Cloud support for large scale e-healthcare systems

Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals’ health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a set of specialists namely, hospitals, healthcare agencies and physicians will take care of such health information. Real-time or semi-real time health information are used for online monitoring of patients at home. This in fact enables the doctors and specialists to provide immediate medical treatments. Large scale e-healthcare systems aim at extending the monitoring coverage from individuals to include a crowd of people who live in communities, cities, or even up to a whole country. In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a wide geographical area. The system is efficiently integrating many emerging technologies such as mobile computing, edge computing, wearable sensors, cloud computing, big data techniques, and decision support systems. It can offer remote monitoring of patients anytime and anywhere in a timely manner. The system also features some unique functions that are of great importance for patients’ health as well as for societies, cities, and countries. These unique features are characterized by taking long-term, proactive, and intelligent decisions for expected risks that might arise by detecting abnormal health patterns shown after analyzing huge amounts of patients’ data. Furthermore, it is using a set of supportive information to enhance the decision support system outcome. A rigorous set of evaluation experiments are conducted and presented to validate the efficiency of the proposed model. The obtained results show that the proposed model is scalable by handling a large number of monitored individuals with minimal overhead. Moreover, exploiting the cloud-based system reduces both the resources consumption and the delay overhead for each individual patient.     

User centric three‐factor authentication protocol for cloud‐assisted wearable devices

Wearable devices, which provide the services of collecting personal data, monitoring health conditions, and so on, are widely used in many fields, ranging from sports to healthcare. Although wearable devices bring convenience to people's lives, they bring about significant security concerns, such as personal privacy disclosure and unauthorized access to wearable devices. To ensure the privacy and security of the sensitive data, it is critical to design an efficient authentication protocol suitable for wearable devices. Recently, Das et al proposed a lightweight authentication protocol, which achieves secure communication between the wearable device and the mobile terminal. However, we find that their protocol is vulnerable to offline password guessing attack and desynchronization attack. Therefore, we put forward a user centric three‐factor authentication scheme for wearable devices assisted by cloud server. Informal security analysis and formal analysis using ProVerif is executed to demonstrate that our protocol not only remedies the flaws of the protocol of Das et al but also meets desired security properties. Comparison with related schemes shows that our protocol satisfies security and usability simultaneously.     

Audio-Visual Emotion Recognition Using Big Data Towards 5G

With the advent of future generation mobile communication technologies (5G), there is the potential to allow mobile users to have access to big data processing over different clouds and networks. The increasing numbers of mobile users come with additional expectations for personalized services (e.g., social networking, smart home, health monitoring) at any time, from anywhere, and through any means of connectivity. Because of the expected massive amount of complex data generated by such services and networks from heterogeneous multiple sources, an infrastructure is required to recognize a user’s sentiments (e.g., emotion) and behavioral patterns to provide a high quality mobile user experience. To this end, this paper proposes an infrastructure that combines the potential of emotion-aware big data and cloud technology towards 5G. With this proposed infrastructure, a bimodal system of big data emotion recognition is proposed, where the modalities consist of speech and face video. Experimental results show that the proposed approach achieves 83.10 % emotion recognition accuracy using bimodal inputs. To show the suitability and validity of the proposed approach, Hadoop-based distributed processing is used to speed up the processing for heterogeneous mobile clients.     

A web service‐based internet of things framework for mobile resource augmentation

Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.     

基于智能感知的物联网云平台探究

近年来,智能设备获得了迅猛的发展,且广泛出现在人们的日常生活中,小到音影娱乐、智能家居以及穿戴设备,大到警务应用、建筑监测、交通监管控制以及农业生产等。首先介绍了系统的核心特征,然后探究了智能感知下的物联网云平台设计策略,最后提出了系统应用场景。     

Pervasive, secure access to a hierarchical sensor-based healthcare monitoring architecture in wireless heterogeneous networks

This study presents a healthcare monitoring architecture coupled with wearable sensor systems and an environmental sensor network for monitoring elderly or chronic patients in their residence. The wearable sensor system, built into a fabric belt, consists of various medical sensors that collect a timely set of physiological health indicators transmitted via low energy wireless communication to mobile computing devices. Three application scenarios are implemented using the proposed network architecture. The group-based data collection and data transmission using the ad hoc mode promote outpatient healthcare services for only one medical staff member assigned to a set of patients. Adaptive security issues for data transmission are performed based on different wireless capabilities. This study also presents a monitoring application prototype for capturing sensor data from wireless sensor nodes. The implemented schemes were verified as performing efficiently and rapidly in the proposed network architecture.     

A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.      

Power-Free Contact Lens for Glucose Sensing

Recent advances in wearable devices have enabled noninvasive monitoring for healthcare applications. Smart contact lenses have gained substantial attention for medical diagnosis through the analysis of vital signs in tear fluids. However, previous studies have mostly focused on designs embedded with electronic devices or antennas for wireless transmission, which are power-intensive and require external receivers around the ocular system. Here, the study reports a power-free smart contact lens for noninvasive glucose sensing according to the color changes of multiple electrochromic electrodes to achieve direct data transmission without the external wireless system. The device detects various glucose concentrations, from the ordinary range (0.16–0.5 mm ) to abnormally high concentrations (0.9 mm ). The multi-electrode design exhibits acceptable accuracy, with a correlation coefficient r = 0.99543 to the controlled sample and allowed low-glucose detections with concentrations down to 0.05 mm . The device shows good reproducibility, with standard deviations of determined glucose levels of 0.0462 and 0.025 for four continuous cycles and for an interval of several days, respectively. It is believed that the reported smart contact lens has the potential for daily health monitoring by ordinary users without a power supply and external devices. Its simple electronics-free structure will allow for immediate application to the market with cost-effective manufacturing.     

Nonwoven fabric active electrodes for biopotential measurement during normal daily activity

Body movement is responsible for most of the interference during physiological data acquisition during normal daily activities. In this paper, we introduce nonwoven fabric active electrodes that provide the comfort required for clothing while robustly recording physiological data in the presence of body movement. The nonwoven fabric active electrodes were designed and fabricated using both hand- and screen-printing thick-film techniques. Nonstretchable nonwoven (Evolon 100) was chosen as the flexible fabric substrate and a silver filled polymer ink (Creative Materials CMI 112-15) was used to form a transducer layer and conductive lines on the nonwoven fabrics. These nonwoven fabric active electrodes can be easily integrated into clothing for wearable health monitoring applications. Test results indicate that nonwoven textile-based sensors show considerable promise for physiological data acquisition in wearable healthcare monitoring applications.     

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

     

Cloud-Assisted Mood Fatigue Detection System

This paper introduces basic concept of mood fatigue detection and existing solutions as well as some typical solutions, such as mobile sensing and cloud computing technology. In the first place, we sum up main challenges of mood fatigue detection and the direction of future study. Then one type of system implementation is put forward, such system consists of: 1) Wearable devices used to detect the users’ mood fatigue; 2) Clouds data center; 3) Application and service manager. We take overall consideration of many factors like the user’s physiological information, external environment conditions and user behavior, evaluate accurately through big data analytic technology the users’ state of mood fatigue and to what extent shall one keeps vigilant as well as what measures shall be adopted to improve the users’ working performance and alert the users to keep themselves away from the danger. Finally a real system is established in this paper, it is composed of the smart clothing, cloud platform and mobile terminal application.     

Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare

Activity recognition plays an important role for pervasive healthcare such as health monitoring, assisted living and pro-active services. Despite of the continuous and transparent sensing with various built-in sensors in mobile devices, activity recognition on mobile devices for pervasive healthcare is still a challenge due to the constraint of resources, such as battery limitation, computation workload, etc. Keeping in view the demand of energy-efficient activity recognition, we propose a hierarchical method to recognize user activities based on a single tri-axial accelerometer in smart phones for health monitoring. Specifically, the contribution of this paper is two-fold. First, it is demonstrated that the activity recognition based on the low sampling frequency is feasible for the long-term activity monitoring. Second, this paper presents a hierarchical recognition scheme. The proposed algorithm reduces the opportunity of usage of time-consuming frequency-domain features and adjusts the size of sliding window to improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm, with more than 85 % recognition accuracy rate for 11 activities and 3.2 h extended battery life for mobile phones. Our energy efficient recognition algorithm extends the battery time for activity recognition on mobile devices and contributes to the health monitoring for pervasive healthcare.     

Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention.

Improvement of the quality and efficiency of healthcare in medicine, both at home and in hospital, is becoming more and more important for patients and society at large. As many technologies (micro technologies, telecommunication, low-power design, new textiles, and flexible sensors) are now available, new user-friendly devices can be developed to enhance the comfort and security of the patient. As clothes and textiles are in direct contact with about 90% of the skin surface, smart sensors and smart clothes with noninvasive sensors are an attractive solution for home-based and ambulatory health monitoring. Moreover, wearable devices or smart homes with exosensors are also potential solutions. All these systems can provide a safe and comfortable environment for home healthcare, illness prevention, and citizen medicine.     

The Era of Digital Health: A Review of Portable and Wearable Affinity Biosensors

Digital health facilitated by wearable/portable electronics and big data analytics holds great potential in empowering patients with real‐time diagnostics tools and information. The detection of a majority of biomarkers at trace levels in body fluids using mobile health (mHealth) devices requires bioaffinity sensors that rely on “bioreceptors” for specific recognition. Portable point‐of‐care testing (POCT) bioaffinity sensors have demonstrated their broad utility for diverse applications ranging from health monitoring to disease diagnosis and management. In addition, flexible and stretchable electronics‐enabled wearable platforms have emerged in the past decade as an interesting approach in the ambulatory collection of real‐time data. Herein, the technological advancements of mHealth bioaffinity sensors evolved from laboratory assays to portable POCT devices, and to wearable electronics, are synthesized. The involved recognition events in the mHealth affinity biosensors enabled by bioreceptors (e.g., antibodies, DNAs, aptamers, and molecularly imprinted polymers) are discussed along with their transduction mechanisms (e.g., electrochemical and optical) and system‐level integration technologies. Finally, an outlook of the field is provided and key technological bottlenecks to overcome identified, in order to achieve a new sensing paradigm in wearable bioaffinity platforms.     

云计算与大数据技术在智慧医疗中的应用

随着云计算和大数据技术的快速发展,智慧医疗领域正逐渐受益于这些创新技术的应用。文中旨在探讨云计算与大数据技术在智慧医疗中的应用。首先,分析了云计算和大数据技术的基本概念和特点,接着重点探讨了云计算技术在智慧医疗中的应用,如电子病历管理等。然后,详细介绍了大数据技术在智慧医疗中的应用,如数据采集和存储技术、数据分析和挖掘等。最后,提出了云计算与大数据技术在智慧医疗中的应用策略,包括医疗数据安全和隐私保护、医疗资源调配和优化等。通过合理的应用策略,云计算和大数据技术有望在智慧医疗领域发挥更大的作用,提升医疗服务的质量和效率。     

Design and implementation of a wireless sensor network architecture using smart mobile devices

Wireless sensor networks improve the quality of human daily life like ubiquitous city and healthcare services as well as the fundamental monitoring such as environment pollution, tunnel monitoring, earthquake diagnostic, and so on. To increase usability and feasibility of collected sensor data, a wireless sensor network should be required to apply a variety of mobile devices to give the information at anytime and anywhere to users. Thus, we present multi-sensor centric smart sensor network architecture using general mobile devices in order to provide more efficient and valuable sensor network application and services. The proposed system architecture is based on IEEE 802.15.4-2006 standard with smart mobile devices. We also show some scenarios with on-demand request and real time event driven data to show the feasibility of the proposed architecture using five kinds of sensors such as magnetic, photodiode, microphone, motion and vibration. Based on the experiment results, we show that the proposed system has the potential as smart mobile device-based wireless sensor network architecture.     

Wearable,Human‐Interactive,Health‐Monitoring,Wireless Devices Fabricated by Macroscale Printing Techniques

Wearable human‐interactive devices are advanced technologies that will improve the comfort, convenience, and security of humans, and have a wide range of applications from robotics to clinical health monitoring. In this study, a fully printed wearable human‐interactive device called a “smart bandage” is proposed as the first proof of concept. The device incorporates touch and temperature sensors to monitor health, a drug‐delivery system to improve health, and a wireless coil to detect touch. The sensors, microelectromechanical systems (MEMS) structure, and wireless coil are monolithically integrated onto flexible substrates. A smart bandage is demonstrated on a human arm. These types of wearable human‐interactive devices represent a promising platform not only for interactive devices, but also for flexible MEMS technology.     

Wireless Technologies in Healthcare: Selected Papers from IEEE PIMRC 2011

The use of wireless technologies for medical device communication, health monitoring (at hospitals or homes) and mobile healthcare information delivery (i.e. m-Health) is one of the most rapidly growing areas in health-IT research today. The papers which appear in this special issue have been carefully selected from the best IEEE PIMRC 2011 conference. They are highlighting various challenging issues in using wireless technology for healthcare applications such as PHY & MAC innovations for wearable and implantable medical sensors, optical communication and location systems in hospital environments and interference mitigation issues.     

A Dual‐Functional Graphene‐Based Self‐Alarm Health‐Monitoring E‐Skin

Diseases such as cardiovascular problems and sleep apnea cause mass deaths annually due to a lack of timely and portable monitoring and alarm measures. Various wearable devices for health monitoring have been intensely researched to reduce mortality. However, these devices themselves can only detect physiological signals; they cannot sound an alarm. Therefore, they must rely on mobile phones or other peripheral devices such as speakers or vibration motors to sound an alarm, which may result in a patient missing the optimal treatment. It is valuable to develop a self‐alarm health monitoring device with the dual functions of physiological signal detection and sound alarm simultaneously. A one‐step laser‐induced graphene (LIG)‐based electronic skin (E‐skin) is fabricated to perform health monitoring and alarm at the same time, which benefit from its both excellent mechanical and acoustical performance. These customized shutter‐patterned E‐skins have an ultrahigh sensitivity of 316.3 and can detect various biosignals such as wrist pulse, respiratory, etc. They also have a self‐alarm function and can sound an alarm when detecting abnormal situations. This study addresses the multifunctional integration required for multisensors, which will open further applications in wearable sensors and health‐care devices.