blood glucose levels. The output of this utility is displayed on an OLED screen and is controlled by the NODEMCU ESP8266. The test reveals an accuracy of 94.8%, indicating that the instrument can provide accurate results and has a relatively low error rate on each sensor. Heart Rate Error on Blood Pressure Sensor 0; Spo2 Error on MAX 30102 Sensor 1; and Blood Sugar Error on MAX 30102 Sensor 2.0. Real-time health monitoring that is more accurate and efficient. This device has the potential to be a practical solution for routine health monitoring and provides significant benefits for maintaining health.

Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications.

In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field. The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section. The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare. Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.

The Role of Interactive Technologies in Optimizing the Process of Patient Treatment: Prospects and Challenges for Health Care

The introduction of medical digital technologies can provide better access and flexibility of healthcare for the population. It includes the availability of open information about health, treatment, complications, and recent progress in biomedical research. Nowadays, even in low-income countries, diagnostic and medical services are becoming more accessible. However, many issues related to digital health technologies remain unresolved, including reliability, security, testing, and ethical issues.The penetration of AI currently affects almost all areas of work of medical organizations and government and supervision bodies in the field of healthcare, as well as all major nosological groups. The leading areas of AI application are: medical diagnostics and image analysis, predictive clinical and managerial analytics, medical decision support systems, data analysis of wearable devices, telemedicine, monitoring of patients with chronic non-communicable diseases, virtual assistants, including robot-assisted surgery, decision assistance mental health problems, etc.This article explores the role of interactive technologies in optimizing the patient care process and discusses the prospects and challenges they present for healthcare. In recent years, interactive technologies such as mobile applications, virtual reality, remote medical consultations and electronic health monitoring systems have become increasingly common in medical practice. Digital platforms can help optimize patient diagnosis, counselling and treatment. However, due to the lack of official regulations and recommendations, stakeholders, including private and public organizations, face the problem of adequate verification and testing of new digital health technologies. In this regard, proper scientific research is needed before a digital product is deployed for the health sector.The authors emphasize that interactive technologies have a significant potential for optimizing the process of treating patients in healthcare. They can improve the availability and quality of medical care, as well as increase patient satisfaction.

Factors Influencing the Adoption of Mobile Health Monitoring and Care Systems by the Elderly Living at Home in South Africa

Introduction: Technology can improve the health care outcomes of the elderly. However, there are many barriers to the adoption of mobile monitoring and care systems (MMCs) by the elderly reported in literature. The main purpose of this research was to investigate why elderly people do not adopt MMCs to improve the quality of their health care.
 
 Methods: The study made use of semi-structured interviews to collect data in the Buffalo City Metropolitan Municipality, South Africa. The study sample consisted of 15 purposively chosen study participants, representing a diverse racial profile, in order to investigate the adoption of MMCs of the elderly in their home environment. The interviews were analysed thematically.
 
 Results: The study found that social challenges like technology anxiety, the intrusive nature of the technology and attitude towards the technology will affect adoption of MMCs. Government need to address the standardisation, data governance and interoperability of the systems. The cost of MMCs were seen as an inhibitor, but could be off set against the perceived return on investment of health care. While the elderly acknowledged that MMCs can improve their independence, the design of the MMCs and training to use it was identified as barriers to the adoption.
 
 Conclusion: The study, therefore, recommends collaborative engagements amongst government, business and the elderly to facilitate the adoption of MMCs in the elderly community to improve the quality of health care.

Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system.

In order to help patients monitor their personal health in real time, this paper proposes an intelligent mobile health monitoring system and establishes a corresponding health network to track and process patients' physical activity and other health-related factors in real time. Performance was analyzed. The experimental results show that after comparing the accuracy, delay time, error range, efficiency, and energy utilization of Im-HMS and existing UCD systems, it is found that the accuracy of Im-HMS is mostly between 98 and 100%, while the accuracy of UCD is mostly between 98 and 100%. Most of the systems are between 91 and 97%; in terms of delay comparison, the delay of the Im-HMS system is between 18 and 39 ms, which is far lower than the lowest value of the UCD system of 84 ms, and the Im-HMS is significantly better than the existing UCD system; the error range of Im-HMS is mainly between 0.2 and 1.4, while the error range of UCD system is mainly between −2 and 14; and in terms of efficiency and energy utilization, Im-HMS values are higher than those of UCD system. In general, the Im-HMS system proposed in this study is more accurate than UCD system and has lower delay, smaller error, and higher efficiency, and energy utilization is more efficient than UCD system, which is of great significance for mobile health monitoring in practical applications.

Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V.2.0 randomised clinical trial

IntroductionSuicide is one of the leading public health issues worldwide. Mobile health can help us to combat suicide through monitoring and treatment. The SmartCrisis V.2.0 randomised clinical trial aims to...

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network.

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

Review of Mobile Cloud Computing in Healthcare for Diabetics Patients Using Machine Learning Techniques

Abstract: The number of chronic diseases such as diabetes, cancer, heart disease, and others is fast increasing in our daily lives. The disadvantages of the traditional healthcare system are becoming more prevalent. One of the most important is that healthcare is only offered in hospitals. No one has access to it and no one is monitoring it. Patients' information is securely acquired from the hospital, with their consent, and monitored on a regular basis using their smart phones in mobile cloud computing. On a daily basis, a real-time mobile cloud health monitoring system is used. The patient's specifics concerning various metrics for data collection, such as blood glucose level, high/low blood pressure, high cholesterol, oxygen level, and so on, are being monitored. Diabetic patients are tracked via mobile cloud-IoT and certain wearable health tracking devices and sensors. Doctors will review the individuals' medical records and make recommendations for improving their health. In the future, it will aid in the control or recovery of diabetics. To provide improved security and performance, the proposed system can leverage advanced encryption techniques in conjunction with a machine learning classifier. Keyword: Predictive analytics; Prediction models; Machine learning; Classifications, Healthcare, Diabetes, Blood Glucose and privacy module.

Electrical behavior investigation of sewn textile transmission paths on weft-knitted fabrics used for muscle activity monitoring

In mobile health monitoring systems, particularly wearable electronic systems (WES), bio-signals measuring tools embedded in clothing becomes a viable alternative. Such an alternative is usually designed using flexible conductive media that can capture and transmit bio-signals while maintaining signal reliability and biocompatibility. In addition, WES products are effectuated by the emergence of conductive threads as an appropriate electrical medium for health monitoring through wearable garments. To accomplish this task, conductive threads are emerging as an appropriate electrical medium for bio-signals monitoring. Therefore, the conductive thread is to be considered as a key factor in designing and manufacturing textile-based electronics. However, the behavior of conductive threads under different sewing parameters has been studied to obtain high-quality bio-signals. The article herein the impact of electrically conductive properties of specific conductive threads under three conditions was studied; (a) as sewn configurations on different types of weft-knitted fabric substrates, (b) as sewn configurations with different stitch types, and (c) as working independent threads under various parameters of the sewing process. The purpose of this study is an attempt to design and attach conductive paths to the surface of different fabrics, integrating wearable sensors and electronic elements with those paths. In addition, our research work could be considered as a comprehensive study to analyze both of electrical performance and behavior of the conductive threads under several sewing conditions for different types of weft-knitted fabrics. The expected results can help in developing final products of textile-based electronics to confront the functional and aesthetic performance of consumer’s acceptance. Experimental results show that both sewing and fabric parameters had significantly influenced the electrical resistance of sewn transmission lines (TLs). In addition, the quality of the acquired and transmitted bio-signals has been consequently affected.

Development of an Intelligent Mobile Health Monitoring System for the Health Surveillance System in Indonesia

Mobile phone applications have been widely used in various fields, including health care. Generally, this technology is used to overcome problems in health care by utilising mobile phone features for facilitating basic needs in health services. This study proposes an intelligent mobile health monitoring system that can be used in rural and remote areas where health services are still lacking. The system was made based on client/server architecture. Nine symptoms of typhoid, cough and diarrhoea from 30 patients were gathered from a hospital. Based on this data, a machine learning model using Support Vector Machine (SVM) was performed to distinguish these diseases. To find the best model parameters of the SVM, three different kernels (linear, polynomial, and Radial Basis Function (RBF)) were analysed. The result showed that RBF with degree 2 provided the best result in this particular application. The system was designed to receive input from patients about symptoms of the disease they have. The mobile phone application sends the data of the symptoms using Short Message Service (SMS) to the server. Furthermore, a machine algorithm module in the server identifies to which disease it belongs to based on the machine learning model created before. The prediction result is accessible to the doctor and the nearest Community Health Center (CHC). Based on the result, the doctor proposes a treatment plan for the patient to be recorded and sent to the patient by CHC. The proposed mobile health monitoring system has run properly and is ready to be evaluated in a real situation.

Mobile sensor based human activity recognition: distinguishing of challenging activities by applying long short-term memory deep learning modified by residual network concept.

Automated recognition of daily human tasks is a novel method for continuous monitoring of the health of elderly people. Nowadays mobile devices (i.e. smartphone and smartwatch) are equipped with a variety of sensors, therefore activity classification algorithms have become as useful, low-cost, and non-invasive diagnostic modality to implement as mobile software. The aim of this article is to introduce a new deep learning structure for recognizing challenging (i.e. similar) human activities based on signals which have been recorded by sensors mounted on mobile devices. In the proposed structure, the residual network concept is engaged as a new substructure inside the main proposed structure. This part is responsible to address the problem of accuracy saturation in convolutional neural networks, thanks to its ability in jump over some layers which leads to reducing vanishing gradientseffect. Therefore the accuracy of the classification of several activities is increased by using the proposed structure. Performance of the proposed method is evaluated on real life recorded signals and is compared with existing techniques in two different scenarios. The proposed structure is applied on two well-known human activity datasets that have been prepared in university of Fordham. The first dataset contains the recorded signals which arise from six different activities including walking, jogging, upstairs, downstairs, sitting, and standing. The second dataset also contains walking, jogging, stairs, sitting, standing, eating soup, eating sandwich, and eating chips. In the first scenario, the performance of the proposed structures is compared with deep learning schemes. The obtained results show that the proposed method may improve the recognition rate at least 5% for the first dataset against its own family alternatives in distinguishing challenging activities (i.e. downstairs and upstairs). For the second data set similar improvements is obtained for some challenging activities (i.e. eating sandwich and eating chips). These superiorities even reach to at least 28% when the capability of the proposed method in recognizing downstairs and upstairs is compared to its non-family methods for the first dataset. Increasing the recognition rate of the proposed method for challenging activities (i.e. downstairs and upstairs, eating sandwich and eating chips) in parallel with its acceptable performance for other non-challenging activities shows its effectiveness in mobile sensor-based health monitoring systems.

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

Mobile Health Monitoring System

Now-a-days in-health has become the leading cause of death in worldwide. It has become very hard to find the health conditions in the patients by the medical practitioner as it needs an experience and knowledge about the disease they are dealing with. The diseases can be affected in many parts of the patient’s body. They are many kinds of places in the human body where the different kinds of health problems is affected with; some are cancer, fever, skin problems, disabilities, infections, inner body diseases, outer body diseases, etc.,. In the era of rapid revolution of Internet of Things (IoT), the sensors for monitoring the patients is every way of feeling their each module of visibility of the things which the world is affected for it and their working of the medicine is created through this development of the Internet of Things for the healthcare centers in worldwide. This paper will be explaining about the architecture for the tool used to monitor the patient’s critical levels and algorithms used for monitoring the patient’s health and resolving the problems for the problems they are suffering for. I will be also explaining the other parameters used in the monitoring of things in which shows the activities of human health condition. The algorithm is also used to predict the next symptom of the disease and know it soon by the sensor which tells a sure about the symptom which the host is been affected with and will be seen and cured with help of the cloud data in which the whole information Is been stored about the disease and cure for it which is linked to the single cloud which the information is collected and resolving the problems the problems they have been suffering for it

Fast Pattern Discovery in Healthcare Data Using Graphics Processors

The mobile medical diagnosis and health monitoring system helps in managing the various chronic diseases like asthma, blood pressure and heart diseases etc. in consultation with the remotely available physicians by initiating the emergency call automatically on the physician’s mobile phone and providing the on-line vital medical parameters captured by the body area sensor network of the patient. We observed that a GPU based solution can outperform a CPU based solution by more than 30% in terms of speed up, while giving same accuracy of results, divided among healthy, normal and unhealthy patients. Finally, key parameter to model our health care data likestandard deviations of {1, 0.5, 0.5}, means of {(1, 1), (0, 0), (-1,-1)} are used to study healthy persons and unhealthy patients.
 Keywords: Healthcare ; GPU; EEG; PCG; datastructure

Mobile Health-monitoring System with Inference, Fall Detection, and Cardiovascular Prediction

As the lifetime of human being gets longer, the problems of chronic diseases grow more. In order to make sure the health statuses of patients are not getting worse, they must be health-monitored continuously in a long term. In this paper, a mobile health-monitoring system is built for patients in place of traditional health-caring manners, which not only gives patients more free spaces, but also can save medical resources, diagnose and predict diseases earlier. In the procedures of health-caring in-house and emergency treatment, a series of vital sensors are combined by integrating sensor network and wireless/mobile network technology to continuously transmit physiological signals of patients to a medical center in a real time, and then doctors can monitor the health statuses of patients exactly, thereby proceeding with diagnosing, recovering, and treatments.

(Invited) Imperceptible Electronic Sensor Foils

Electronics of tomorrow will be imperceptible and will form a seamless link between soft, living beings and the digital world. Exploring the fundamental physics, mechanical form factors, and materials required to meet the needs of this new generation of soft electronics is driving multidisciplinary research worldwide. Weight, flexibility and conformability are pivotal for future wearable, soft and stretchable electronics to proliferate. The abilities to be imperceptible, epidermal, transient and self-healing are fueling the vision of autonomous smart appliances to be embedded everywhere, on textiles, on our skin, and even in our body. We here introduce a technology platform for the development of large-area, ultrathin and lightweight electronic and photonic devices, including solar cells[1,2], light emitting diodes[3]and photodetectors[4], active-matrix touch panels[5], implantable organic electronics, imperceptible electronic wraps and “sixth-sense” magnetoception[6]in electronic skins. Air stable perovskite solar cells, only 3 µm thick, endure extreme mechanical deformation and have an unprecedented power output per weight of 23 W/g. Highly flexible, stretchable organic light emitting diodes are combined with photodetectors for on-skin photonics and pulse oximetry. E-skins with GMR-based magnetic field sensors equip the wearer with an unfamiliar sense that enables perceiving of and navigating in magnetic fields. These large area sensor networks build the framework for sensor skins that are not only highly flexible but become highly stretchable when combined with engineered soft materials such as elastomers, shape memory polymers or hydrogels. We show mobile health monitoring systems, smart, tissue-like electronics and soft machines that utilize tough hydrogels[7]as soft robots, generators and adaptive lenses.

Mobile health architecture for obesity management using sensory and social data

Abstract One of the principal causes of several chronic diseases (e.g., diabetes, high cholesterol, and hypertension) is the obesity epidemic in high and middle income countries. Obesity also leads to an increasingly negative effect on public health resources. Therefore, obesity and overweight have to be monitored to mitigate and prevent the potential risks generated from the threat of related diseases and from reducing productivity experienced by businesses. A mobile-health monitoring system includes sensing, transmitting, storing, processing, and analyzing intensive, continuous, and heterogeneous medical data. However, current approaches are standalone mobile applications, augmented mobile applications, or mobile health systems. These approaches only consider simple activities (assess, detect, or control obesity) and rely on a mobile phone to perform complex processing operations on the collected data. Such complex operations need (1) efficient data mining techniques, (2) more memory consumption and processing time, and (3) long life mobile battery. In this work, we develop a new comprehensive mobile architecture for tackling the challenging issues of obesity control, monitoring, and prevention. We introduce a set of business requirements considering stakeholders, sensor devices, and architecture requirements to meet our architecture's objectives. Our architecture system can also help individuals track food intake, lifestyle, calories intake, calories consumption, and exercise activities. We analyze the data collected from continuous monitoring using non-invasive sensors, in addition to the data collected from social communities created to propagate awareness and share appropriate information about the obesity problem and its solution. We develop data mining algorithms and sentiment analysis algorithms and generate intelligent suggestions, warnings, and recommendations to control and mitigate the risk of obesity and its related diseases. We develop schemes for reducing data and saving energy, which minimize the amount of network traffic within the community of sensors. Moreover, we totally implement our architecture system as a collection of Web services organized by the model–view–controller design pattern to write, retrieve, and access data to and from the cloud storage firebase. We finally evaluate the efficacy and scalability of the implemented system using a comprehensive cloud database including entered data, calculated data, sensory data, and social data of 50 underweight, overweight, normal, and obese volunteer subjects. The obtained results show our architecture's objectives are fulfilled.

Design and Development of Treadmill Controller with Wireless Mobile Health Monitoring System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Effects of stitch classes on the electrical properties of conductive threads

Bio-physical signal measurement tools embedded in clothing are becoming a viable alternative in mobile health monitoring systems, particularly Wearable Electronic Textile-based Systems (WETS). To assure clinical viability, utilizing flexible and inconspicuous conductive media that can acquire and transmit reliable signals while assuring signal durability and biocompatibility are particularly important when developing WETS for medical applications. To accomplish this task, conductive threads are emerging as an appropriate electrical medium for health monitoring garments. However, little has been studied on the behavior of these conductive threads under various conditions. We report here the electrical conductive properties of specific conductive threads under two conditions: (i) as sewn configurations onto a textile substrate with different stitch types and (ii) as independent strands under controlled extension independent from a sewing machine. Statistical results showed that the stitch class and thread location significantly influenced the electrical resistance of the conductive thread, revealing the chain stitch to provide resistance even lower than the un-stitched conductive thread. In addition, under controlled extension all three of the conductive threads exhibited both a hysteresis and a stress-relaxation effect. These are important phenomena to examine when conductive threads are incorporated into WETS because the choice of stitch type will influence the strength of the signals received and transmitted, while the wearers’ body movements will cause the threads to encounter multi-axial stretch. Knowing the influence of stitch type, stretch, and relaxation on conductive thread resistance will inform objective design and manufacturing decisions for developing clinical-grade textile-based electrical circuits for medical applications.

New algorithms for processing time-series big EEG data within mobile health monitoring systems

Background and objectivesRecent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. MethodsIn this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. ResultsWe tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. ConclusionsOur approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.