Mobile Solutions and Their Usefulness in Everyday Life

The Internet-of-Things (IoT) is a revolutionary technology that is rapidly changing the world. IoT systems strive to provide automated solutions for almost every life aspect; traditional devices are becoming connected, ubiquitous, pervasive, wireless, context-aware, smart, and controlled through mobile solutions, to name but a few. IoT devices can now be found in our apartments, places of work, cars, buildings, and in almost every aspect of life. In this investigation, we propose an IoT system Development Model (IDM). The proposed IDM enables the development of IoT systems from concept to prototyping. The model comprises concept refinement pyramids, decision trees, realistic constraint lists, architecture and organization diagrams, communication interface patterns, use cases, and menus of analysis metrics and evaluation indicators. The investigation confirms that the proposed model enjoys several properties, such as clarity, conciseness, thoroughness, and productivity. The model is deployed for a variety of systems that belong to heterogeneous areas of application; the model is proven to be effective in application and successful in integrating mobile solutions. This chapter includes the presentation of the IDM sub-models, reasoning about their usefulness, and technical developments of several systems. The chapter includes thorough discussions, analysis of the model usability and application, and in-depth evaluations.

Internet-of-Things (IoT) promises to impact every aspect of our daily life by connecting and automating everyday objects which bring the notion of SmartLiving. While it is certain that the trend will grow at a rapid speed, at the same time, challenge to alleviate intelligence of things by reaping value from the data requires to be addressed. The intelligence further cannot depend only on the existing cloud-based solutions which edge computing is expected to mitigate by integrating distributed intelligence. An IoT application necessitates applying knowledge with low latency. However, to comply with the vision of autonomic IoT and real-time intelligence, extracting and applying knowledge are necessitated for which this chapter proposes to exploit mobile edge computing (MEC) to further assist distributed intelligence. Therefore, the problem that this chapter addresses is feasibility investigation of MEC to provide intelligence by reasoning contextualised data and, thereby, the role of MEC in distributed intelligence.

Seasonally, a huge number of people visit public places (e.g., holy places like El-Harm El-Madini El-Harm El-Makki (KSA), railway stations like Mumbai suburban railway (India), or sports events in big stadiums). Crowd management is critical in these situations in order to avoid crowd disasters (e.g., stampede and suffocation). Therefore, there is an urgent need for a framework to manage these crowds in order to save people’s lives. This framework shall be smart and efficient in terms of crowd time management and exerted efforts. The proposed framework is based on IoT and supports mobile device interaction through smart applications with a fairly simple interface to suit all ages. The aim is to strongly support administrators controlling and distributing visitors over the given place. The framework consists of three layers: sensor, management, and interface layers. The sensor layer is responsible for crowd data acquisition. The management layer acts as a middleware between sensors and interface layers. It includes web services which are responsible for collecting and analyzing the data coming from the sensors. It then notifies administrators about overcrowded areas to take the suitable decisions. Afterwards, the suitable decision (e.g., close/open doors and roads) will be taken and transferred to the interface layer. The interface layer is formed by user-friendly applications that communicate information between the management layer and the visitors. It provides mobile applications that aim to inform visitors about (1) current opening roads and doors, (2) how to find noncrowded areas, and (3) how to locate their groups and friends. The proposed framework provides high availability, reliability, usability, and performance.

This chapter presents a case study to promote and enhance transport sustainability by providing a mobile app that allows visually impaired people (VIP) to independently use public transportation. This work is a follow-up of a previous case study, and in this chapter, we present new functionalities available in the mobile app, including a careful and detailed layout definition to improve usability. We also present the new tests made in the field with some associates and that allowed us to make important conclusions regarding the usefulness of such an application as well as future directions.

As humans grow older, their cognitive needs change more frequently due to distal and proximal life events. Designers and developers need to come up with better designs that integrate older users’ needs in a short period of time with more interaction with the users. Therefore, the positioning of human end users in the center of the design itself is not the key to the success of design artifacts while designing applications for older adults to use a smartphone as a promising tool for journey planner while using public transportation. This study analyzed the use of human-centered design (HCD) components, the spiral model, and the design for failure (DfF) approach to improve the interactions between older users and designers/developers in gathering usability needs in the concept stage and during the development of the app with short iterative cycles. To illustrate the importance of the applied approach, a case study with particular focus on older adults is presented.

Maps for Easy Paths (MEP) project aims to improve accessibility of our cities, by collecting data of urban barriers and accessible paths using mobile devices. Its focus is on users with motor impairments; however, the application design takes into account also some characteristics of other kinds of disabilities. In this chapter, we describe the MEP project in general and present our mobile applications and their design to meet the requirements of usability, accessibility, and usefulness. In particular, we report our usability–accessibility evaluation done both with automatic tools and with manual/visual analysis and describe the experience in using it in different cities and in campaigns with middle and high school students, to understand the perceived usefulness and its ease of use.

Currently, people are more concerned about their health and diseases. Therefore, their interests in health and diseases have increased tremendously in the last decade. Till date, medical industries developed several programs and services to promote the health-related issues such as awareness programs regarding HIV, diabetes, dengue, overweight, etc. Due to increased concern for ubiquitous health services, it incorporates the advantage of information technology which can lead to design a preventive management system for various types of disease and health conditions. Further, the technological advancement is also favorable to the management of chronic diseases. In this work, a personal health record (PHR)-based decision support model is proposed for monitoring diabetes using mobile environment. In order to facilitate the people, a graphical user interface is incorporated into the PHR-based model for analyzing their lifestyles.

With the advent and rapid deployment of Internet of Things (IoT), artificial intelligence (AI), powerful smartphones, and wearable sensor devices (e.g., smartwatch), we are entering into the era of automated, remote, on-demand mobile healthcare services. According to the WHO, cardiovascular disease is the modern-day disease. However, prognosis rate of cardiac disease patients can be potentially made high with early detection and diagnosis. In this book chapter, we describe automated cardiac health monitoring system using smartphone and wearable sensors. The main contribution of such mobile applications and systems is to form a connected universe with biomedical sensors, patients, physicians, clinics, hospitals, and other medical service providers and to exploit robust analytics to infer and actuate the appropriate information and formative actions. The powerful anomaly analytics exploit AI, signal processing, and deep learning mechanisms that enable predictive decision-making and facilitate preventive cardiac health screening. The main emphasis is to develop and deploy smart, computationally efficient, rather than human-in-loop, user-friendly, data-driven cardiac healthcare solutions, where patients and healthcare service providers are seamlessly connected. In this book chapter, we discuss about important cardiovascular signals, namely, electrocardiogram (ECG), photoplethysmogram (PPG), and heart sound or phonocardiogram (PCG), and describe their role in the process of developing a mobile-based cardiac care solution. These cardiac marker signals constitute an intelligent and robust feature space for detection of different cardiac abnormalities and diseases like coronary artery disease, cardiac arrhythmia, and others. These sensor signals can be captured by affordable wearable sensors. In order to develop such mobile applications and systems, we need to address different challenges like noisy signal removal and data privacy protection along with providing robust analytics engine.

The aftermath of the Ebola Virus Disease (EVD) crisis in West Africa in 2014 was horrific. The EVD epidemic claimed 11,315 lives and had almost a 40 per cent killing rate. The EVD had huge negative effects on social, economic and political fronts of the four most affected countries Guinea, Liberia, Sierra Leone and Nigeria. Liberia was the country worst hit and Nigeria was the least affected among them. In both countries, social media was widely used to provide public health messaging and behavioural change guidance to the population on how to avoid getting infected with the deadly EVD. However, while the use of social media significantly reduced the spread of Ebola in Nigeria by 75 per cent, it was not effective in reducing the spread of the EVD in Liberia. In this paper, we reflect factors responsible for the ineffective use of mobile social media in aiding social behavioural change guidance towards the prevention of EVD spread in Liberia and the successful use of mobile social media in doing the same in Nigeria. We further reflect on the definition of ‘mobile social media’.

The cancer epidemic causes more deaths in developing countries than the more hyped HIV, malaria, and TB combined. Sub-Saharan Africa has the highest prevalence of cancer in developing countries, and the most prevalent type of cancer is the cancer of the cervix, with 57,381 deaths yearly. Many medical scholars agree that the high death rate from the cervical cancer scourge is preventable with effective cervical cancer control strategies such as appropriate prevention strategies in the form of awareness campaigns, effective screening for early diagnosis, and treatment programs. There have been numerous calls for measures that can improve prevention, early detection, and treatment of cervical cancer among women in Sub-Saharan Africa, and numerous approaches have so far been piloted. This paper presents an analysis of the potential of social media technologies to effectively support prevention, early detection, and treatment of cervical cancer in Sub-Saharan Africa.

This book chapter presents the importance of mobile solutions based on body sensor network (BSN) architecture for health monitoring in case of motor-impaired people. In this work, we present a noninvasive system based on mobile technology that allows biomedical signal monitoring by wearable electrodes. The concept of brain–computer interfaces (BCIs) is the ultimate trend for the entertainment industry (gaming), but this technology has potential by providing signal alerts to motor-impaired people (epilepsy or to enable communication). The mobile technologies allow developing the private cloud for tracking data from biomedical sensors and temporary data storage. Motor impairment is total or partial loss of function of a body part that can be translated to muscle weakness, lack of muscle control, or total paralysis. In case of people with motor impairments, monitoring at home involves a monitoring system based on body sensor network (BSN), Internet of Things (IoT), and feedback from doctors. Such a system may lead to reduced costs of hospitalization.

Air pollution is one of the most compelling global problems since it poses a serious threat on everyone’s health. Governments and people thus put a premium on the reduction of air pollution in the living environment. Consequently, it draws considerable attention on how to efficiently collect air quality data, especially in cities. In the past, the job of air quality monitoring was usually conducted by installing a few monitoring stations on fixed locations. However, this scheme provides just coarse-grained monitoring, where the resolution of air-quality samplings may be poor. Even worse, it is difficult to move monitoring stations after installation, but the monitoring mission could be often changed. To deal with the problems, many studies propose various mobile solutions to air quality monitoring by equipping gas sensors on mobile devices or vehicles, which allow people to actively and cooperatively detect air pollution in their surroundings. In the chapter, we provide a comprehensive survey of these mobile solutions, and our discussion has four parts. First, we introduce the techniques to evaluate air quality, including an index to report the quality of air and models to predict the dispersion of air pollution. Then, we present the mobile solutions to collect air quality, which can be realized by pedestrians, cyclists, and drivers. Afterward, we discuss how to analyze raw data collected by smartphones, followed by the issue of reporting sensing data collected by cars. Some research directions and challenges for future mobile solutions to air quality monitoring will be also addressed in the chapter.

Parkinsonian syndromes are a heterogeneous entity of movement disorders, with various described subtypes. This systematic review aimed to examine the available literature on smartphone applications for assessment of Parkinson’s disease motor and nonmotor symptoms and signs. Papers published from 2013 to 2017, listed in two electronic databases—IEEE Xplore and PubMed—were searched, to identify the works related with smartphone use for PD patients’ diagnosis and monitoring. Full-text articles were analyzed to evaluate the quality of the reported methods and results, considering the validity, reliability, and sensitivity of the techniques used in the measurements as well as the Grading of Recommendations Assessment, Development and Evaluation guideline. The data from 26 full-text articles suggest that many and relevant data can be collected automatically and accurately via mobile phone. Inertial measurement units as well as capacitive, force/pressure, acoustic sensors were used for the development of smartphone-based tools to improve assessment and monitor symptoms and signs of Parkinson’s disease. Smartphone-based information on upper limbs tremor, gait, posture, balance, activities, and speech may improve quality of healthcare services for Parkinson’s disease patients and their quality of life.

Falling is an important health risk, especially for the elderly people. This situation prevents individuals from living independently. Automatic and high-accuracy detection of the falls will contribute in preventing the negative situations that may occur. In this study, a mobile solution with a new architecture for the detection of falls is presented. For this purpose, motion sensor data have been collected simultaneously from smartwatch and smartphone with Android operating system. Data sets for both smartwatch and smartphone have been created by labeling the falls and actions which are not falling in the data. The performances of Decision Tree, Naive Bayes, and k-Nearest Neighbor (kNN) methods have been tested on these data sets, and the kNN method has given the best result on two data sets. Accordingly, the kNN method is used for classification in the developed Android-based mobile solution. In addition, it is aimed to detect and prevent actions that could lead to bad results by monitoring the heart rate of the user with the built-in heart rate monitor on the smartwatch.

Innovation in response systems has evolved over time, moving away from hardware that requires extensive wiring to achieve network connectivity. There has been an increasing demand for clickers, a handheld remote control device, used to convey responses to questions. However, clicker-based audience response systems have been difficult to use and deploy. In today’s mobile-centric world, an individual with a mobile device has access to infinite opportunities. This study aims to utilize mobile technology to enable members of the audience to respond to questions through their mobile devices instead of additional hardware, which is inconvenient and expensive. This mobile application provides presenters with an important analytics tool that would help process identified aspects, based on participants’ responses, with illustrated graphs and identify the most frequent items with minimal user time and effort using Misra-Gries algorithm.

Although hand-based interaction dominates mobile applications, this can be unsuitable for use by motor-impaired individuals or in situations such as musical performance or surgery, where the hands are otherwise occupied. The alternative of foot-based interaction, the subject of this chapter, has been shown to offer reasonable performance in such conditions and offers benefits in terms of diversity of input techniques, wide applicability, and social acceptability. This chapter also describes potential applications of foot-based interfaces, with an emphasis on factors related to usability. We aim to inspire designers and developers to consider the potential for leveraging interaction through the feet as a replacement for, or complement to, more traditional application designs.