Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment.

Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.

Prediction of Heart Disease Using Deep Convolutional Neural Networks

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

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Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.

Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning

In this paper, we first build up an electroencephalogram (EEG)-based driving fatigue detection system, and then propose a subject transfer framework for this system via component analysis. We apply a subspace projecting approach called transfer component analysis (TCA) for subject transfer. The main idea is to learn a set of transfer components underlying source domain (source subjects) and target domain (target subjects). When projected to this subspace, the difference of feature distributions of both domains can be reduced. Meanwhile, the discriminative information can be preserved. From the experiments, we show that the TCA-based algorithm can achieve a significant improvement on performance with the best mean accuracy of 77.56 %, in comparison of the baseline accuracy of 66.56 %. The improvement shows the feasibility and efficiency of our approach for subject transfer driving fatigue detection from EEG.

Transfer Components Between Subjects for EEG-based Driving Fatigue Detection

Nonwoven fabrics are widely used in the textile manufacturing industry due to their advanced characteristics, such as their soft, water-repellent, recycle, ecological, and resilient functions. Nowadays, one of the innovated technologies applied to bond nonwoven fabrics is the ultrasonic welding method, due to the advantages afforded by its clean, fast, and reliable approach. In this work, isotactic polypropylene (PP) nonwoven fabrics were bonded by a continuous ultrasonic welding process. In order to consider the influence of the roller on the formation of welding joints and their mechanical properties, different roller profiles were designed, fabricated, and tested. Eight types of roller profiles corresponding to No. 1–No. 8 in the experiments were divided into four groups. After bonding, the microstructure in a typical case (i.e., No. 1) was captured by scanning electron microscopy (SEM) to examine the formation of the welding joints. Additionally, the load and the peel strength of the welding joints of all eight roller profiles were analyzed. The results showed that no welding defects, such as cracks or blowholes, were visible in the melted zone. The load depended on the area ratio(s) of the welding area (S0) to the cycling area (S1). Furthermore, it was found out that the peel strength of the welding joints with brick structures were higher than the peel strength in the case of solid line structures.

Effects of Different Roller Profiles on the Microstructure and Peel Strength of the Ultrasonic Welding Joints of Nonwoven Fabrics

Accurate classification of heart diseases plays an important role and IoTs applied in a medical system will increase the effectiveness of diagnosis In this chapter, we propose an IoTs-based diagnostic system for heart diseases classification This system is designed to transmit classified data to server for storage and diagnosis In particular, ECG devices are connected to internet systems through wifi or 3G/4G technologies for transmitting ECG data to a cloud-based processing system for storing patient’s profiles Therefore, datasets are pre-processed for extracting features using a WPD algorithm In addition, a wkPCA method and a deep learning framework are employed for classifying heart diseases Experimental results and the IoTs-based system description are shown to illustrate the effectiveness of the proposed method

A Deep Learning Framework for Heart Disease Classification in an IoTs-Based System

In this study, the effect of oxygen in the shielding gas on the material flow behavior of the weld pool surface was discussed to clarify the dominant driving weld pool force in keyhole plasma arc welding (KPAW). To address this issue, the convection flow on the top surface of weld pool was observed using a high-speed video camera. The temperature distribution on the surface along keyhole wall was measured using the two-color pyrometry method to confirm the Marangoni force activity on the weld pool. The results show that the inclination angle of the keyhole wall (keyhole shape) increased especially near the top surface due to the decrease in the surface tension of weld pool through surface oxidation when a shielding gas of Ar + 0.5% O2 was used. Due to the change in the keyhole shape, the upward and backward shear force compositions created a large inclination angle at the top surface of the keyhole. From the temperature measurement results, the Marangoni force was found to alter the direction when 0.5% O2 was mixed with the shielding gas. The shear force was found to be the strongest force among the four driving forces. The buoyant force and Lorentz force were very weak. The Marangoni force was stronger than the Lorentz force but was weaker than shear force. The interaction of shear force and Marangoni force controlled the behavior and speed of material flow on the weld pool surface. A strong upward and backward flow was observed in the case of mixture shielding gas, whereas a weak upward flow was observed for pure Ar. The heat transportation due to the weld pool convection significantly changed when only a small amount of oxygen was admixed in the shielding gas. The results can be applied to control the penetration ratio in KPAW.

Material Flow Behavior on Weld Pool Surface in Plasma Arc Welding Process Considering Dominant Driving Forces

Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.

Artifact elimination in ECG signal using wavelet transform

In the injection molding field, the flow of plastic material is one of the most important issues, especially regarding the ability of melted plastic to fill the thin walls of products. To improve the melt flow length, a high mold temperature was applied with pre-heating of the cavity surface. In this paper, we present our research on the injection molding process with pre-heating by external gas-assisted mold temperature control. After this, we observed an improvement in the melt flow length into thin-walled products due to the high mold temperature during the filling step. In addition, to develop the heating efficiency, a flow focusing device (FFD) was applied and verified. The simulations and experiments were carried out within an air temperature of 400 °C and heating time of 20 s to investigate a flow focusing device to assist with external gas-assisted mold temperature control (Ex-GMTC), with the application of various FFD types for the temperature distribution of the insert plate. The heating process was applied for a simple insert model with dimensions of 50 mm × 50 mm × 2 mm, in order to verify the influence of the FFD geometry on the heating result. After that, Ex-GMTC with the assistance of FFD was carried out for a mold-reading process, and the FFD influence was estimated by the mold heating result and the improvement of the melt flow length using acrylonitrile butadiene styrene (ABS). The results show that the air sprue gap (h) significantly affects the temperature of the insert and an air sprue gap of 3 mm gives the best heating rate, with the highest temperature being 321.2 °C. Likewise, the actual results show that the height of the flow focusing device (V) also influences the temperature of the insert plate and that a 5 mm high FFD gives the best results with a maximum temperature of 332.3 °C. Moreover, the heating efficiency when using FFD is always higher than without FFD. After examining the effect of FFD, its application was considered, in order to improve the melt flow length in injection molding, which increased from 38.6 to 170 mm, while the balance of the melt filling was also clearly improved.

Thanh-Hai Nguyen

Papers

Effects of Different Roller Profiles on the Microstructure and Peel Strength of the Ultrasonic Welding Joints of Nonwoven Fabrics

A Deep Learning Framework for Heart Disease Classification in an IoTs-Based System

Material Flow Behavior on Weld Pool Surface in Plasma Arc Welding Process Considering Dominant Driving Forces

Artifact elimination in ECG signal using wavelet transform

Study on External Gas-Assisted Mold Temperature Control with the Assistance of a Flow Focusing Device in the Injection Molding Process.