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Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern... more
Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat peopl...
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Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and... more
Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyz...
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Research Interests: Computer Science and IPV
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Over the past decades, the application of computer science and engineering principles to the field of Human-Computer Interaction (HCI) has proven to be of paramount importance, leading to remarkable technical, methodological and... more
Over the past decades, the application of computer science and engineering principles to the field of Human-Computer Interaction (HCI) has proven to be of paramount importance, leading to remarkable technical, methodological and scientific achievements (Stephen, 2009). Biomedical signals are a particularly popular topic within the global research community. Recent examples spread through academia and industry, including initiatives such as the brain-computer interface (BCI) research program at Facebook Reality Labs (Tekla, 2020), the heart rate enhanced communication being explored by Snap, Inc. Research (Liu et al., 2021), or the work of Yamaha Corporation and world-renowned dancer Kaiji Moriyama on the use of Electromyography (EMG) to control a piano (Yamaha Corporation, 2018). Nowadays, biomedical signals applied to HCI drive researchers in areas ranging from computer science and electrical engineering to social sciences, being one of the areas with high potential to enhance the ...
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Context: Ethics have broad applications in different fields of study and different contexts. Like other fields of study, ethics have a significant impact on the decisions made in computing concerning software artifact production and its... more
Context: Ethics have broad applications in different fields of study and different contexts. Like other fields of study, ethics have a significant impact on the decisions made in computing concerning software artifact production and its processes. Hence, in this research, ethics is considered in the context of requirements engineering during the software development process. Objective: The aim of this paper is to discuss the investigation results regarding ethical problems of requirements engineering processes by taking sample software developing companies and exposing existing research gaps. Method: This research uses interviewing, focus group discussions, purposive sampling, and qualitative analysis research methods. Result: This research finds an absence of industry practices, professional responsibility code of conduct standards, and other guidelines within companies when integrating ethical concerns of software during requirements engineering. It also indicates that almost all ...
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This paper presents a PhD project related to the use of multi-sensor data fusion techniques, applied to the sensors embedded in mobile devices, as a mean to identify user’s daily activities. It introduces some basic concepts, such as the... more
This paper presents a PhD project related to the use of multi-sensor data fusion techniques, applied to the sensors embedded in mobile devices, as a mean to identify user’s daily activities. It introduces some basic concepts, such as the definition of activities of daily living, mobile platforms/sensors, multisensor technologies, data fusion, and data imputation. These techniques have already been applied to fuse the data acquired with different sensors, but due to memory constraints, battery life and processing power of these devices, not all the techniques are suited to be used in these environments. This paper explains an overview about the state of the research in this topic, explaining the methodology to create a best effort method to recognize a large number of activities of daily living using a mobile device.
Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s... more
Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable ...
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The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data... more
The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost re...
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The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the... more
The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Network...
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The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with... more
The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with reliable accuracy. This paper focuses on the literature review of the existing methods to make the identification of ADL in order to assess the efficiency of the different methods for the identification of ADL and their environments using off-the-shelf mobile devices. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment. Therefore, the main purpose of this study is to present a review of the machine learning methods already used on this field, relating them with the accuracy and number of ADL recognized.
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Background:Mobile applications can be used for the monitoring of lifestyles and physical activity. It can be installed in commodity mobile devices, which are currently used by different types of people in their daily activities worlwide... more
Background:Mobile applications can be used for the monitoring of lifestyles and physical activity. It can be installed in commodity mobile devices, which are currently used by different types of people in their daily activities worlwide .Objective:This paper reviews and categorizes the mobile applications related to diet, nutrition, health, physical activity and education, showing the analysis of 73 mobile applications available on Google Play Store with the extraction of the different features.Methods:The mobile applications were analyzed in relation to each proposed category and their features, starting with the definition of the search keywords used in the Google Play Store. Each mobile application was installed on a smartphone, and validated whether it was researched in scientific studies. Finally, all mobile applications and features were categorized.Results:These mobile applications were clustered into four groups, including diet and nutrition, health, physical activity and ed...
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Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors,... more
Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors’ data, repor...
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Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the... more
Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors.Objective:This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user.Methods:T...
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Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the... more
Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.
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The use of wearable technologies in medicine and health care has become of important in order to considerably improve benefits for patients and health service providers. Within telemedicine, biomedical clothing plays a crucial role. The... more
The use of wearable technologies in medicine and health care has become of important in order to considerably improve benefits for patients and health service providers. Within telemedicine, biomedical clothing plays a crucial role. The main technology advances and the research of the Textile and Paper Materials Research Unit (UMTP) and of the Assisted Living Computing and Telecommunications Laboratory (ALLab) teams, in the area, will be addressed. Issues that remain unsolved will be presented. The chapter presents an overview of the key concepts for telemedicine and the role of textile electrodes and their integration in smart clothing. The development of software algorithms that specifically handle signals that are collected using biomedical clothing, integrating resiliency and a proper set of alarms, is presented and discussed in the context of classical biomedical signal processing. Finally, biomedical clothing design will be discussed in social, psychological, and esthetical co...
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The convergence of two powerful technologieswireless and the Internetthrough IPv4/v6 protocol has led to emergence of next-generation networks (NGNs). NGN is no more a network of mere computers but a connected conglomeration of varied... more
The convergence of two powerful technologieswireless and the Internetthrough IPv4/v6 protocol has led to emergence of next-generation networks (NGNs). NGN is no more a network of mere computers but a connected conglomeration of varied networks with diverse physical properties, with a plethora of network elements, along with a variety of real-time multimedia applications. This book covers the entire gamut of technology challenges from physical layer to application layer including security from both academic and industrial perspectives.
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The ubiquity of mobile devices and the pervasive Internet raised a new paradigm in care models, based more on contacts than on visits. However, the effects of assistive computerised systems on practitioners and patients remain... more
The ubiquity of mobile devices and the pervasive Internet raised a new paradigm in care models, based more on contacts than on visits. However, the effects of assistive computerised systems on practitioners and patients remain understudied, and their promise of increasing self-care, acceptability, and accuracy of healthcare monitoring mostly untested. Similarly, evidence remains controversial concerning the effectiveness of providing support to caregivers of Alzheimer’s disease patients, through technological devices integrated with existing care services. This paper aims to contribute further results and evidences gained from experimental trials, related to two different solutions: an electronic pain monitoring system, and a technological kit to support Alzheimer’s patients caregivers. The positive outcomes suggest to further extend similar studies, to better clarify the role and realistic expectations on the use of ICT in healthcare.
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The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous... more
The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolate...
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The User Datagram Protocol (UDP) and other similar protocols send application data from the source to the destination machine inside segments, without foreseeing for any type of control on the transmission or success metrics. These... more
The User Datagram Protocol (UDP) and other similar protocols send application data from the source to the destination machine inside segments, without foreseeing for any type of control on the transmission or success metrics. These protocols are very convenient for real time transmission. To sustain the increased functionality and features of the connection-oriented protocol, a set of mechanisms is implemented based on some specific fields of the segment header. These mechanisms result in a significant overhead in terms of the increased number of transmitted packets. This may further translate into significant delays, because of the additional number of switching and routing tasks, and eventually, because of more complex communications procedures, such as e.g. transmission window resizing, and of course, acknowledgement and sequence numbers updating. The two extremes of these communication modalities, one that has no control at all, and the other one that allows for full control, ha...
Cloud computing recently offers immense benefits. But higher education institutions (HEIs) are hesitating to adopt. As a result, they are not benefited more from such technology. This is because key adoption capabilities are not explored... more
Cloud computing recently offers immense benefits. But higher education institutions (HEIs) are hesitating to adopt. As a result, they are not benefited more from such technology. This is because key adoption capabilities are not explored in detail in HEIs context. In addition to this, there is no comprehensive empirically validated adoption readiness assessment model which helps them to adopt Cloud solutions efficiently. Hence, to fill this gap this study identifies the critical capabilities and develops a model to assess Cloud computing adoption readiness of higher education institutions before initiating the adoption. Resource based view theory was used as theoretical lens to identify capabilities and to build the model. The conceptual model considers adoption readiness and adoption success from technological, human, organizational, financial and external environment dimensions. The model proposes that the degree to which an organization holds these capabilities is related to the ...
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
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Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition,... more
Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.
The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative... more
The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests’ duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, durati...
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Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous... more
Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innov...
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Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare... more
Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.
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Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision... more
Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost t...
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Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to... more
Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of...
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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by... more
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT contex...
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In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple... more
In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging technologies, objective and reliable techniques provide qualitative information that can be further processed to provide quantitative information on the size, structure, and color characteristics of wounds. These efficient image analysis algorithms help determine the injury features and the progress of healing in a short time. This paper presents a systematic investigation of articles that specifically address the measurement of wounds’ sizes with image processing techniques, promoting the connection between computer science and health. Of the 208 studies identified by searching electronic databases, 20 were included in the review. From the perspec...
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The recent developments in electronic and communication technologies have brought notable revolution in the e-healthcare industry for efficient transmission of the patient's data. One of the emergent applications of telehealth... more
The recent developments in electronic and communication technologies have brought notable revolution in the e-healthcare industry for efficient transmission of the patient's data. One of the emergent applications of telehealth monitoring is the Internet of medical things (IoMTs). They are used to transfer and monitor medical information in patient-centred systems. Patient's data is very critical, so its secure transmission is of paramount requirement in smart healthcare applications. The current era has witnessed the large-scale usage of cryptographic and biometric systems, and machine learning (ML) approaches for authentication and anomaly detection, respectively, for securing medical systems. In IoMTs, sensor devices have limited power and battery, so to provide a balance between security and resource-efficiency is also an important aspect to consider during deploying IoMT. Therefore, this research aims to present an innovate framework to protect medical information from external threats with the consumption of less possible resources of low-powered medical devices. In this study, the ML-based biometric security framework is proposed in which features are extracted from Electrocardiogram (ECG) signals for the training phase. However, in the testing phase, the user authentication will be verified by utilizing generated unique biometric EIs from the ECG and acquired coefficients from polynomial approximation. The proposed framework has got the scientific as well as economic significance; thus, it could be used for real-time healthcare applications.