983 research outputs found
Active and assisted living ecosystem for the elderly
A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Towards rapid modeling and prototyping of indoor and outdoor monitoring applications
Nowadays, the capability to remotely monitor indoor and outdoor environments would allow to reduce energy consumption and improve the overall management and users’ experience of network application systems. The most known solutions adopting remote control are related to domotics (e.g., smart homes and industry 4.0 applications). An important stimulus for the development of such smart approaches is the growth of the Internet of Things (IoT) technologies and the increasing investment in the development of green houses, buildings, and, in general, heterogeneous environments. While the benefits for the humans and the environment are evident, a pervasive adoption and distribution of remote monitoring solutions are hindered by the following issue: modeling, designing, prototyping, and further developing the remote applications and underlying architecture require a certain amount of time. Moreover, such systems must be often customized on the basis of the need of the specific domain and involved entities. For such reasons, in this paper, we provide the experience made in addressing some relevant indoor and outdoor case studies through IoT-targeted tools, technologies and protocols, highlighting the advantages and disadvantages of the considered solutions as well as insights that can be useful for future practitioners
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Human Activity Recognition and Fall Detection Using Unobtrusive Technologies
As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people
Developing a Governance Framework for a Commercially Successful, Inclusive, and Safe Metaverse
This study aimed to fill a significant gap in the literature on empirical research on the governance structure of the Metaverse. The purpose of this study was to describe Metaverse strategy and innovation management experts’ views on how business leaders and policymakers may collaborate on shaping a governance structure for the Metaverse. This study used a multiple case study design to collect data from a purposeful sample of eight Metaverse experts using a semistructured interview format. This study was framed by three key concepts: Rogers’s concept of diffusion of innovations, Ball’s concept of the Metaverse, and Fernandez and Hiu’s concept of privacy, ethics, and governance in the Metaverse. Twelve themes emerged from the analysis of the data: (a) the Metaverse as disruptors across all industries, (b) human adoption and collaboration as drivers of future business innovation in the Metaverse, (c) the challenge of interoperability across public and private platforms, (d) global network of stakeholders that fosters a holistic and innovative approach to data governance, (e) governance framework that creates value for the consumer, (f) centralized and decentralized options for governance, (g), advantages and challenges of user control over personal data, (h) collaborative policies as regulators of human behavior in the Metaverse, (i) multi-stakeholder generated Metaverse security and privacy policy, (j) policies that regulate user-generated content, (k) incorporation of diversity, equity, and inclusion principles for organizations operating in the Metaverse, and (l) accessibility to all consumers. This study’s result may drive positive social change by presenting practical information on developing a governance framework to regulate the Metaverse
Effective behavior change techniques in digital health interventions targeting non-communicable diseases: an umbrella review
Background: Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of common non-communicable diseases (NCDs), it is unclear what specific components make an intervention effective in changing human behavior.Purpose: The aim of this umbrella review was to identify the most effective behavior change techniques (BCTs) in DHIs that address the most common NCDs. Methods: Five electronic databases were searched for articles published in English between 1st January 2007 and 24th January 2021. Studies were included if they were systematic reviews or meta-analyses of e- or mHealth interventions targeting the modification of one or more NCD-related risk factors in adults. Study quality was assessed using AMSTAR 2. Sixty-one articles, spanning 10 health domains and comprising over half a million individual participants, were included in the review. Results: DHIs are favorably associated with improved health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest education, communication with a professional, tailored reminders, goals and planning, feedback and monitoring, and personalization components increase the effectiveness of DHIs targeting NCDs and lifestyle behaviors. Conclusions: Common BCTs across health domains, such as ‘goals and planning’, increase DHI effectiveness and should be prioritized for inclusion within future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines
Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds
With the emergence of Cloud computing, Internet of Things-enabled
Human-Computer Interfaces, Generative Artificial Intelligence, and
high-accurate Machine and Deep-learning recognition and predictive models,
along with the Post Covid-19 proliferation of social networking, and remote
communications, the Metaverse gained a lot of popularity. Metaverse has the
prospective to extend the physical world using virtual and augmented reality so
the users can interact seamlessly with the real and virtual worlds using
avatars and holograms. It has the potential to impact people in the way they
interact on social media, collaborate in their work, perform marketing and
business, teach, learn, and even access personalized healthcare. Several works
in the literature examine Metaverse in terms of hardware wearable devices, and
virtual reality gaming applications. However, the requirements of realizing the
Metaverse in realtime and at a large-scale need yet to be examined for the
technology to be usable. To address this limitation, this paper presents the
temporal evolution of Metaverse definitions and captures its evolving
requirements. Consequently, we provide insights into Metaverse requirements. In
addition to enabling technologies, we lay out architectural elements for
scalable, reliable, and efficient Metaverse systems, and a classification of
existing Metaverse applications along with proposing required future research
directions
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