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Boz HE, Limoncu H, Zygouris S, Tsolaki M, Giakoumis D, Votis K, Tzovaras D, Öztürk V, Yener G. A short assessment tool for small vessel disease with cognitive impairment: The Virtual Supermarket (VSM). Alzheimers Dement 2020. [DOI: 10.1002/alz.047040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | | | - Magda Tsolaki
- Aristotle University of Thessaloniki Thessaloniki Greece
| | - Dimitris Giakoumis
- Centre for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI) Thessaloniki Greece
| | - Konstantinos Votis
- Centre for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI) Thessaloniki Greece
| | - Dimitris Tzovaras
- Center for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI) Thessaloniki Greece
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Abdelnour C, Tantyna N, Hernandez J, Giakoumis D, Ribes JC, Gerlowska J, Skrobas U, Korchut A, Grabowska K, Szklener S, Hernandez I, Rosende‐Roca M, Mauleon A, Vargas L, Alegret M, Espinosa A, Ortega G, Sanchez D, Rodriguez‐Gomez O, Sanabria A, Perez A, Canabate P, Moreno M, Preckler S, Ruiz A, Rejdak K, Tzovaras D, Tarraga L, Boada M. [P4–322]: ARE THERE DIFFERENCES IN THE OPINION OF PATIENTS WITH ALZHEIMER DISEASE AND THEIR CAREGIVERS ABOUT HAVING SUPPORT FROM A SERVICE ROBOT AT HOME? Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.06.2192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Carla Abdelnour
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Natalia Tantyna
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Joan Hernandez
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Dimitris Giakoumis
- Centre for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI)ThessalonikiGreece
| | - Joan Carles Ribes
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | | | | | | | | | | | - Isabel Hernandez
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | | | - Ana Mauleon
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Liliana Vargas
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Monserrat Alegret
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Ana Espinosa
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Gemma Ortega
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Domingo Sanchez
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | | | - Angela Sanabria
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Alba Perez
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Pilar Canabate
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Mariola Moreno
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Silvia Preckler
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Agustín Ruiz
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | | | - Dimitrios Tzovaras
- Centre for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI)ThessalonikiGreece
| | - Lluis Tarraga
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
| | - Mercè Boada
- Fundació ACE. Barcelona Alzheimer Treatment & Research CenterBarcelonaSpain
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Gaggioli A, Cipresso P, Serino S, Pioggia G, Tartarisco G, Baldus G, Corda D, Ferro M, Carbonaro N, Tognetti A, De Rossi D, Giakoumis D, Tzovaras D, Riera A, Riva G. A decision support system for real-time stress detection during virtual reality exposure. Stud Health Technol Inform 2014; 196:114-120. [PMID: 24732491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.
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Affiliation(s)
| | | | - Silvia Serino
- ATN-P Lab., Istituto Auxologico Italiano, Milan, Italy
| | - Giovanni Pioggia
- National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy
| | - Gennaro Tartarisco
- National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy
| | - Giovanni Baldus
- National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy
| | - Daniele Corda
- National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy
| | - Marcello Ferro
- "Antonio Zampolli" Institute for Computational Linguistics (ILC), Italy
| | - Nicola Carbonaro
- Research Center "E.Piaggio", University of Pisa, Via Diotisalvi 2, Pisa, Italy
| | - Alessandro Tognetti
- Research Center "E.Piaggio", University of Pisa, Via Diotisalvi 2, Pisa, Italy
| | - Danilo De Rossi
- Information Engineering Department, University of Pisa, Via Caruso 2, Pisa, Italy
| | - Dimitris Giakoumis
- Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thermi, Thessaloniki, Greece
| | - Dimitrios Tzovaras
- Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thermi, Thessaloniki, Greece
| | | | - Giuseppe Riva
- ATN-P Lab., Istituto Auxologico Italiano, Milan, Italy
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Giakoumis D, Drosou A, Cipresso P, Tzovaras D, Hassapis G, Gaggioli A, Riva G. Using activity-related behavioural features towards more effective automatic stress detection. PLoS One 2012; 7:e43571. [PMID: 23028461 PMCID: PMC3446965 DOI: 10.1371/journal.pone.0043571] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 07/26/2012] [Indexed: 11/19/2022] Open
Abstract
This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.
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Affiliation(s)
- Dimitris Giakoumis
- Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece.
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Giakoumis D, Drosou A, Cipresso P, Tzovaras D, Hassapis G, Gaggioli A, Riva G. Real-time monitoring of behavioural parameters related to psychological stress. Stud Health Technol Inform 2012; 181:287-291. [PMID: 22954873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We have developed a system, allowing real-time monitoring of human gestures, which can be used for the automatic recognition of behavioural correlates of psychological stress. The system is based on a low-cost camera (Microsoft Kinect), which provides video recordings capturing the subject's upper body activity. Motion History Images (MHIs) are calculated in real-time from these recordings. Appropriate algorithms are thereafter applied over the MHIs, enabling the real-time calculation of activity-related behavioural parameters. The system's efficiency in real-time calculation of behavioural parameters has been tested in a pilot trial, involving monitoring of behavioural parameters during the induction of mental stress. Results showed that our prototype is capable to effectively calculate simultaneously eight different behavioural parameters in real-time. Statistical analysis indicated significant correlations between five of these parameters and self-reported stress. The preliminary findings suggest that our approach could potentially prove useful within systems targeting automatic stress detection, through unobtrusive monitoring of subjects.
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Affiliation(s)
- Dimitris Giakoumis
- Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece.
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