Evaluating Mental Patients Utilizing Video Analysis of Facial Expressions

Tziomaka, Melina; Kallipolitis, Athanasios; Tsanakas, Panayiotis

doi:10.1007/978-3-030-79157-5_16

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…For that reason, we try to assess the degree of symptoms severity in patients with mental disorders based on their social behavior and cognitive functioning, while they are conducting the weekly interviews with the clinicians. Specifically, we aim to automatically recognize the alterations in psychopathology, which are determined through the Positive and Negative Syndrome Scale (PANSS) [ 40 ], using features extracted from the patients’ facial expressions [ 125 ].…”

Section: Material Methods and Research Resultsmentioning

confidence: 99%

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Zlatintsi

Filntisis

Garoufis

et al. 2022

Sensors

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Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

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Section: Material Methods and Research Resultsmentioning

confidence: 99%

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Zlatintsi

Filntisis

Garoufis

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Despite the obvious necessity for interpretability, there are still many approaches that are presented without the provision of an inherent interpretability scheme or the capability for future extension [22]. Therefore, it is highly recommended that ML systems in the healthcare domain are accompanied by an interpretability scheme that returns plausible explanations for their decisions and a straightforward connection between the cause and effect [23,24].…”

Section: Introductionmentioning

confidence: 99%

Extracting Interpretable Knowledge from the Remote Monitoring of COVID-19 Patients

Tziomaka,

Kallipolitis,

Menychtas

et al. 2024

MAKE

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Apart from providing user-friendly applications that support digitized healthcare routines, the use of wearable devices has proven to increase the independence of patients in a healthcare setting. By applying machine learning techniques to real health-related data, important conclusions can be drawn for unsolved issues related to disease prognosis. In this paper, various machine learning techniques are examined and analyzed for the provision of personalized care to COVID-19 patients with mild symptoms based on individual characteristics and the comorbidities they have, while the connection between the stimuli and predictive results are utilized for the evaluation of the system’s transparency. The results, jointly analyzing wearable and electronic health record data for the prediction of a daily dyspnea grade and the duration of fever, are promising in terms of evaluation metrics even in a specified stratum of patients. The interpretability scheme provides useful insight concerning factors that greatly influenced the results. Moreover, it is demonstrated that the use of wearable devices for remote monitoring through cloud platforms is feasible while providing awareness of a patient’s condition, leading to the early detection of undesired changes and reduced visits for patient screening.

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Evaluating Mental Patients Utilizing Video Analysis of Facial Expressions

Cited by 2 publications

References 25 publications

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Extracting Interpretable Knowledge from the Remote Monitoring of COVID-19 Patients

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