3,513 research outputs found

    Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

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    Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

    ALT-C 2010 - Conference Introduction and Abstracts

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    Scaling up: Achieving a breakthrough in adult learning with technology

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    The first report commissioned by Ufi Charitable Trust. It investigates opportunities for and barriers to the application of digital technology to adult learning. It focuses on possible ways to transform the UK’s vocational education and training system, identifying three main priorities for funding by the Ufi Charitable Trust: * increasing the capability of those involved in running the vocational learning system * exploiting networks to bring together learners, learning content and learning professionals * harnessing computers to support individualised and differentiated learning

    Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

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    Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

    Designing a Virtual Embedded Scenario-Based Military Simulation Training Program using Educational and Design Instructional Strategies

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    The purpose of this dissertation in practice was to develop and implement a new training program for designers of military intelligence simulation scenarios used to train soldiers. The use of education and design instructional strategies assisted in the ability for designers to gain mastery skills in creating realistic, high-fidelity scenarios that are applied in the training process. The use of simulation scenarios to train adult learners has increased significantly with improvements in technology and its fidelity to engage learners in a realistic way. Despite these advances, the lack of effective design, implementation and analysis of military simulation training programs in the military intelligence community has led to a decrease in simulation utilization, as in the case of the organization examined in this problem of practice. The current training program\u27s increasing difficulties with consistent use by military intelligence simulation scenario designers were discovered in the results of a gap analysis conducted in 2014, prompting this design. This simulation design aimed to examine: (1) a research-based design methodology to match training requirements for the designers, (2) formative assessment of performance and (3) a research-based evaluation framework to determine the effectiveness of the new training program. For the organization\u27s training program, a Simulation-Based Embedded Training (SBET) solution using scenarios was conceived based on research grounded in cognitive theory and instructional design considerations for simulations. As a structured framework for how to design and implement an effective and sustained training program, the educational instructional design model, ADDIE, was used. This model allowed for continual flexibility in each phase to evaluate and implement changes iteratively. The instructional model and its techniques were used with fidelity, specifically for training the designers of the simulation system. Industries will continue to increase the use of simulation as advances in technologies offer more realistic, safe, and complex training environments. A detailed strategy was provided specific to the organization using a research-based instructional approach integrated into program requirements set forth by the government. This proposed solution, supported by research in the application of instructional strategies, is specific to this organization; however, the training program design differs from other high-fidelity military simulator training programs through its use of dispersed training to the simulation scenario designers using realistic scenarios to mimic the tasks that the designers themselves must create. The difference in the solution in this dissertation in practice is: 1) that the simulation scenarios are designed without the help of subject matter experts by using the embedded instructional strategies and 2) the design is to the fidelity of realism required for military intelligence training exercises
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