Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence

Kallipolitis, Athanasios; Galliakis, Michael; Menychtas, Andreas

doi:10.1007/s00521-020-05203-z

Cited by 16 publications

(10 citation statements)

References 39 publications

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“…The i-COVID web application has been developed (Figure 1) to support the provision of remote monitoring services in the context of the project using state-of-the-art applications and technologies. The i-COVID platform deployment was based on related health information systems used in other scientific research projects [16][17][18]. The platform includes roles for all involved stakeholders as well as end-user hardware (smart devices) for the patients.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Pervasive Monitoring Solution of COVID-19 Patients

Panagopoulos¹,

Menychtas

Jahaj

et al. 2022

Studies in Health Technology and Informatics

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The COVID-19 pandemic transforms the healthcare delivery models and accelerates the implementation and the adoption of telemedicine solutions at all levels of the healthcare system. Telehealth services ensure the continuity of care and treatment of both inpatients and outpatients during this pandemic, while reducing the spread of the virus through hospitals. The aim of this paper is to present an intelligent remote monitoring system with innovative data analytics features for COVID-19 patients. The i-COVID platform provides remote COVID-19 patients monitoring. The presented solution is addressed to patients with mild COVID-19 symptoms, as well as it can be used for post intensive-care monitoring. The platform offers advanced analytic capabilities using Proactive AI, to detect health condition deterioration, and automatically trigger personalized support workflows. Remote monitoring of COVID-19 patients using bio-sensors, seems to be an effective tool against the COVID-19 pandemic, as reduces the number of visits to patient screening centres and hospital admissions.

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Section: Resultsmentioning

confidence: 99%

Intelligent Pervasive Monitoring Solution of COVID-19 Patients

Panagopoulos¹,

Menychtas

Jahaj

et al. 2022

Studies in Health Technology and Informatics

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“…Individuals with depression in remission could conversely be monitored for signs of relapse. Preliminary studies have shown that integration of real-time audiovisual analysis into telemedicine platforms may be a feasible method of detecting an individual's emotional state ( 82 ). Additionally, the use of smartphone and wearables technology to record these features have also demonstrated feasibility and acceptability in initial pilot studies ( 55 , 83 ).…”

Section: Complementary and Novel Strategies To Measure Depressionmentioning

confidence: 99%

“…Amount of detected speech was positively associated with overall well-being. of detecting an individual's emotional state (82). Additionally, the use of smartphone and wearables technology to record these features have also demonstrated feasibility and acceptability in initial pilot studies (55,83).…”

Section: Complementary and Novel Strategies To Measure Depressionmentioning

confidence: 99%

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

DeSouza¹,

Robin²,

Gumus³

et al. 2021

Front. Psychiatry

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Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.

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“…The usage of mHealth technologies has been also linked to highly positive impact on public health [10,11]. By measuring and continuously monitoring the user's physical activity and vital signs, mHealth applications can be used to collect and analyse biosignals to ensure the users' good health status [12][13][14][15][16][17]. Finally, mHealth solutions can encourage a healthier lifestyle through coaching mechanisms that promote physical activity [18,19].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Citizens’ Well-Being in Areas with Natural Based Solutions Using Mobile Computing

Gallos

Menychtas

Panagopoulos³

et al. 2022

Studies in Health Technology and Informatics

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Urban planners, architects and civil engineers are integrating Nature-Based Solutions (NBS) to address contemporary environmental, social, health and economic challenges. Many studies claim that NBS are poised to improve citizens’ well-being in urban areas. NBS can also benefit Public Health, as they can contribute to optimising environmental parameters (such as urban heat island effects, floods, etc.), as well as to the reduction of diseases, as for example cardiovascular ones and the overall mortality rate. In addition, the usage of mobile health (mHealth) solutions has been broadly applied to support citizens’ well-being as they can offer monitoring of their physical and physiological status and promote a healthier lifestyle. The aim of this paper is to present the specifications, the design and the development of a mobile app for monitoring citizens’ well-being in areas where NBS have been applied. The users’ physical activity and vital signs are recorded by wearable devices and the users’ locations are recorded by the proposed mobile application. All collected data are transferred to the cloud platform where data management mechanisms aggregate data from different sources for combined analysis. The mobile application is currently available for Android and iOS devices and it is compatible with most smart devices and wearables. The “euPOLIS by BioAssist” application can be used as a health and other data collection tool to investigate citizen’s well-being improvement in areas with NBS.

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Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence

Cited by 16 publications

References 39 publications

Intelligent Pervasive Monitoring Solution of COVID-19 Patients

Intelligent Pervasive Monitoring Solution of COVID-19 Patients

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Quantifying Citizens’ Well-Being in Areas with Natural Based Solutions Using Mobile Computing

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