More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC-DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power management circuits.

Micropower energy harvesting

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Computational Science and Engineering (CSE) is the multi-disciplinary field of computer-based modelling and simulation for studying scientific phenomena and engineering designs. Modelling and simulation helps to validate theory, and makes it possible to analyse scenarios that would otherwise be too time-consuming, expensive, or dangerous to study by experiment. Data exploration helps to turn numbers into insight which is especially challenging in times of Big Data.

Computational Science and Engineering

Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines

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A study on medical Internet of Things and Big Data in personalized healthcare system

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

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COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

Designing power aware wearable devices is the main key in building compact size autonomous smart devices to successfully connect health Internet of things solutions. With their ability to perform tasks ranging from simple self-monitoring to complex interactive tasks, these devices hold great promises in providing a large scale cost effective solution to the challenges facing nowadays healthcare systems. Despite the advances in sensing and hardware design, there still remain several technical challenges facing the research community to build devices that meet the computational requirements with a self-powered capability. Overcoming these challenges require major improvements in all the building blocks of wearable devices including sensors, power management, signal processing, computing architectures, and communication. This paper surveys some of the past milestones related to these subsystems and discusses promising research directions addressing their limitations.

Empowering Technology Enabled Care Using IoT and Smart Devices: A Review

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s $T^{2}$ statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

This study presents pre-trained Convolutional Neural Network (CNN) models to classify pre-segmented mammogram mass tumors as benign or malignant. Transfer learning, particular pre-processing and data augmentation were applied to overcome the limitation of the scarcity of available training dataset. The proposed models are based on modified versions of Inception V3 and ResNet50 to tackle the classification problem mentioned above. The proposed architectures have been tested on the Digital Database for Screening Mammography (DDSM) dataset, and it achieved an accuracy of 0.796, precision of 0.754, and a recall of 0.891 on InceptionV3-like CNN model. On the other hand, an accuracy of 0.857, precision of 0.857, and a recall rate of 0.873 have been achieved with the ResNet50-like CNN network. Overall, the proposed ResNet50-like model achieved a ~5% improvement in accuracy compared to the existing state-of-the-art method for this dataset.

Breast Mass Tumor Classification using Deep Learning

The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 33 % in the worst case scenario

IoT Based Compressive Sensing for ECG Monitoring

In this letter, we propose a parametric modeling technique for the electrocardiogram (ECG) signal based on signal dependent orthogonal transform. The technique involves the mapping of the ECG heartbeats into the singular values (SV) domain using the left singular vectors matrix of the impulse response matrix of the LPC filter. The resulting spectral coefficients vector would be concentrated, leading to an approximation to a sum of exponentially damped sinusoids (EDS). A two-stage procedure is then used to estimate the model parameters. The Prony's method is first employed to obtain initial estimates of the model, while the Levenberg-Marquardt (LM) method is then applied to solve the non-linear least-square optimization problem. The ECG signal is reconstructed using the EDS parameters and the linear prediction coefficients via the inverse transform. The merit of the proposed modeling technique is illustrated on the clinical data collected from the MIT-BIH database including all the arrhythmias classes that are recommended by the Association for the Advancement of Medical Instrumentation (AAMI). For all the tested ECG heartbeats, the average values of the percent root mean square difference (PRDs) between the actual and the reconstructed signals were relatively low, varying between a minimum of 3.1545% for Premature Ventricular Contractions (PVC) class and a maximum of 10.8152% for Nodal Escape (NE) class.

Hamza Baali

Papers

Empowering Technology Enabled Care Using IoT and Smart Devices: A Review

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

Breast Mass Tumor Classification using Deep Learning

IoT Based Compressive Sensing for ECG Monitoring

ECG Parametric Modeling Based on Signal Dependent Orthogonal Transform