15,985 research outputs found
Use of personal call alarms among community-dwelling older people.
Having a fall and then lying on the floor for an hour or more is known as a âlong lieâ, which are associated with serious injury and an elevated risk of admission to hospital, long-term care, and death. Personal call alarms are designed to prevent long lies, although little is known about their use. Using cross-sectional data from the English Longitudinal Study on Ageing, this study investigated the proportion of self-reported users of personal call alarms among 3091 community-dwelling adults aged 65+ who reported difficulties of mobility or activities of daily living. The characteristics of users were then explored through logistic regressions comparing those living alone with those living with others. One hundred and eighty people self-reported using a personal call alarm (6%). Multivariate regression found the following to significantly predict personal call alarm use among both those living alone and with others: greater difficulty with activities / instrumental activities of daily living, older age, and for those living with others only: lower score on the quality of life subscale for control. Personal call alarm use may be markedly lower than the 30 per cent annual incidence of falls among community-dwelling older people. Better understanding is needed of the reasons for low usage, even amongst those at highest falls risk for whom such alarms are most likely to be beneficial
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Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Characterizing Pedophile Conversations on the Internet using Online Grooming
Cyber-crime targeting children such as online pedophile activity are a major
and a growing concern to society. A deep understanding of predatory chat
conversations on the Internet has implications in designing effective solutions
to automatically identify malicious conversations from regular conversations.
We believe that a deeper understanding of the pedophile conversation can result
in more sophisticated and robust surveillance systems than majority of the
current systems relying only on shallow processing such as simple word-counting
or key-word spotting.
In this paper, we study pedophile conversations from the perspective of
online grooming theory and perform a series of linguistic-based empirical
analysis on several pedophile chat conversations to gain useful insights and
patterns. We manually annotated 75 pedophile chat conversations with six stages
of online grooming and test several hypothesis on it. The results of our
experiments reveal that relationship forming is the most dominant online
grooming stage in contrast to the sexual stage. We use a widely used
word-counting program (LIWC) to create psycho-linguistic profiles for each of
the six online grooming stages to discover interesting textual patterns useful
to improve our understanding of the online pedophile phenomenon. Furthermore,
we present empirical results that throw light on various aspects of a pedophile
conversation such as probability of state transitions from one stage to
another, distribution of a pedophile chat conversation across various online
grooming stages and correlations between pre-defined word categories and online
grooming stages
A protected discharge facility for the elderly: design and validation of a working proof-of-concept
With the increasing share of elderly population worldwide, the need for assistive
technologies to support clinicians in monitoring their health conditions is becoming
more and more relevant. As a quantitative tool, geriatricians recently proposed the
notion of frail elderly, which rapidly became a key element of clinical practices for the
estimation of well-being in aging population. The evaluation of frailty is commonly
based on self-reported outcomes and occasional physicians evaluations, and may
therefore contain biased results.
Another important aspect in the elderly population is hospitalization as a risk factor
for patient\u2019s well being and public costs. Hospitalization is the main cause of functional
decline, especially in older adults. The reduction of hospitalization time may
allow an improvement of elderly health conditions and a reduction of hospital costs.
Furthermore, a gradual transition from a hospital environment to a home-like one,
can contribute to the weaning of the patient from a condition of hospitalization to a
condition of discharge to his home. The advent of new technologies allows for the
design and implementation of smart environments to monitor elderly health status
and activities, fulfilling all the requirements of health and safety of the patients.
From these starting points, in this thesis I present data-driven methodologies to
automatically evaluate one of the main aspects contributing to the frailty estimation,
i.e., the motility of the subject. First I will describe a model of protected discharge
facility, realized in collaboration and within the E.O. Ospedali Galliera (Genoa, Italy),
where patients can be monitored by a system of sensors while physicians and nurses
have the opportunity to monitor them remotely. This sensorised facility is being
developed to assist elderly users after they have been dismissed from the hospital
and before they are ready to go back home, with the perspective of coaching them
towards a healthy lifestyle. The facility is equipped with a variety of sensors (vision,
depth, ambient and wearable sensors and medical devices), but in my thesis I primarily
focus on RGB-D sensors and present visual computing tools to automatically
estimate motility features. I provide an extensive system assessment I carried out onthree different experimental sessions with help of young as well as healthy aging volunteers. The results I present are in agreement with the assessment manually
performed by physicians, showing the potential capability of my approach to complement
current protocols of evaluation
NĂ€gemistaju automaatsete protsesside eksperimentaalne uurimine
VĂ€itekirja elektrooniline versioon ei sisalda publikatsiooneVĂ€itekiri keskendub nĂ€gemistaju protsesside eksperimentaalsele uurimisele, mis on suuremal vĂ”i vĂ€hemal mÀÀral automaatsed. Uurimistöös on kasutatud erinevaid eksperimentaalseid katseparadigmasid ja katsestiimuleid ning nii kĂ€itumuslikke- kui ka ajukuvamismeetodeid. Esimesed kolm empiirilist uurimust kĂ€sitlevad liikumisinformatsiooni töötlust, mis on evolutsiooni kĂ€igus kujunenud ĂŒheks olulisemaks baasprotsessiks nĂ€gemistajus. Esmalt huvitas meid, kuidas avastatakse liikuva objekti suunamuutusi, kui samal ajal toimub ka taustal liikumine (Uurimus I). NĂ€gemistaju uurijad on pikka aega arvanud, et liikumist arvutatakse alati mĂ”ne vĂ€lise objekti vĂ”i tausta suhtes. Meie uurimistulemused ei kinnitanud taolise suhtelise liikumise printsiibi paikapidavust ning toetavad pigem seisukohta, et eesmĂ€rkobjekti liikumisinformatsiooni töötlus on automaatne protsess, mis tuvastab silma pĂ”hjas toimuvaid nihkeid, ja taustal toimuv seda eriti ei mĂ”juta. Teise uurimuse tulemused (Uurimus II) nĂ€itasid, et nĂ€gemissĂŒsteem töötleb vĂ€ga edukalt ka seda liikumisinformatsiooni, millele vaatleja teadlikult tĂ€helepanu ei pööra. See tĂ€hendab, et samal ajal, kui inimene on mĂ”ne tĂ€helepanu hĂ”lmava tegevusega ametis, suudab tema aju taustal toimuvaid sĂŒndmusi automaatselt registreerida. IgapĂ€evaselt on inimese nĂ€gemisvĂ€ljas alati palju erinevaid objekte, millel on erinevad omadused, mistĂ”ttu jĂ€rgmiseks huvitas meid (Uurimus III), kuidas ĂŒhe tunnuse (antud juhul vĂ€rvimuutuse) töötlemist mĂ”jutab mĂ”ne teise tunnusega toimuv (antud juhul liikumiskiiruse) muutus. NĂ€itasime, et objekti liikumine parandas sama objekti vĂ€rvimuutuse avastamist, mis viitab, et nende kahe omaduse töötlemine ajus ei ole pĂ€ris eraldiseisev protsess. Samuti tĂ€hendab taoline tulemus, et hoolimata ĂŒhele tunnusele keskendumisest ei suuda inimene ignoreerida teist tĂ€helepanu tĂ”mbavat tunnust (liikumine), mis viitab taas kord automaatsetele töötlusprotsessidele. Neljas uurimus keskendus emotsionaalsete nĂ€ovĂ€ljenduste töötlusele, kuna need kannavad keskkonnas hakkamasaamiseks vajalikke sotsiaalseid signaale, mistĂ”ttu on alust arvata, et nende töötlus on kujunenud suuresti automaatseks protsessiks. NĂ€itasime, et emotsiooni vĂ€ljendavaid nĂ€gusid avastati kiiremini ja kergemini kui neutraalse ilmega nĂ€gusid ning et vihane nĂ€gu tĂ”mbas rohkem tĂ€helepanu kui rÔÔmus (Uurimus IV). VĂ€itekirja viimane osa puudutab visuaalset lahknevusnegatiivsust (ingl Visual Mismatch Negativity ehk vMMN), mis nĂ€itab aju vĂ”imet avastada automaatselt erinevusi enda loodud mudelist ĂŒmbritseva keskkonna kohta. Selle automaatse erinevuse avastamise mehhanismi uurimisse andsid oma panuse nii Uurimus II kui Uurimus IV, mis mĂ”lemad pakuvad vĂ€lja tĂ”endusi vMMN tekkimise kohta eri tingimustel ja katseparadigmades ning ka vajalikke metodoloogilisi tĂ€iendusi. Uurimus V on esimene kogu siiani ilmunud temaatilist teadustööd hĂ”lmav ĂŒlevaateartikkel ja metaanalĂŒĂŒs visuaalsest lahknevusnegatiivsusest psĂŒhhiaatriliste ja neuroloogiliste haiguste korral, mis panustab oluliselt visuaalse lahknevusnegatiivsuse valdkonna arengusse.The research presented and discussed in the thesis is an experimental exploration of processes in visual perception, which all display a considerable amount of automaticity. These processes are targeted from different angles using different experimental paradigms and stimuli, and by measuring both behavioural and brain responses. In the first three empirical studies, the focus is on motion detection that is regarded one of the most basic processes shaped by evolution. Study I investigated how motion information of an object is processed in the presence of background motion. Although it is widely believed that no motion can be perceived without establishing a frame of reference with other objects or motion on the background, our results found no support for relative motion principle. This finding speaks in favour of a simple and automatic process of detecting motion, which is largely insensitive to the surrounding context. Study II shows that the visual system is built to automatically process motion information that is outside of our attentional focus. This means that even if we are concentrating on some task, our brain constantly monitors the surrounding environment. Study III addressed the question of what happens when multiple stimulus qualities (motion and colour) are present and varied, which is the everyday reality of our visual input. We showed that velocity facilitated the detection of colour changes, which suggests that processing motion and colour is not entirely isolated. These results also indicate that it is hard to ignore motion information, and processing it is rather automatically initiated. The fourth empirical study focusses on another example of visual input that is processed in a rather automatic way and carries high survival value â emotional expressions. In Study IV, participants detected emotional facial expressions faster and more easily compared with neutral facial expressions, with a tendency towards more automatic attention to angry faces. In addition, we investigated the emergence of visual mismatch negativity (vMMN) that is one of the most objective and efficient methods for analysing automatic processes in the brain. Study II and Study IV proposed several methodological gains for registering this automatic change-detection mechanism. Study V is an important contribution to the vMMN research field as it is the first comprehensive review and meta-analysis of the vMMN studies in psychiatric and neurological disorders
MAISON -- Multimodal AI-based Sensor platform for Older Individuals
There is a global aging population requiring the need for the right tools
that can enable older adults' greater independence and the ability to age at
home, as well as assist healthcare workers. It is feasible to achieve this
objective by building predictive models that assist healthcare workers in
monitoring and analyzing older adults' behavioral, functional, and
psychological data. To develop such models, a large amount of multimodal sensor
data is typically required. In this paper, we propose MAISON, a scalable
cloud-based platform of commercially available smart devices capable of
collecting desired multimodal sensor data from older adults and patients living
in their own homes. The MAISON platform is novel due to its ability to collect
a greater variety of data modalities than the existing platforms, as well as
its new features that result in seamless data collection and ease of use for
older adults who may not be digitally literate. We demonstrated the feasibility
of the MAISON platform with two older adults discharged home from a large
rehabilitation center. The results indicate that the MAISON platform was able
to collect and store sensor data in a cloud without functional glitches or
performance degradation. This paper will also discuss the challenges faced
during the development of the platform and data collection in the homes of
older adults. MAISON is a novel platform designed to collect multimodal data
and facilitate the development of predictive models for detecting key health
indicators, including social isolation, depression, and functional decline, and
is feasible to use with older adults in the community
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