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This research puts forward novel deep hybrid frameworks for assessment of mental health indicators viz. depression and sleep quality using IoT-based motor activity recordings. The study employs long short-term memory (LSTM) to extract high-level features from sequential motor activity data, which are then combined with statistical features of the raw data to form a hybrid feature extraction model. The combined feature vector obtained via the hybrid feature extractor is fed into four regression models, namely linear regression (LR), sequential minimal optimization regression (SMOR), random forest regression (RFR) and adaptive neuro-fuzzy inference system (ANFIS) forming four deep hybrid regression models, namely LSTM-LR, LSTM-SMOR, LSTM-RFR, and LSTM-ANFIS for prediction of depression and sleep quality. The proposed deep hybrid frameworks are validated on benchmark datasets, namely the Depresjon dataset and the MESA actigraphy dataset, and the best performance is observed by LSTM-RFR with the adjusted R2 value of 74.19% on the MESA dataset. On validating the significance of statistical features, it has been observed that all the models show significant improvement in performance on combining the statistical features with high-level features with highest reduction in mean absolute error as 4.0436 and highest increase in R2 statistic as 0.1651. Model building time of the four regression methods is also compared for which LR gave the best results as only 0.01 s for training and testing. [ABSTRACT FROM AUTHOR]

Sustainable smart city initiatives significantly improve the living standards of citizens and bring significant changes in economic, environmental, and social well-being. Sustainable and remote medical services are vital attributes of modern smart health infrastructure. Smart remote health infrastructure uses interconnected devices to gather and exchange health data. In smart health infrastructure, massive quantities of health data are gathered as Personal Health Records (PHR). Clinical images form a major part of the accumulated PHR. As medical images are confidential and sensitive, secure storage and transmission are significant. Hence, different cryptographic techniques are used to enhance medical image security. The conventional cryptographic schemes are complex and generate a single cipher image corresponding to a plain image. The cipher images are stored in a single database. Such storage makes the cryptosystem weak against single-point attacks. This paper discusses a simple, keyless, and distributed secure storage mechanism for medical images called SecMISS. SecMISS uses Random Grid-based Visual Secret Sharing (RGVSS) along with super-resolution for secure encryption with distributed storage and better reconstruction. The security parameters such as SSIM (≈ 0), correlation of adjacent pixels (≈ 0), PSNR (≤ 8 dB), and entropy (= 1) between share and initial image show that SecMISS provides highest level of security. Similarly, different Human Visual System (HVS) parameters between the initial and reconstructed images show an improvement in the reconstruction. Experimental results show that SecMISS outperforms the existing technique in terms of security and reconstruction. Thereby SecMISS can be efficiently used for securing medical image in sustainable smart hospitals and health infrastructures. [ABSTRACT FROM AUTHOR]

Technology convergence (TC), in which technological innovation occurs by integrating at least two or more existing technologies into hybrid technologies, has been a growing innovation pattern in recent decades. Facing substantial and accelerated technological changes, it is important for technology-based firms to identify TC patterns in pursuing competitive strengths. However, most studies do not consider multi-TC with technical emergence. This research thus develops an anticipation methodology based on the network theory and association rule mining (ARM) for early identification of multi-TC patterns associated with emerging technology classes from patent data in the smart health industry. First, a technological knowledge flow network is built for subsequent analyses using smart health related patent data. Then, core-periphery analysis is used to identify peripheral technology classes which may become influential in the future. Several technical emergence indicators are developed to evaluate emergence scores of identified peripheral technology classes. Finally, ARM is used to discover intriguing multi-TC associated with emerging technology classes. We conclude that the research results are useful for R&D managers for early discovery of emerging multi-TC patterns to explore potential technological opportunities. [ABSTRACT FROM AUTHOR]

Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning). [ABSTRACT FROM AUTHOR]

The present spreading out in healthcare initiated with the appreciation of e-healthcare by interconnecting the medical sensors. IoT played a dynamic part in the healthcare industry, which founded the realization of the Industrial Internet of Things (IIoT). The IIoT is becoming a recognized measure of healthcare universally and is shifting the aptitude to treat patients. IIoT-enabled smart health can easily authenticate the patients and expedites efficient tracking. It supports critical information sharing for effective diagnosis and treatments. IIoT-enabled health systems are the combination of sensors, cameras, devices, and objects that produce a giant quantity of data called big data. The spreading out of Big Data founded the realization of Machine Learning (ML) to predict diseases for better treatment. The major goal of IIoT-enabled smart health systems is to investigate big data and provide valued insights for improved QoS. This research suggests an IIoT-enabled system for healthcare to process the Big Data generated by IIoT sensors using machine learning. The proposed scheme is the optimized parallel and distributed framework for real-time efficient processing. The proposed framework is comprised of three modules that are big data preparation, optimized model building, and big data computation. The data preparation is performed for accurate prediction and to get faster real-time big data processing. The current proposals lack effective prediction and resourceful parallel data processing. The proposed framework is simulated through an experiment with the parallel and distributed framework. The efficiency of the proposed scheme has been verified using authentic datasets with experimentations and simulations. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) in health care is gaining popularity in the field of research to improve quality in smart health care systems and applications. However, security and privacy of Smart Health (S-Health) data are the challenging issues due to Sybil attacks. Sybil attack is one of the most common attacks where a malicious node uses morphed identities to generate Sybil nodes. Sybil nodes can acquire an authorized node identity and misbehaves by affecting its routing information, incurs interruption on communication line and storage. One of the IoT based smart health methodology is Privacy-Aware Smart Health (PASH), in which policy hiding is used to protect the privacy of users. The major issues in PASH is expensive to implement in S-Health applications, also it does not deal with attribute revocation and node traceability. To addresses these issues, a novel SybilWatch Enhanced Privacy-Aware Smart Health (E-PASH) approach is proposed in this paper. This approach has three major phases such as initialization phase, secure communication and Sybil node detection. A Lightweight Encryption Algorithm (LEA) is used to transmit SHRs (Smart Health Record) in encrypted form using prime order grouping. A novel BlueTits Detection (BTD) algorithm is used in detection phase where cluster head gathers the recent activities of the suspicious user, and based on the gathered parameters (Master key and Last One-Time Authentication), the cluster head declares it as a Sybil node. As soon as Sybil node is detected, revised revocation list is shared with active users. The proposed approach is less expensive compared to the existing approach, it also supports attribute revocation and node traceability which are the major setbacks is PASH. Simulation results and comparison analysis shows that proposed SybilWatch is efficient and cost effective compared to the existing approach, the proposed approach yields high detection rate of 99.7% and also false positive rate is reduced to 1% in the smart health systems, which is better compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems. [ABSTRACT FROM AUTHOR]

The terrible cost of injuries and sudden illnesses does have fatal consequences that exposes the limitations of the current prehospital processes in terms of time for emergency staff to arrive on scene and lack of first aid skills among the available incident witnesses. In this paper we aim at developing a smart pervasive chatbot for emergency case assistance based on cloud computing called SPeCECA that assists victims or incident witnesses to help avoiding deterioration of the subject's condition and maintaining his/her physical integrity until the aid arrives, which could dramatically increase the victim's survivability chances. Therefore, even a person with no first aid skills, can help the victim to survive by performing first aid support as suggested by the virtual assistant. Furthermore, thanks to its connectivity with the emergency medical service, trusted person(s), and the access to social media, SPeCECA has its own way of alarming the emergency case, in parallel, after having released the degree of the emergency situation's severity. The proposed method is a mobile pervasive healthcare service in the form of a connected mobile application as a virtual assistant for the benefit of anyone facing an emergency case. The proposed chatbot allows an online human-bot interaction that supports different scenarios for every single emergency case. The design of the system is introduced by its six interdependent components: information pre-processing component (IPPC), natural language processing component (NLPC), context component (CC), information post-processing component (IPoPC), response generator component (RGC), and alert message constructor component (AMCC). [ABSTRACT FROM AUTHOR]

The exoskeletons are an emerging technology that may help the population in many physical health aspects, regardless of the application area. An affliction that is alarmingly increasing due to the rise in the population's average age and their sedentary lifestyles, is sarcopenia. The sarcopenia is the diminish of muscle mass and muscle strength, with the pass of the time. One of the main ways to avoid its development is by performing physical activities, however, when an elderly person needs to perform them, there is a problem due to their physical capacity. A lower-limb exoskeleton can help the elderly, who do not have the minimum muscle strength needed, to perform physical activities. Monitoring the muscles of the user with an electromyography (EMG) portable device, while wearing the exoskeleton, will give a quantitative response analysis to ensure the exoskeleton is helping the patient. After the data has been acquired by the EMG, it can then be uploaded to the cloud for the processing of the information and its further display for a daily check by a medical expert. This process provides a more efficient way of checking the patient's sarcopenia development, but also, to promote the further constant use of the exoskeleton in the daily life of the patients, in order to improve their life quality. [ABSTRACT FROM AUTHOR]

In remote health multiple sensors are attached to a patient and collected data is transferred to the cloud, which is used by the physician for diagnosis. These sensors are prone to random errors. Challenge is to provide correct information since any inaccuracy in the information leads to the incorrect diagnosis of the patient. Any abnormal condition recorded by a single sensor can be cross-checked by matching it against another sensor recording different vital signs. The decisions of the doctors rely upon whatever the sensor is streaming currently, because matching against other vitals has to be done at real-time. Since fuzzy-based system works with the imprecise dataset and merges them in ranges, it favours the decision of a medical practitioner in remote health. Therefore, in this paper, we discuss how to reduce uncertainty from the remote health sensors using fuzzy modelling system. We discuss some use cases to simulate remote health scenario with fuzzy inferencing system and obtain acceptable output in presence of random errors. We also compare our proposed model with basic (statistical) and advanced (context-aware) models to show its performance exceeds the other two. First, the statistical model needs more data set than our proposed model and second context-aware model may not correctly detect random error from the current context. [ABSTRACT FROM AUTHOR]

Today's medical industry can be represented by a human-centered u-healthcare paradigm that is available and accessible anywhere, where high-tech IT can serve as the basis at any time and any place. In addition, in the medical industry, studies of many of the developments and applications are actively conducted based on the development of information communication technology. The aim of medical-information systems is the construction of an advanced IT and integrated u-healthcare system that evolves in the direction of integrated medical-based IT-convergence systems. Accordingly, to resolve the problems of telemedicine, in terms of the remote access to medical data, some public and private initiatives have been proposed, ranging from patient-mobility approaches to medical data. In addition, regarding GENICloud, which provides links with the existing future Internet testbeds and the Eucalyptus Cloud, two out of the seven GENIAM APIs have been announced as the common APIs of the future Internet testbeds in GENI, and they have been implemented and are provided. In the present GENI Cloud system, due to the provision of limited APIs, restrictions may occur in the future Internet testbeds and the Eucalyptus Cloud system management. Therefore, this study proposes a cloud-based mobile health service for the enhancement of the quality of service (QoS) including factors such as reliability and response time to resolve the problems of the broadband-communication infrastructure in the existing mobile health service and the delay problem on the wireless body area network. In this paper, we propose the cloud-based u-healthcare network with a QoS-guaranteed mobile health service. For this method, the TMO-distribution object model that was used in the existing research to implement a reliable and efficient cloud system for users was not used, and instead, a cloud-platform environment was built up through the construction of a distributed system based on a cluster-based mobile object. For this purpose, this study considered the characteristics of the wireless-communication environments between the terminals and the cloud servers in the mobile cloud environment and the proposed cloud mobility services and the specialized mobile cloud-control software. Later, for linkages with cloud computing environments and testbeds was proposed. In addition, this study carried out a cloud mobility-control design to provide a service in the mobile cloud environment that is based on the actual future Internet testbeds. Lastly, based on the structured cloud-platform environment, this study designed access interfaces to provide a mobile healthcare service in consideration of the user convenience. For the mobile-service access interfaces, since the same service interfaces can be used to access the characteristics and functions of all of the applications from browsers and device clients, the model-view-controller structure of the platform was designed, including the components for the further improvement of the requirements, reuse, and maintenance of the codes in medium and large distributed systems. [ABSTRACT FROM AUTHOR]

In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification. In the former stage, the ECG heartbeats are projected to a 2D state space, to reveal heartbeats' trajectory behaviors and produce 2D images by a split-then-hit method. In the second stage, a convolutional neural network is introduced to automatically learn the intricate patterns directly from the ECG image representations without heavy feature engineering, and then perform user identification. Experimental results on two acquired datasets using our wearable prototype, show a promising identification rate of 98.4% (single-arm-ECG) and 91.1% (ear-ECG), respectively. To the best of our knowledge, it is the first study on the feasibility of using single-arm-ECG/ear-ECG for user identification purpose, which is expected to contribute to pervasive ECG-based user identification in smart health applications. [ABSTRACT FROM AUTHOR]

The development of the IT convergence technology has been serving as the basis for the application of the IoT devices to the products being used in our everyday life, such as clock, health band, scale, TV, light and doorlock. The government and companies have been developing the IoT-based smart home, smart city and smart health to enhance the national health as well as the life convenience. In particular, the government has been providing its diverse supports to the smart health in order to utilize electronic medical records, personal health record and self-diagnosis. The Ministry of Health and Welfare has been expanding the self-diagnosis service through conducting an epidemiologic survey on mental illness and mental health, the two issues that are becoming a major problem in our society. The results of the survey-based self-diagnosis may vary depending on the environment surrounding the users as well as the mental state of the users. In this paper, we propose the depression index service using the knowledge-based crowdsourcing within the smart health platform. The proposed method provides the index suitable for the context of each user through using the knowledge-based crowdsourcing within the smart health platform to compare the actual users with the similar users and predict the depression level. The prediction of the depression index uses the collaborative filtering model based on the comparison of users with other users, and uses the crowdsourcing to supplement the sparsity problem. CES-D is used as the criterion for determining the depression level, and the CES-D data is collected through the crowdsourcing. In the preexisting crowdsourcing process, the highly expert/reliable knowledge base to which the context information of the users is applied is constructed. The context information of the users is additionally collected through the self-diagnosis process, and the data are determined through the preconditioning process. The actual users data and determined data are applied to the Pearson's correlation coefficient to analyze the similarity level of between the actual/preexisting users. Then the analyzed similarity is used to service the result of the self-diagnosis to the actual users. This is a flexible service to which the context information of the users is applied, and it contributes to the users' decision-making. [ABSTRACT FROM AUTHOR]

To introduce smart health in Wuhan, and provide some references for other cities. As the largest mega-city in central China, Wuhan is investing large amounts of resources to push forward the development of Smart Wuhan and Health Wuhan, and it has unique features. It is one of the centerpieces of China's New Healthcare Reform, and great hope is put on it to help solve the conflict between limited healthcare resources and the large population of patients. How to plan and design smart health is important. The construction of Wuhan Smart Health includes some aspects as follows, like requirement analysis, the establishment of objectives and blueprint, the architecture design of regional health information platform, evaluation and implementation, problems and solutions, and so on. Wuhan Smart Health has obtained some achievements in health network, information systems, resident's health records, information standard, and the first phase of municipal health information platform. The focus of this article is the whole construction process of smart health in Wuhan. Although there are some difficulties during this period, some smart health services and management have been reflected. Compared with other cities or countries, Wuhan Smart Health has its own advantages and disadvantages. This study aims to provide a reference for other cities. Because smart health of Wuhan is characteristic in construction mode. Though still in the initial stage, it has great potentials in the future. [ABSTRACT FROM AUTHOR]

Prescription medication abuse is a major healthcare problem and can lead to addiction syndrome, higher healthcare cost, and serious harm to patients. Smart health can play a major role in addressing medication abuse by monitoring patient's health conditions and medication consumption, and, by connecting with healthcare professionals and utilizing suitable interventions. More specifically, medication behavior can be analyzed using information from smart medication systems, specialized wearable sensors and/or mobile devices with patient-entered consumption data. In this paper, we study monitoring systems to predict current abuse and near-future addiction and provide interventions to reduce medication abuse. The future work involves development and evaluation of Ab-Med mobile app for monitoring medication consumption and comprehensive cost effectiveness of interventions for medication abuse. [ABSTRACT FROM AUTHOR]