96 research outputs found
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time
Wearable artificial intelligence for anxiety and depression: A scoping review
Background:
Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services.
Objective:
The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.
Methods:
We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis.
Results:
Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM).
Conclusions:
Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression
Devices and Data Workflow in COPD Wearable Remote Patient Monitoring: A Systematic Review
Background: With global increase in Chronic Obstructive Pulmonary Disease (COPD)
prevalence and mortality rates, and socioeconomical burden continuing to rise, current
disease management strategies appear inadequate, paving the way for technological
solutions, namely remote patient monitoring (RPM), adoption considering its acute disease
events management benefit. One RPM’s category stands out, wearable devices, due to its
availability and apparent ease of use.
Objectives: To assess the current market and interventional solutions regarding wearable
devices in the remote monitoring of COPD patients through a systematic review design from
a device composition, data workflow, and collected parameters description standpoint.
Methods: A systematic review was conducted to identify wearable device trends in this
population through the development of a comprehensive search strategy, searching beyond
the mainstream databases, and aggregating diverse information found regarding the same
device. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis
(PRISMA) guidelines were followed, and quality appraisal of identified studies was
performed using the Critical Appraisal Skills Programme (CASP) quality appraisal
checklists.
Results: The review resulted on the identification of 1590 references, of which a final 79
were included. 56 wearable devices were analysed, with the slight majority belonging to the
wellness devices class. Substantial device heterogeneity was identified regarding device
composition type and wearing location, and data workflow regarding 4 considered
components. Clinical monitoring devices are starting to gain relevance in the market and
slightly over a third, aim to assist COPD patients and healthcare professionals in
exacerbation prediction. Compliance with validated recommendations is still lacking, with
no devices assessing the totality of recommended vital signs.
Conclusions: The identified heterogeneity, despite expected considering the relative
novelty of wearable devices, alerts for the need to regulate the development and research of
these technologies, specially from a structural and data collection and transmission
standpoints.Introdução: Com o aumento global das taxas de prevalência e mortalidade da Doença
Pulmonar Obstrutiva Crónica (DPOC) e o seu impacto socioeconómico, as atuais estratégias
de gestão da doença parecem inadequadas, abrindo caminho para soluções tecnológicas,
nomeadamente para a adoção da monitorização remota, tendo em conta o seu benefĂcio na
gestão de exacerbações de doenças crónicas. Dentro destaca-se uma categoria, os
dispositivos wearable, pela sua disponibilidade e aparente facilidade de uso.
Objetivos: Avaliar as soluções existentes, tanto no mercado, como na área de investigação,
relativas a dispositivos wearable utilizados na monitorização remota de pacientes com
DPOC através de uma revisão sistemática, do ponto de vista da composição do dispositivo,
fluxo de dados e descrição dos parâmetros coletados.
Métodos: Uma revisão sistemática foi realizada para identificar tendências destes
dispositivos, através do desenvolvimento de uma estratégia de pesquisa abrangente,
procurando pesquisar para além das databases convencionais e agregar diversas
informações encontradas sobre o mesmo dispositivo. Para tal, foram seguidas as diretrizes
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), e a
avaliação da qualidade dos estudos identificados foi realizada utilizando a ferramenta CASP
(Critical Appraisal Skills Programme).
Resultados: A revisão resultou na identificação de 1590 referências, das quais 79 foram
incluĂdas. Foram analisados 56 dispositivos wearable, com a ligeira maioria a pertencer Ă
classe de dispositivos de wellness. Foi identificada heterogeneidade substancial nos
dispositivos em relação à sua composição, local de uso e ao fluxo de dados em relação a 4
componentes considerados. Os dispositivos de monitorização clĂnica já evidenciam alguma
relevância no mercado e, pouco mais de um terço, visam auxiliar pacientes com DPOC e
profissionais de saúde na previsão de exacerbações. Ainda assim, é notória a falta do
cumprimento das recomendações validadas, nĂŁo estando disponĂveis dispositivos que
avaliem a totalidade dos sinais vitais recomendados.
ConclusĂŁo: A heterogeneidade identificada, apesar de esperada face Ă relativa novidade
dos dispositivos wearable, alerta para a necessidade de regulamentação do
desenvolvimento e investigação destas tecnologias, especialmente do ponto de vista
estrutural e de recolha e transmissĂŁo de dados
Sensing solutions for improving the performance, health and wellbeing of small ruminants
Diversity of production systems and specific socio-economic barriers are key reasons explaining
why the implementation of new technologies in small ruminants, despite being needed
and beneficial for farmers, is harder than in other livestock species. There are, however, helpful
peculiarities where small ruminants are concerned: the compulsory use of electronic identification
created a unique scenario in Europe in which all small ruminant breeding stock
became searchable by appropriate sensing solutions, and the largest small ruminant population
in the world is located in Asia, close to the areas producing new technologies.
Notwithstanding, only a few research initiatives and literature reviews have addressed the
development of new technologies in small ruminants. This Research Reflection focuses on
small ruminants (with emphasis on dairy goats and sheep) and reviews in a non-exhaustive
way the basic concepts, the currently available sensor solutions and the structure and elements
needed for the implementation of sensor-based husbandry decision support. Finally, some
examples of results obtained using several sensor solutions adapted from large animals or
newly developed for small ruminants are discussed. Significant room for improvement is
recognized and a large number of multiple-sensor solutions are expected to be developed
in the relatively near future
Methods for the real-world evaluation of fall detection technology : a scoping review
Falls in older adults present a major growing healthcare challenge and reliable detection
of falls is crucial to minimise their consequences. The majority of development and testing has
used laboratory simulations. As simulations do not cover the wide range of real-world scenarios
performance is poor when retested using real-world data. There has been a move from the use of
simulated falls towards the use of real-world data. This review aims to assess the current methods
for real-world evaluation of fall detection systems, identify their limitations and propose improved
robust methods of evaluation. Twenty-three articles met the inclusion criteria and were assessed with
regard to the composition of the datasets, data processing methods and the measures of performance.
Real-world tests of fall detection technology are inherently challenging and it is clear the field is in
it’s infancy. Most studies used small datasets and studies differed on how to quantify the ability to
avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and
standardise. To increase robustness and make results comparable, larger standardised datasets are
needed containing data from a range of participant groups. Measures which depend on the definition
and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the
most suitable robust measures for evaluating the real-world performance of fall detection systems
Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication
Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person's body. The Wi-Fi signals received using non-wearable devices are converted into time-frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%
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Thermal and wind devices for multisensory human-computer interaction: an overview
In order to create immersive experiences in virtual worlds, we need to explore different human senses (sight, hearing, smell, taste, and touch). Many different devices have been developed by both industry and academia towards this aim. In this paper, we focus our attention on the researched area of thermal and wind devices to deliver the sensations of heat and cold against people’s skin and their application to human-computer interaction (HCI). First, we present a review of devices and their features that were identified as relevant. Then, we highlight the users’ experience with thermal and wind devices, highlighting limitations either found or inferred by the authors and studies selected for this survey. Accordingly, from the current literature, we can infer that, in wind and temperature-based haptic systems (i) users experience wind effects produced by fans that move air molecules at room temperature, and (ii) there is no integration of thermal components to devices intended for the production of both cold or hot airflows. Subsequently, an analysis of why thermal wind devices have not been devised yet is undertaken, highlighting the challenges of creating such devices.EspĂrito Santo Research and Innovation Foundation (FAPES, Brazil) - Finance Code 2021-GL60J), the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil) - Finance Code 88881.187844/2018-01 and 88887.570688/2020-00 and by the National Council for Scientific and Technological (CNPq, Brazil) - Finance Code 307718/2020-4. The work was also funded by the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 688503. E. B. Saleme additionally acknowledges aid from the Federal Institute of EspĂrito Santo
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
A Multi-Resident Number Estimation Method for Smart Homes
Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%
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