An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence

With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community

Analyzing the effects of context-aware mobile design principles on student performance in undergraduate kinesiology courses

Learning occurs when content is accessed in a recursive process of awareness, exploration, reflection and resolution within one’s social context. With the rapid adoption of mobile technologies, mobile learning (m-Learning) researchers should incorporate aspects of mobile human-computer interaction research into the instructional design process. Specifically, the most visible, current definitions of and current research in m-Learning provide overviews of the learning theory informing mobility and focus on device characteristics, but do not focus on how people interact with mobile devices in their every day lives. The purpose of this convergent study was to determine what effect does the incorporation of research in mobile user context have on student learning. Six mobile design principles were extracted from literature and applied to mobile apps. Using a true experimental design, the study had 60 participants randomly assigned to treatment and control conditions. Participants in the treatment group received a series of apps designed according to the mobile design principles. The control group received a placebo app that mimicked content from the learning management system for their course. The results of the analysis of covariance procedure indicated the treatment group scored a significantly higher mean score than that of the control group. Further analysis of event tracking data indicated a statistically significant correlation between content access events and posttest scores. Students in the treatment group used their apps for less time, but had more content access events and subsequently higher posttest scores. The data suggests that m-Learning is something more than just an extension of what already exist. It is not just a luggable form of Web based learning. It’s more than a deep understanding of pedagogy or the delivery of course material to a mobile device. It requires the designer to understand instructional and software design, mobile human-computer usage patterns, and learning theory

The intention to use mobile digital library technology: A focus group study in the United Arab Emirates

IGI Global (“IGI”) granted Brunel University London the permission to archive this article in BURA (http://bura.brunel.ac.uk).This paper presents a qualitative study on student adoption of mobile library technology in a developing world context. The findings support the applicability of a number of existing constructs from the technology acceptance literature, such as perceived ease of use, social influence and trust. However, they also suggest the need to modify some adoption factors previously found in the literature to fit the specific context of mobile library adoption. Perceived value was found to be a more relevant overarching adoption factor than perceived usefulness for this context. Facilitating conditions were identified as important but these differed somewhat from those covered in earlier literature. The research also uncovered the importance of trialability for this type of application. The findings provide a basis for improving theory in the area of mobile library adoption and suggest a number of practical design recommendations to help designers of mobile library technology to create applications that meet user needs

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Human Factors and Innovation with Mobile Devices

Advancements in technology are a significant driving force in educational innovation, but a strong focus on technology means that human aspects and implications may not be given the attention they deserve. This chapter examines usability issues surrounding the use of mobile devices in learning. A key aim is to empower educators and learners to take control of personal devices and realise their potential in relation to teaching and learning. The background section reviews the development of usability studies and explores why mobile device usability presents specific new challenges. The impact of changing requirements in education, and new visions for ways of thinking and competences that learners should be acquiring, are also examined. Finally, the chapter provides a set of concepts that can inform conversations between educators and learners, mobile system engineers, developers, support staff, and others