19,782 research outputs found
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
Physical exercise is an essential component of rehabilitation programs that
improve quality of life and reduce mortality and re-hospitalization rates. In
AI-driven virtual rehabilitation programs, patients complete their exercises
independently at home, while AI algorithms analyze the exercise data to provide
feedback to patients and report their progress to clinicians. To analyze
exercise data, the first step is to segment it into consecutive repetitions.
There has been a significant amount of research performed on segmenting and
counting the repetitive activities of healthy individuals using raw video data,
which raises concerns regarding privacy and is computationally intensive.
Previous research on patients' rehabilitation exercise segmentation relied on
data collected by multiple wearable sensors, which are difficult to use at home
by rehabilitation patients. Compared to healthy individuals, segmenting and
counting exercise repetitions in patients is more challenging because of the
irregular repetition duration and the variation between repetitions. This paper
presents a novel approach for segmenting and counting the repetitions of
rehabilitation exercises performed by patients, based on their skeletal body
joints. Skeletal body joints can be acquired through depth cameras or computer
vision techniques applied to RGB videos of patients. Various sequential neural
networks are designed to analyze the sequences of skeletal body joints and
perform repetition segmentation and counting. Extensive experiments on three
publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and
IntelliRehabDS, demonstrate the superiority of the proposed method compared to
previous methods. The proposed method enables accurate exercise analysis while
preserving privacy, facilitating the effective delivery of virtual
rehabilitation programs.Comment: 8 pages, 1 figure, 2 table
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
UniverSeg: Universal Medical Image Segmentation
While deep learning models have become the predominant method for medical
image segmentation, they are typically not capable of generalizing to unseen
segmentation tasks involving new anatomies, image modalities, or labels. Given
a new segmentation task, researchers generally have to train or fine-tune
models, which is time-consuming and poses a substantial barrier for clinical
researchers, who often lack the resources and expertise to train neural
networks. We present UniverSeg, a method for solving unseen medical
segmentation tasks without additional training. Given a query image and example
set of image-label pairs that define a new segmentation task, UniverSeg employs
a new Cross-Block mechanism to produce accurate segmentation maps without the
need for additional training. To achieve generalization to new tasks, we have
gathered and standardized a collection of 53 open-access medical segmentation
datasets with over 22,000 scans, which we refer to as MegaMedical. We used this
collection to train UniverSeg on a diverse set of anatomies and imaging
modalities. We demonstrate that UniverSeg substantially outperforms several
related methods on unseen tasks, and thoroughly analyze and draw insights about
important aspects of the proposed system. The UniverSeg source code and model
weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project
Website: https://universeg.csail.mit.ed
The place where curses are manufactured : four poets of the Vietnam War
The Vietnam War was unique among American wars. To pinpoint its uniqueness, it was necessary to look for a non-American voice that would enable me to articulate its distinctiveness and explore the American character as observed by an Asian. Takeshi Kaiko proved to be most helpful. From his novel, Into a Black Sun, I was able to establish a working pair of 'bookends' from which to approach the poetry of Walter McDonald, Bruce Weigl, Basil T. Paquet and Steve Mason. Chapter One is devoted to those seemingly mismatched 'bookends,' Walt Whitman and General William C. Westmoreland, and their respective anthropocentric and technocentric visions of progress and the peculiarly American concept of the "open road" as they manifest themselves in Vietnam. In Chapter, Two, I analyze the war poems of Walter McDonald. As a pilot, writing primarily about flying, his poetry manifests General Westmoreland's technocentric vision of the 'road' as determined by and manifest through technology. Chapter Three focuses on the poems of Bruce Weigl. The poems analyzed portray the literal and metaphorical descent from the technocentric, 'numbed' distance of aerial warfare to the world of ground warfare, and the initiation of a 'fucking new guy,' who discovers the contours of the self's interior through a set of experiences that lead from from aerial insertion into the jungle to the degradation of burning human
feces. Chapter Four, devoted to the thirteen poems of Basil T. Paquet, focuses on the continuation of the descent begun in Chapter Two. In his capacity as a medic, Paquet's entire body of poems details his quotidian tasks which entail tending the maimed, the mortally wounded and the dead. The final chapter deals with Steve Mason's JohnnY's Song, and his depiction of the plight of Vietnam veterans back in "The World" who are still trapped inside the interior landscape of their individual "ghettoes" of the soul created by their war-time experiences
The motivational value of listening during intimate and difficult conversations
Abstract: Outcomes of conversations, including those dealing with controversial, deeply personal, or threatening disclosures, result not only from what is said but also from how listeners receive these messages. This article integrates the motivational framework of self‐determination theory (SDT) and the expanding literature on interpersonal listening to explore the reasons why high‐quality listening is so impactful during these conversations. We describe why high‐quality listening is a specific and distinguishable autonomy‐supportive motivational strategy, and argue that there is much to gain by considering that listening can satisfy basic psychological needs, in particular for autonomy and relatedness. We argue that SDT can help explain why high‐quality listening is effective, especially in reducing defensiveness, bridging divides, and motivating change. The discussion focuses on ways motivation science can build more effective interventions for behavioral change by harnessing listening as an interpersonal strategy
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