Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network's generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.

Prediction of gestational diabetes based on explainable deep learning and fog computing

Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However, early prediction is preferable because it may decrease the risk. The continuous monitoring of the mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this research is to provide a comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers, which are: (i) Internet of things (IoT) Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IoT sensors to aggregate vital signs from pregnancies using invasive and non-invasive sensors. The vital signs are then transmitted to fog nodes to be processed and finally stored in the cloud layer. The main contribution in this research is located in the fog layer producing the GDM module to implement two influential tasks which are as follows: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free up the cache space for new incoming data items. The cache replacement is very important in the case of the healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the occurrence of GDM in the second trimester of the pregnancy. To evaluate our model, we extracted data from 16,354 pregnant women from the medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data, and laboratory tests were aggregated. The results of the prediction model are superior to the state-of-the-art (ACC = 0.957, AUC = 0.942). Regarding explainability, we used Shapley additive explanation (SHAP) framework to provide local and global explanations for the developed models. Overall, the proposed framework is medically intuitive and allows the early prediction of GDM with a cost-effective solution.

Novel ICT System for Recycling and Eco-Shopping

Recently, there has been a growing effort to reduce the environmental impact of products throughout their life cycle, particularly during the end-of-life (EoL) stage. To incentivise consumers’ recycling/reusing behaviours and enhance their environmental awareness, a novel ICT-based system for recycling and eco-shopping has been developed in this paper. The recycling of EoL products is conducted based on information-communication technologies to remotely monitor and manage the recycled products (such as electronics or household bio-wastes), enabling consumers’ recycling process over the Internet. Consumers are awarded the eco-credits, which can be used for various forms of eco-incentives, such as shopping discounts, tree planting donations, and exchanges for theatre and museum tickets. The eco-costs reflect the environmental impact of a product throughout its life cycle. The consumer is informed about the eco-costs through eco-shopping, which are displayed on a payment receipt. Both eco-costs and eco-credits are recorded in the consumer’s eco-account. To develop the recycling and eco-shopping system, multiple information-communication technologies are utilised, such as hardware digital monitoring/control, Internet-based communication services, traceability media (bar-code and QR code), user identity recognition and privacy protection, and multi-language supports. A case study is conducted, including online tracking of the recycling process and then implementing incentive activities with the eco-credits and eco-costs. The system has been successfully validated via illustrating recycling, eco-shopping, and eco-incentives in public places (e.g., schools, urban cultural centres), as well as promoting the consumer’s participation in recycling and enhancing their environmental awareness, which proved the successful implementation of the novel contribution of this research.

Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

Construction of a Tangible VR-Based Interactive System for Intergenerational Learning

The recent years have witnessed striking global demographic shifts. Retired elderly people often stay home, seldom communicate with their grandchildren, and fail to acquire new knowledge or pass on their experiences. In this study, digital technologies based on virtual reality (VR) with tangible user interfaces (TUIs) were introduced into the design of a novel interactive system for intergenerational learning, aimed at promoting the elderly people’s interactions with younger generations. Initially, the literature was reviewed and experts were interviewed to derive the relevant design principles. The system was constructed accordingly using gesture detection, sound sensing, and VR techniques, and was used to play animation games that simulated traditional puppetry. The system was evaluated statistically by SPSS and AMOS according to the scales of global perceptions of intergenerational communication and the elderly’s attitude via questionnaire surveys, as well as interviews with participants who had experienced the system. Based on the evaluation results and some discussions on the participants’ comments, the following conclusions about the system effectiveness were drawn: (1) intergenerational learning activities based on digital technology can attract younger generations; (2) selecting game topics familiar to the elderly in the learning process encourages them to experience technology; and (3) both generations are more likely to understand each other as a result of joint learning.

Location Based Indoor and Outdoor Lightweight Activity Recognition System

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure.

The Internet of Things in Geriatric Healthcare

There is a significant increase in the geriatric population across the globe. With the increase in the number of geriatric people and their associated health issues, the need for larger healthcare resources is inevitable. Because of this, healthcare service-providing industries are facing a severe challenge. However, technological advancement in recent years has enabled researchers to develop intelligent devices to deal with the scarcity of healthcare resources. In this regard, the Internet of things (IoT) technology has been a boon for healthcare services industries. It not only allows the monitoring of the health parameters of geriatric patients from a remote location but also lets them live an independent life in a cost-efficient way. The current paper provides up-to-date comprehensive knowledge of IoT-based technologies for geriatric healthcare applications. The study also discusses the current trends, issues, challenges, and future scope of research in the area of geriatric healthcare using IoT technology. Information provided in this paper will be helpful to develop futuristic solutions and provide efficient cost-effective healthcare services to the needy.

Overview and Exploitation of Haptic Tele-Weight Device in Virtual Shopping Stores

In view of the problem of e-commerce scams and the absence of haptic interaction, this research aims to introduce and create a tele-weight device for e-commerce shopping in smart cities. The objective is to use the proposed prototype to provide a brief overview of the possible technological advancements. When the tele-weight device is affixed over the head-mounted display, it allows the user to feel the item’s weight while shopping in the virtual store. Addressing the problem of having no physical interaction between the user (player) and a series game scene in virtual reality (VR) headsets, this research approach focuses on creating a prototype device that has two parts, a sending part and a receiving part. The sending part measures the weight of the object and transmits it over the cellular network to the receiver side. The virtual store user at the receiving side can thus realize the weight of the ordered object. The findings from this work include a visual display of the item’s weight to the virtual store e-commerce user. By introducing sustainability, this haptic technology-assisted technique can help the customer realize the weight of an object and thus have a better immersive experience. In the device, the load cell measures the weight of the object and amplifies it using the HX711 amplifier. However, some delay in the demonstration of the weight was observed during experimentation, and this indirectly altered the performance of the system. One set of the device is sited at the virtual store user premises while the sending end of the device is positioned at the warehouse. The sending end hardware includes an Arduino Uno device, an HX711 amplifier chip to amplify the weight from the load cell, and a cellular module (Sim900A chip-based) to transmit the weight in the form of an encoded message. The receiving end hardware includes a cellular module and an actuator involving a motor gear arrangement to demonstrate the weight of the object. Combining the fields of e-commerce, embedded systems, VR, and haptic sensing, this research can help create a more secure marketplace to attain a higher level of customer satisfaction.