On-body input interfaces that can be used accurately without visual attention could have a wide range of applications where vision is needed for a primary task: emergency responders, pilots, astronauts, and people with vision impairments could benefit by making interfaces accessible. This paper describes a between-participant study (104 participants) to determine how well users can locate e-textile

Smart healthcare applications rely on frequent monitoring information from the patient’s body using wearable sensors (WSs). Healthcare systems rely on multi-level information for identifying the diseases and hence providing a proper diagnosis. The challenging task is the acquisition of WS information and assimilation of the same for diagnosis purposes. This proposal introduces an error-less data fusion

The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan

Healthcare data analysis is a prominent field of research supporting information technologies in the medical industry. Handling large volumes of data and mining them for application-related services requires time-efficient and less complex processing. With the implication of machine learning in computing processes, the analysis systems and mining performance are improved. In this manuscript, knowledge-based

Novel coronavirus pneumonia in 17 city (Hubei) provinces was analyzed by using the principle of thermodynamics. A thermodynamic imaging model of infectious diseases was established to calculate the cumulative superimposed density of epidemic in 17 cities (prefectures). An evaluation rule of urban risk grade is established and evaluates the COVID-19 risk of 17 cities. The results show that (1) the higher

This essay develops an interdisciplinary framework for understanding the relationship between “the person” and “the user” in the Internet of Things (IoT) by exploring a similarly troubled dyadic discourse: the thing. The goal is twofold: first, to provide a critical framework for scholars studying the humanistic and social implications of IoT; second, to broaden the scholarly discussion of IoT beyond

Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term

Using deep learning networks to recognize the table attracts lots of attention. However, due to the lack of high-quality table datasets, the performance of using deep learning networks is limited. Therefore, TableRobot has been proposed, an automatic annotation method for heterogeneous tables. To be more specific, the annotations of table consist of the coordinates of the item block and the mapping

Recently, the leading cause of preventable blindness is diabetic retinopathy (DR). Although there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal screening could facilitate the early detection and treatment of DR. The goal of this research is to develop a reliable DR screening and detection model to reduce the risk of DR-related blindness. DR-infected eyes describe

Although recommender systems enjoy widespread adoption in numerous different production settings, standard methods draw only on previous purchases or ratings, and optionally simple customer or product features. In many domains, however, the purchase or rating history is very sparse. Standard approaches suffer from such data sparsity and neglect to account for important additional dependencies that

This paper constructs the measurement index of core competence by text analysis method and empirically tests the influence of core competence on enterprise capital structure. We find that the stronger the core competence is, the lower the asset-liability ratio will be, which means core competence can help enterprises to deleverage. The mechanism test finds that the influence of core competence on enterprise

Society today is facing a rapidly aging population. While various monitoring systems have been proposed for protecting elderly persons in their daily lives, concerns relating to privacy limit the effectiveness of these systems. In response to this issue, we investigate the use of Doppler radar images for monitoring the elderly, as these images are known to protect privacy very well. As the first step

An object shape information plays a vital role in many computer applications. Among these applications, some tasks can allow object shape analysis directly solve the problem. Thus, how to extract shape features and model the shape is a crucial issue. This paper proposes a new shape modeling method utilizing the centered convolutional deep Boltzmann machine to model two-dimensional (2D) shape. The proposed

WiFi has recently established itself as a powerful medium for radio frequency (RF) sensing due to its low cost and convenience. Many tasks, such as gesture recognition, activity recognition, and fall detection, can be implemented by measuring and calculating how the propagation of WiFi signals is affected by human activities. However, current WiFi-based sensing solutions have limited scales as they

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users’ requests according to the users’ context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment

Cybersecurity and cybercrime cannot exist without each other. They are not contraries, but rather two opposite poles of the same idea. Although it may seem that it is a rather black and white kind of relationship, the measures aimed at protecting innocent people raise a myriad of ethical dilemmas. This paper presents the results of a horizon scanning study aimed at identifying the ethical and human

With the fast development of natural language processing (NLP), financial text data processing has gained much attention due to its huge potential business value. Deep learning model based on manual perception-based labeling is commonly used to illustrate implicit meanings behind financial text. However, such manual labeling is costly and subjective, and may not perform well due to its weak link with

Implicit HCI is about computers understanding the intentions and needs of the user and proactively triggering functions or adapting the interface to help users achieve their goals. In ubiquitous learning environments, this could mean that the software and hardware settings make relevant learning material available to students; activate proper learning environments, like collaborative authoring tools

Mobile applications are an essential element in pervasive and ubiquitous computing, and they face many challenges during their generation process from the analysis of user needs to the design of specific mobile interfaces and their development in several technological platforms. Moreover, the rise of Ambient Intelligent and context-aware environments also introduces multiple interaction aspects to

A Correction to this paper has been published: https://doi.org/10.1007/s00779-020-01504-1

Precise and reliably tracking of players’ position during the action or during the game is important in many sports. Our motivation was to investigate and validate the use of ultra-wideband (UWB) systems for various player and coach applications that depend on tracking the position of the player and possible related events. We developed and implemented a real-time positioning system that allows (a)

Interactive sonification is an effective tool used to guide individuals when practicing movements. Little research has shown the use of interactive sonification in supporting motor therapeutic interventions for children with autism who exhibit motor impairments. The goal of this research is to study if children with autism understand the use of interactive sonification during motor therapeutic interventions

Over the last decades, Internet of Things-based applications have become increasingly popular among many researchers and companies in developing a comfortable and safe lifestyle for people. Currently, many Internet of Things-based systems are providing extensive benefits to our daily lifestyle, although low power, failure in connectivity, and lack of computing knowledge are major challenges within

Participant selection is a fundamental challenge to perform sensing tasks with adequate data quality in various mobile crowdsensing (MCS) applications. In this paper, we explore participant selection mechanisms with sensing-gain constraints in MCS. First, we propose a novel quality-aware participant reputation model with active factors. Second, since user density differs in various applications, we

With an intention of improving healthcare performance, wearable technology products utilize several digital health sensors which are classically linked into sensor networks, including body-worn and ambient sensors. On the other hand, intracerebral hemorrhage (ICH) defines the injury of blood vessels in the brain regions, which is accountable for 10–15% of strokes. X-ray computed tomography (CT) scans

Air pollution is worsening almost everywhere in the world. According to the Health Effects Institute (HEI), more than 95% of the world population breathe polluted air, toxic to their cardiovascular and respiratory health, which caused the death of 4.2 million people worldwide in 2016. As a result, the air pollution has become one of the leading causes of death worldwide. Therefore, an early cost-efficient

The rapid development of mobile intelligence and wearable computing promotes the rise of quantified-self. Users quantify themselves and realize self-tracking and self-cognition through the connection between mobile phone terminals and wearable computing devices. The characteristics of immersive quantified information design in the convergence mode of mobile intelligence and wearable computing attract

Previous studies have highlighted the difficulty that designers face in designing interactive systems that will help visitors learn and reflect upon cultural issues in support of museums’ new roles of shaping cultural identify and community building. In this paper, we report a study to explore the potential of Internet of things (IoT) serious games to support cultural learning and reflection by incorporating

Technological advances are driving the growth of online health communities. However, there are some problems such as low user participation and insignificant social benefits in online health communities. This paper discusses the evolution law of information sharing behavior of members of online health community to study the influence of different behaviors on health information sharing results and

Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious

The use of capacitive sensors in the automotive context opens new possibilities in the development of new interfaces for machine interaction with the vehicle occupants. Large smart surfaces with gesture recognition will possibly be part of such new interfaces. However, the data processing cost of such new sensors should be maintained at a minimum while increasing the complexity of their gesture recognition

Passwords are regarded as the most common authentication mechanism used by Web-based services, despite large-scale attacks and data breaches regularly exploiting password-associated vulnerabilities. We investigate the trends behind password formulation in an exploratory study to postulate that social identity and language play a major role in users’ general attitude toward formulating passwords. For

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related

During the last decade, touchless gestural interfaces have been widely studied as one of the most promising interaction paradigms in the context of pervasive displays. In particular, avatars and silhouettes have proved to be effective in making the touchless capacity of displays self-evident. In this paper, we focus on a child–display interaction approach to avatar-based touchless gestural interfaces

At present, we live in prosperity contrary to the past times. As income increases, people enjoy wealth, but more people tend to pursue their own inner happiness: travel. People go to other places or visit foreign countries for business or journey. This study aims to identify the best tour route for foreign tourists in South Korea. Based on the review analysis results, this paper also aims to put forward

The absence of a human driver creates novel challenges for fully automated public transport. Passengers are likely to have different expectations, needs, or even fears when traveling without a driver in potentially dangerous situations. We present the results from two field studies in which we explored incident management in a driverless shuttle bus. We explored participant’s behavior and willingness

Mobile technologies used for education may offer advantages for children with Communication Disorders, among which we can find language disorders and speech disorders, which are identified in DSM-V. In this research, we have introduced two educational activities, “Matching Cards” and “Cards & Sounds”, based on the Montessori Method and which deal with the first stages of reading and writing. We have

The aim of the present study is to identify influential factors of travel intention under the context of highly developed IT society today. Due to the development of IT and diversified ways of technology convergence, individual travelers become highly efficient in travel-related information processing. As predicted, the results of both Korean and Chinese samples show that acquaintance recommendation

With the creation of a mobile edge computing environment in which IoT technologies are converged on cloud services, the importance of high-capacity data processing technologies is increasing. In this paper, we propose a block chain–based convergence data management technique to efficiently handle different kinds of data processed in mobile edge computing environment. The proposed technique minimized

An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. Location is a key context element, particularly indoor location. Recent advances in radio ranging technologies, such as Wi-Fi RTT, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build

Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal

Microarray data analysis is a major challenging field of research in recent days. Machine learning–based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning

Many people with chronic obstructive pulmonary diseases (COPD) are subjected to emergencies triggered by breathing difficulties. Oxygen therapy, administered from medical oxygen cylinders, can be used to relieve respiratory airways, and restore the supply of oxygen to the body’s vital organs. In this paper, we present a location-based ubiquitous crowdsourcing solution to enable COPD patients to request

Fig. 7 and Fig. 8 have incorrect legends in the images and have been updated.

Recently, multimodal interfaces are incorporating smell as an additional means of interaction. Devices called olfactory displays have been designed to improve applications with various objectives, such as notifying or alerting through scents, increasing immersion in virtual or augmented reality applications, or learning and enhancement of mental functions. Based on the potential of olfactory memory

After almost a decade of relentless development, pervasive urban displays have fragmented into a diversity of approaches with radically distinct characteristics in terms of how mobile they are, as well as the materials they are made of. In this article, we investigate such a diversity in terms of the relationships between key conceptual entities of pervasive urban displays, namely the displayed content

The visual saliency models based on low-level features of an image have the problem of low accuracy and scalability, while the visual saliency models based on deep neural networks can effectively improve the prediction performance, but require a large amount of training data, e.g., eye tracking data, to achieve good results. However, the traditional eye tracking method is limited by high equipment

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy

Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision

Nowadays, automation not only permeates industry but also becomes a substantial part of our private, everyday lives. Driven by the idea of increased convenience and more time for the “important things in life,” automation relieves us from many daily chores—robots vacuum floors and automated coffee makers produce supposedly barista-quality coffee on the press of a button. In many cases, these offers

In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area

Self-tracking technologies bring a new set of experiences into our lives. Through sensors and ubiquitous measurements of bodily performance, a new form of automation experience shapes our understanding of our body and our behavior. While for many individuals self-tracking has an important role in their daily lives, a theoretical understanding of the level and behavioral manifestations of commitment

There are multiple distinct sources through which numerous news contents that occur in digital medium tend to converge. Web contents constitute massive number of features. Complete coverage of all kinds of news is absolutely vital to retain customer confidence and to have a competitive edge over other news agencies. Aggregating such massive news content from different heterogeneous sources requires

COVID-19 has caused a serious impact on the global economy. Effectively stimulating consumption has become a momentous mission in responding to the impact of the epidemic. The popularity of mobile shopping makes shopping behavior no longer limited by time and space, so impulse purchase is more commonly seen nowadays; it can effectively promote residents’ consumption. However, consensus has not been

Deep learning has revolutionized many areas, including time series data mining. Multivariate time series classification (MTSC) remained to be a well-known problem in the time series data mining community, due to its availability in various practical applications such as healthcare, finance, geoscience, and bioinformatics. Recently, multivariate long short-term memory with fully convolutional network

Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than

The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. In the narrowband Internet of Things (NB-IoT), the huge number of IoT devices sends the information to the remote network. Simultaneously, the massive IoT devices from the heterogeneous locations access to the radio remote head (RRH)

Access to knowledge, information, and technology is a key element for the development of individuals and society as a whole. While computing systems play a fundamental role in this process, efforts aimed at diminishing the worldwide digital divide are still scarce. In this study, we propose a tailoring-based approach for personal systems design as a way to promote digital and social inclusion in contexts

With the advent of deep neural networks (DNNs) in the last two decades, tremendous developments have been made in many fields, such as image classification/recognition, voice recognition, and action recognition. These advanced DNNs require large amounts of labeled data, whose collection is costly and requires great effort. In this paper, we provide a convergence method for DNNs to solve some of these