Building a context rich interface to low level sensor data

Sensor networks play an important role in our modern information society. These networks are used for a variety of activities in different domains, including traffic monitoring, environmental analysis, transport and personal health. In general, systems generate data in their own format with little or no associated semantics. As a result, data must be managed individually and significant human effort is required to analyze data and develop ad-hoc applications for different end-user requirements. The research presented here proposes a holistic and comprehensive approach to significantly reduce the human effort in analyzing networks of sensors. The goal is to facilitate any form of sensor network, enabling users to combine related semantics with sensor data, and facilitate the end-user transformation of data necessary to provide more complex query expressions, and thus meet the analytical requirements

AN INVESTIGATION OF ONLINE LEARNING READINESS AMONG PHYSICAL EDUCATION STUDENTS IN VIETNAM

Cardiorespiratory fitness is the ability to maintain moderate or high-intensity efforts for In light of the global Covid pandemic, educational institutions worldwide have shifted to online modalities, presenting both opportunities and challenges. This study offers an in-depth examination of the online learning readiness among students at the Ho Chi Minh City University of Physical Education and Sports in Vietnam. Drawing from comprehensive data, we assess critical factors determining successful online learning experiences. These factors include the availability and adequacy of students' technological equipment, their personal internet access quality and reliability, the specific online learning intentions and motivations of the 14th intake students, and the hurdles they encountered when interfacing with the learning management system. Additionally, the research sheds light on potential pedagogical adjustments and infrastructural enhancements that can be made to streamline the transition. By discerning the precise state of online learning preparedness and the challenges faced, this research not only gauges the current state of affairs but also provides actionable insights aimed at optimizing the effectiveness of digital instruction at the Ho Chi Minh City University of Physical Education and Sports.  Article visualizations

Using sensor networks to measure intensity in sporting activities

The deployment of sensor networks is both widespread and varied with more niche applications based on these networks. In the case study provided in this work, the network is provided by two football teams with sensors generating continuous heart rate values for the duration of the activity. In wireless networks such as these, the requirement is for complex methods of data management in order to deliver more and more powerful query results. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. This paper describes a system and series of experiments that requires powerful data management capabilities to meet the requirements of sports scientists

Context-Aware Service Discovering System for Nomad Users

International audienceThis paper presents an architecture for a system that provides nomad users, context-aware personalised services. Users might need any sort of services: information about the weather forecast for the next day, or about a museum in the neighbour worth to visit. These services are known as stateless services. More complex situations ocurre when services are stateful. Such services are, for example those which need users to be logged in (e.g. booking a room in a hotel). The question discussed in the text are those related to: i) user's privacy, ii) recommendation and discovery of services, iii) composition of recommended services into a composite service, and iv) execution of the resulting composite service

Data transformation and query management in personal health sensor networks

Sensor technology has been exploited in many application areas ranging from climate monitoring, to traffic management, and healthcare. The role of these sensors is to monitor human beings, the environment or instrumentation and provide continuous streams of information regarding their status or well being. In the case study presented in this work, the network is provided by football teams with sensors generating continuous heart rate values during a number of different sporting activities. In wireless networks such as these, the requirement is for methods of data management and transformation in order to present data in a format suited to high level queries. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. The challenge arises from the gap that emerges between the low level sensor output and the high level user requirements of the domain experts. This paper describes a process to close this gap by automatically harvesting the raw sensor data and providing semantic enrichment through the addition of context data

"You Tube and I Find" - personalizing multimedia content access

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhereVfrom personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, fromcreation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itselfVits affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today’s facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user’s personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

Determinants of Users Intention to Adopt Mobile Fitness Applications: an Extended Technology Acceptance Model Approach

The present research was motivated by the recognition that the use of mobile fitness applications (MFA) is increasingly popular among sports and exercise participants in recent years. Using an extended Technology Acceptance Model (TAM) perspective, this study explored potential predictors of behavioral intention toward MFAs such as perceived ease of use, perceived usefulness, personalization, personal innovativeness in information technology (PIIT), perceived enjoyment, mobile application self-efficacy, involvement in sports and exercise participation, and social influences (interpersonal and external influences). A theoretical model was developed and tested against the empirical data collected from 385 collegiate students enrolled in physical activity classes at a large university in the United States. The result of descriptive statistics indicated that the samples are active sports and exercise participants with their weekly exercise and sports participation of 5.41 hours. A measurement model and structural equation model were tested using AMOS 22.0 and confirmed eight out of eleven hypothesized relationships. In particular, personalization and PIIT were found to have significant effects on perceived usefulness and perceived ease of use, which in turn, affected behavioral intention toward using MFAs. Interpersonal influence and involvement in sports and exercise participation were also found to have significant effects on intention whereas no significant effects of mobile application self-efficacy, perceived enjoyment, and external influence were observed. The analyses demonstrated that perceived usefulness was the most powerful determinants of behavioral intention followed by interpersonal influence in terms of the path coefficient values. The construct of PIIT and personalization accounted for 43.4% variances in perceived ease of use and 48.9% variances in perceived usefulness variance. All the constructs within the structural model except external influence, perceived enjoyment, and mobile application self-efficacy, collectively explained the 75.1 % variances in intention to use MFAs, suggesting that the examined model has a strong explanatory power regarding MFA users decision making process.\u2