Connected Health in Smart Cities

While maintaining a food record is an essential means of health management, there has long been a reliance on conventional methods, such as entering text into record sheets, in the health medicine field. Food recording is a time-consuming activity; hence, there is a need for innovation using information technology. We have developed the smartphone application “FoodLog,” as a new framework for food recording. This application uses digital pictures and is supported by image recognition and searches. It is available for general release. In this paper, we present an overview of this framework, the data statistics obtained using FoodLog, and the future prospects of this application.

There has been a great deal of research activity in computer- and robot-assisted surgeries in recent years. Some of the advances have included robotic hip surgery, image-guided endoscopic surgery, and the use of intra-operative MRI to assist in neurosurgery. However, most of the work in the literature assumes that all of the expert surgeons are physically present close to the location of a surgery. A new direction that is now worth investigating is assisting in performing surgeries remotely. As a first step in this direction, this chapter presents a system that can detect movement of hands and fingers, and thereby detect gestures, which can be used to control a catheter remotely. Our development is aimed at performing remote endovascular surgery by controlling the movement of a catheter through blood vessels. Our hand movement detection is facilitated by sensors, like LEAP, which can track the position of fingertips and the palm. In order to make the system robust to occlusions, we have improved the implementation by optimally integrating the input from two different sensors. Following this step, we identify high-level gestures, like push and turn, to enable remote catheter movements. To simulate a realistic environment we have fabricated a flexible endovascular mold, and also a phantom of the abdominal region with the endovascular mold integrated inside. A mechanical device that can remotely control a catheter based on movement primitives extracted from gestures has been built. Experimental results are shown demonstrating the accuracy of the system.

The advent of artificial intelligence methodologies pave the way towards smarter healthcare by exploiting new concepts such as deep learning. This chapter presents an overview of deep learning techniques that are applied to smart healthcare. Deep learning techniques are frequently applied to smart health to enable AI-based recent technological development to healthcare. Furthermore, the chapter also introduces challenges and opportunities in deep learning particularly in the healthcare domain.

Mood disorders, anxiety, depression, and stress affect people’s quality of life and increase the vulnerability to diseases and infections. Depression, e.g., can carry undesirable consequences such as death. Hence, emotional states detection approaches using wearable technology are gaining interest in the last few years. Emerging wearable devices allow monitoring different physiological signals in order to extract useful information about people’s health status and provide feedback about their health condition. Wearable applications include e.g., patient monitoring, stress detection, fitness monitoring, wellness monitoring, and assisted living for elderly people, to name a few. This increased interests in wearable applications have allowed the development of new approaches to assist people in everyday activities and emergencies that can be incorporated into the smart city concept. Accurate emotional state detection approaches will allow an effective assistance, thus improving people’s quality of life and well-being. With these issues in mind, this chapter discusses existing emotional states’ approaches using machine and/or deep learning techniques, the most commonly used physiological signals in these approaches, existing physiological databases for emotion recognition, and highlights challenges and future research directions in this field.

A plethora of interwoven social enablers and technical advancements have elevated smart healthcare from once a supplemental feature to now an indispensable necessity crucial to addressing intractable problems our modern cities face, which range from gradual population aging to ever surging healthcare expenses. State-of-the-art smart healthcare implementations now span a wide array of smart city applications including smart homes, smart environments, and smart transportation to take full advantage of the existing synergies among these services. This engagement of exogenous sources in smart healthcare systems introduces a variety of challenges; chief among them, it expands and complicates the attack surface, hence raising security and privacy concerns. In this chapter, we study the emerging trends in smart healthcare applications as well as the key technological developments that give rise to these transitions. Particularly, we emphasize threats, vulnerabilities, and consequences of cyberattacks in modern smart healthcare systems and investigate their corresponding proposed countermeasures.

This chapter consists of five main sections. It begins by discussing the scope of utilizing biofeedback technology in healthcare systems. Then, it presents a brief history of biofeedback technology and previous reviews. The second section highlights the sensory technology in biofeedback systems by presenting the different types of sensors and their features. The third section explores recent research of biofeedback-based healthcare systems by presenting a range of applications in different fields combined with the utilized sensors. The fourth section discusses the challenges and issues that affect the deployment of biofeedback in healthcare systems. The last section concludes this review.

With the increasing prevalence in the use of wearables, social media, smart living, and personalized recommender systems for consumer health, it becomes imperative to converge these technologies to provide personalized, context driven, proactive, and preventive care in real time. Digital Twins are a convergence technology and involve making a digital replica of any living or nonliving entity. At present, Digital Twins are extensively used in Industry 4.0 where Digital Twins help in optimizing the performance of machines by proactive and predictive maintenance. This chapter gives an overview of the existing literature and aims to provide an overview of existing literature on Digital Twins for personal health and well-being—key terminologies, key applications, and key gaps.

Medical Cyber Physical Systems (MCPSs) prescribe a platform in which patient health information is acquired by the emerging Internet of Things (IoT) sensors, pre-processed locally, and processed via advanced machine intelligence algorithms in the cloud. The emergence of MCPSs holds the promise to revolutionize remote patient healthcare monitoring, accelerate the development of new drugs or treatments, and improve the quality-of-life for patients who are suffering from various medical conditions among other various applications. The amount of raw medical data gathered through the IoT sensors in an MCPS provides a rich platform that artificial intelligence algorithms can use to provide decision support for either medical experts or patients. In this paper, we provide an overview of MCPSs and the data flow through these systems. This includes how raw physiological signals are converted into features and are used by machine intelligence algorithms, the types of algorithms available for the healthcare domain, how the data and the decision support output are presented to the end user, and how all of these steps are completed in a secure fashion to preserve the privacy of the users.

In an effort to improve the quality of care for any population, technology is integrated into the healthcare system. Different types of technologies can aid in health promotion through prevention, education, and monitoring techniques. Prevention methods are becoming more common with older adults to assist with their activities of daily living as well as to support them in learning and remembering healthy behaviors. The willingness to adopt a new technology is key to successfully modifying behavior and what hinder the outcome are issues of competency as well as access.

The purpose of this book chapter is to use empirical studies to review the types of health technology used with the older population, as well as the overall level of success on their behaviors. Once the research question was defined, an inclusion and exclusion criterion was used to select the peer-reviewed articles. Various studies that fulfilled the predefined criteria were used. Data was extracted from 39 articles for the evaluation of the different health technologies and their uses.

mHealth and phones are the most popular type used for health promotion, as it is present in 36% of the articles evaluated. Other successful and popular types of technology used were websites and modules (26%), as well as monitoring technology (23%). In all of the studies, the elderly population was able to successfully use the technology, indicating that the adoption of new technology is possible at any age. Technology can be used to affect the elderly population to integrate healthier habits into their lives. The variety of accessible technologies allows individuals to use it in conjunction for their desired outcomes.

Connected Health, also known as Technology-Enabled Care (TEC), refers to a conceptual model for health management where devices, services, or interventions are designed around the patient’s needs and health-related data is shared in such a way that the patient can receive care in the most proactive and efficient manner. In particular, TEC enables the remote exchange of information, mainly between a patient and a healthcare professional, to monitor health status, and to assist in diagnosis. To that aim recent advances in pervasive sensing, mobile, and communication technologies have led to the deployment of new smart sensors that can be worn without affecting a person’s daily activities. This chapter encompasses a brief literature review on TEC challenges, with a focus on the key technologies enabling the development of wearable solutions for remote human motion tracking. A wireless sensor network-based remote monitoring system, together with the main challenges and limitations that are likely to be faced during its implementation is also discussed, with a glimpse at its application.

This chapter explores the most relevant aspects in relation to the outcomes and performance of the different components of a healthcare system with a particular focus on mobile healthcare applications. In detail, we discuss the six quality principles to be satisfied by a generic healthcare system and the main international and European projects, which have supported the dissemination of these systems. This diffusion has been encouraged by the application of wireless and mobile technologies, through the so-called m-Health systems. One of the main fields of application of an m-Health system is telemedicine, for which reason we will address an important challenge encountered during the realization of an m-Health application: the analysis of the functionalities that an m-Health app has to provide. To achieve this latter aim, we will present an overview of a generic m-Health application with its main functionalities and components. Among these, the use of a standardized method for the treatment of a massive amount of patient data is necessary in order to integrate all the collected information resulting from the development of a great number of new m-Health devices and applications. Electronic Health Records (EHR), and international standards, like Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR), aims at addressing this important issue, in addition to guaranteeing the privacy and security of these health data. Moreover, the insights that can be discerned from an examination of this vast repository of data can open up unparalleled opportunities for public and private sector organizations. Indeed, the development of new tools for the analysis of data, which on occasions may be unstructured, noisy, and unreliable, is now considered a vital requirement for all specialists who are involved in the handling and using of information. These new tools may be based on rule, machine or deep learning, or include question answering, with cognitive computing certainly having a key role to play in the development of future m-Health applications.

Electroencephalography (EEG) motor imagery signals have recently gained significant attention due to its ability to encode a person’s intent to perform an action. Researchers have used motor imagery signals to help disabled persons control devices, such as wheelchairs and even autonomous vehicles. Hence, the accurate decoding of these signals is important to brain–computer interface (BCI) systems. Such motor imagery-based BCI systems can become an integral part of cognitive modules that are increasingly being used in smart city frameworks. However, the classification and recognition of EEG have consistently been a challenge due to its dynamic time series data and low signal-to-noise ratio. Deep learning methods, such as the convolution neural network (CNN), have achieved remarkable success in computer vision tasks. Considering the limited applications of deep learning for motor imagery EEG classification, this work focuses on developing CNN-based deep learning methods for such purpose. We propose a multiple-CNN feature fusion architecture to extract and fuse features by using subject-specific frequency bands. CNN has been designed with variable filter sizes and split convolutions for the extraction of spatial and temporal information from raw EEG data. A feature fusion technique based on autoencoders is applied. Cross-encoding technique has been proposed and is successfully used to train autoencoders for a novel cross-subject information transfer and augmenting EEG data. This proposed method outperforms the state-of-the-art four-class motor imagery classification methods for subject-specific and cross-subject data. Autoencoder cross-encoding helps to learn subject invariant and generic features for EEG data and achieves more than 10% increase on cross-subject classification results. The fusion approaches show the potential of applying multiple CNN feature fusion techniques for the advancement of EEG-related research.