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5. Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

Author

Cipresso, P, Serino, S, Borghesi, F, Tartarisco, G, Riva, G, Pioggia, G, Gaggioli, A, Cipresso P., Serino S., Borghesi F., Tartarisco G., Riva G., Pioggia G., Gaggioli A., Cipresso, P, Serino, S, Borghesi, F, Tartarisco, G, Riva, G, Pioggia, G, Gaggioli, A, Cipresso P., Serino S., Borghesi F., Tartarisco G., Riva G., Pioggia G., and Gaggioli A.

Abstract

Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system's architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system's knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.

Published
2021

6. Mediating Mindfulness-Based Interventions with Virtual Reality in Non-Clinical Populations: The State-of-the-Art

Author

Failla, C., Marino, F., Bernardelli, L., Gaggioli, Andrea, Doria, G., Chila, P., Minutoli, R., Mangano, R., Torrisi, R., Tartarisco, G., Bruschetta, R., Arcuri, F., Cerasa, A., Pioggia, G., Gaggioli A. (ORCID:0000-0001-7818-7598), Failla, C., Marino, F., Bernardelli, L., Gaggioli, Andrea, Doria, G., Chila, P., Minutoli, R., Mangano, R., Torrisi, R., Tartarisco, G., Bruschetta, R., Arcuri, F., Cerasa, A., Pioggia, G., and Gaggioli A. (ORCID:0000-0001-7818-7598)

Abstract

Mindfulness is one of the most popular psychotherapeutic techniques that help to promote good mental and physical health. Combining mindfulness with immersive virtual reality (VR) has been proven to be especially effective for a wide range of mood disorders for which traditional mindfulness has proven valuable. However, the vast majority of immersive VR-enhanced mindfulness applications have focused on clinical settings, with little evidence on healthy subjects. This narrative review evaluates the real effectiveness of state-of-the-art mindfulness interventions mediated by VR systems in influencing mood and physiological status in non-clinical populations. Only studies with an RCT study design were considered. We conclude that most studies were characterized by one single meditation experience, which seemed sufficient to induce a significant reduction in negative mood states (anxiety, anger, depression, tension) combined with increased mindfulness skills. However, physiological correlates of mindfulness practices have scarcely been investigated. The application of VR-enhanced mindfulness-based interventions in non-clinical populations is in its infancy since most studies have several limitations, such as the poor employment of the RCT study design, the lack of physiological measurements (i.e., heart rate variability), as well as the high heterogeneity in demographical data, technological devices, and VR procedures. We thus concluded that before applying mindfulness interventions mediated by VR in clinical populations, more robust and reliable methodological procedures need to be defined.

Published
2022

11. Manuale di Neuropsichiatria Infantile e dell'adolescenza - Terapie basate sull'uso di computer e tecnologie mobili

Author

Pennisi P., Tartarisco G., Bernava G., and Pioggia G.

Subjects
processo diagnostico, tecnologie mobili, intervento precoce
Abstract

Il manuale ha l'obiettivo di dotare la comunità neuropsichiatrica infantile italiana di uno strumento formativo, completo e aggiornato, redatto in lingua italiana. Il progetto è ispirato ad alcuni manuali statunitensi completi e moderni sia dal punto di vista editoriale, sia in quanto associati ad un sito-web per rendere disponibile al lettore materiale supplementare come ad esempio video di casi clinici e questionari utili all'esercizio della professione

Published
2018

12. Deliverable 3.2 - Unobtrusive solution for the monitoring and the assessment of physical status in adults with CHF

Author

Slapnicar G., Lustrek M., Cvetkovic B., Tartarisco G., and Tiihonen A.

Subjects
wearable devices, machine learning, decision support system, heart rate variability, galvanic skin response, physical assesment
Abstract

This deliverable begins with a description of the monitoring devices used in the HeartMan system. The main device is the HeartMan wristband, which measures the heart rate, heart rate variability, photoplethysmogram, galvanic skin response, temperature and acceleration. It can connect to the mobile phone via Bluetooth low energy. Additional devices are the Ruuvi ambient sensor, which monitors the temperature, humidity and light at home, and also connects to the mobile phone via Bluetooth low energy; a blood pressure monitor, scales and pill dispenser. The acceleration and heart rate from the wristband are used to recognise the patients' activity and estimate their energy expenditure. To do this, the sensor data are split into windows, a number of features (such as averages, standard deviations and angles) are computed from each window, and finally these features are fed into a model built with a machine-learning algorithm to recognize the activity or estimate the energy expenditure. The HeartMan method can recognize ten activities with the accuracy of 72 %. The error of the energy expenditure estimation is 0.58 metabolic equivalents of task, which is better than dedicated consumer devices. The photoplethysmogram is used to estimate the blood pressure. This is a challenging task that requires complex processing. The data are first cleaned, and cycles (corresponding to heartbeats) are extracted. A number of features are extracted from each cycle, which - together with the raw signal - are fed into a deep neural network to estimate the blood pressure. Some data belonging to each patient is required for personalisation, to achieve adequate accuracy. The error on hospital data is below 8 mmHg for systolic blood pressure, and below 4 mmHg for diastolic blood pressure. The errors on real-life data, where traditional regression was used instead of deep learning because of lack of data, are around 12 and 6 mmHg.

Published
2018

13. Deliverable 3.3 - Unobtrusive solution for the monitoring and the assessment of psychological status in adults with CHF

Author

Tartarisco G., Marino F., Busà L., Nucera S., and Pioggia G.

Subjects
machine learning, decision support system, wearable sensors, psychological assessment
Abstract

The following deliverable presents the multiple physiological data sources such as HR, HRV, GSR, BR, Speech, combined with psychological questionnaires collected by the HeartMan application during a semi-structured interview between patient and relative caregiver. All these features are sent through a cloud infrastructure to the psychological support module which processes the information with a machine learning model and recognizes the psychological health status of the patient (motivated, anxious, depressed). This module represents one of the main components of psychological DSS (deliverable D4.3). In this document, we also present preliminary results obtained with a mobile platform that captures physiological and vocal responses respectively extracted from the Zephyr chest belt and the smartphone. The challenge is to combine all these parameters for automatic detection of psychological comorbidities while providing a personalized intervention based on cognitive behavioural strategies and mindfulness exercises. Even if the sample is still limited and further optimizations will be obtained at the end of the trial, the achieved mean accuracy of models generalized with the leave one subject out approach is about 88.6%. This is an encouraging achievement, since it suggests that the explored features are good candidates for psychological assessment. During next months, we will integrate further parameters such as galvanic skin response, temperature and breath rate presented in section 2.3 and 2.4, that will be collected during the trial with the final release of HeartMan wristband aiming to increase the accuracy of psychological assessment.

Published
2018

14. Deliverable 4.3 - Psychological DSS for CHF

Author

Marino F., Nucera S., Bernava M., Pioggia G., and Tartarisco G.

Subjects
mindfulness, psychological decision support system, cognitive behavioural therapy
Abstract

The deliverable presents the integration of medical advices provided by the DSS with structured psychological interventions based on three main components: cognitive dissonance, mindfulness exercises and cognitive behavioural advices adapted to the psychological profile of the patient (Task 4.4). All activities of T4.4 were carried out successfully. We reviewed with specialized psychologists the best CBT to address psychological comorbidities with DSS, such as anxiety and depression related to chronic disease. The first component was based on CBT strategies integrated with medical DSS to improve the behavioral repertoire related to physical exercise and to follow a correct diet. The cognitive dissonance solver was the second component implemented applying the refurbishment of ineffective thinking (cognitive dissonance) based on the assessment of thoughts, beliefs and attitudes, through dedicated questionnaires to improve adherence of medical advices. The third component was based on management of exercises of relaxation and mindfulness based on games, mindful messages and experiential audio files to make the patients more aware of the present moment and to help them to see their illness in a new light, without allowing fear to consume them and drive unhealthy behaviors. In this context, we also designed how these contents have to interact with the medical DSS and the mobile application interface with the user. The final implementation and integration of the psychological components with medical DSS will be carried out in WP5. When the psychological interventions become operational, we expect to get additional data of better quality, which we will use to further improve the psychological model and possibly develop and integrate new decision support rules.

Published
2018

15. Neuro-Fuzzy Physiological Computing to Assess Stress Levels in Virtual Reality Therapy

Author

Tartarisco, G., Carbonaro, N., Tonacci, A., Bernava, G. M., Arnao, A., Crifaci, G., Cipresso, P., Riva, G., Gaggioli, A., De Rossi, D., Tognetti, A., and Pioggia, G.

Subjects
Artificial intelligence, Empirical studies in HCI, Stress recognition, Neuro-fuzzy, Settore M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA, business.industry, Computer science, Physiological computing, Settore M-PSI/03 - PSICOMETRIA, Virtual reality, Virtual reality therapy, Stress level, Human-Computer Interaction, stress, Human computer interaction, Physiological computingt, psychophysiology, Settore M-PSI/01 - PSICOLOGIA GENERALE, business, Software
Abstract

This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizingmap and a fuzzy-rule-basedmodule, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neurofuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate.

Published
2015
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16. Deliverable 3.1 - A Report on HeartMan Monitoring devices

Author

Tartarisco G., Bernava G., Tiihonen A., Arnao A., and Pioggia G.

Subjects
wearable sensors, medical devices
Abstract

This report tightly relates to T3.1 Health devices for multi-parametric monitoring in which the aim is to present the state-of-the-art of the devices suitable for the HeartMan platform and their characteristics in order to monitor the physiological and psychological status of Congestive Heart Failure (CHF) patients. The activities carried out in task 3.1 deal with the research and description of low-cost off-the-shelf health monitoring solutions available on the market, medical sensors and research products selected according to medical and user requirements provided by WP2.

Published
2017

17. A decision support system for real-time stress detection during virtual reality exposure

Author

Westwood, JD, Westwood, SW, Felländer-Tsai, L, Fidopiastis, CM, Haluck, RS, Robb, RA, Senger, S, Vosburgh, KG, 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, 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, Westwood, JD, Westwood, SW, Felländer-Tsai, L, Fidopiastis, CM, Haluck, RS, Robb, RA, Senger, S, Vosburgh, KG, 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, 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, and RIVA G

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.

Published
2014

18. Computerized experience-sampling approach for realtime assessment of stress

Author

Serino, S, Cipresso, P, Tartarisco, G, Baldus, G, Corda, D, Pioggia, G, Gaggioli, A, Riva, G, Serino, Silvia, Cipresso, Pietro, Tartarisco, G., Baldus, G., Corda, D., Pioggia, G., Gaggioli, Andrea, Riva, Giuseppe, Serino, S, Cipresso, P, Tartarisco, G, Baldus, G, Corda, D, Pioggia, G, Gaggioli, A, Riva, G, Serino, Silvia, Cipresso, Pietro, Tartarisco, G., Baldus, G., Corda, D., Pioggia, G., Gaggioli, Andrea, and Riva, Giuseppe

Abstract

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health. Computerized experience-sampling method appears to be a promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://psychlog.com) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

Published
2013

19. Neuro-Fuzzy Physiological Computing to Assess Stress Levels in Virtual Reality Therapy

Author

Tartarisco, G, Carbonaro, N, Tonacci, A, Bernava, G, Arnao, A, Crifaci, G, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea, De Rossi, D, Tognetti, A, Pioggia, G., Cipresso, Pietro (ORCID:0000-0002-0662-7678), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Tartarisco, G, Carbonaro, N, Tonacci, A, Bernava, G, Arnao, A, Crifaci, G, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea, De Rossi, D, Tognetti, A, Pioggia, G., Cipresso, Pietro (ORCID:0000-0002-0662-7678), Riva, Giuseppe (ORCID:0000-0003-3657-106X), and Gaggioli, Andrea (ORCID:0000-0001-7818-7598)

Abstract

This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizing map and a fuzzy-rule-based module, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neuro-fuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate. RESEARCH HIGHLIGHTS Stress levels were predicted on the basis of physiological computing using a neuro-fuzzy model during virtual reality therapy. Features were extracted from ECG and respiration obtaining high accuracy and optimization of computational costs. The neuro-fuzzy model shows better performance than the more frequently adopted classifiers. This approach may enhance the use of physiological computing for stress treatment in clinical practice

Published
2015

20. Computerized experience-sampling approach for real-time assessment of stress

Author

Serino S, Cipresso P, Tartarisco G, Baldus G, Corda D, Pioggia G, Gaggioli A, and Riva G

Subjects
pervasive computing, experience-sampling method, heart rate variability, psychophysiology, psychological stress
Abstract

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health. Computerized experience-sampling method appears to be a promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://psychlog.com) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

Published
2012

21. An open source mobile platform for psychophysiological self tracking

Author

Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, and Riva, Giuseppe

Subjects
Electrocardiography, Mental Health, Monitoring, Settore M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA, Remote Sensing Technology, Ambulatory, Telecommunications, Monitoring, Ambulatory, Settore M-PSI/01 - PSICOLOGIA GENERALE, Pilot Projects
Published
2012

22. Experiential virtual scenarios with real-time monitoring (interreality) for the management of psychological stress: A block randomized controlled trial

Author

Gaggioli, A, Pallavicini, F, Morganti, L, Serino, S, Scaratti, C, Briguglio, M, Crifaci, G, Vetrano, N, Giulintano, A, Bernava, G, Tartarisco, G, Pioggia, G, Raspelli, S, Cipresso, P, Vigna, C, Grassi, A, Baruffi, M, Wiederhold, B, Riva, G, Riva, G., PALLAVICINI, FEDERICA, Gaggioli, A, Pallavicini, F, Morganti, L, Serino, S, Scaratti, C, Briguglio, M, Crifaci, G, Vetrano, N, Giulintano, A, Bernava, G, Tartarisco, G, Pioggia, G, Raspelli, S, Cipresso, P, Vigna, C, Grassi, A, Baruffi, M, Wiederhold, B, Riva, G, Riva, G., and PALLAVICINI, FEDERICA

Abstract

The recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. Objective: Our goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). Methods: The study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations-teachers and nurses-that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. Results: Although both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic t

Published
2014

23. Experiential virtual scenarios with real-time monitoring (interreality) for the management of psychological stress: A block randomized controlled trial

Author

Gaggioli, Andrea, Morganti, Luca, Serino, Silvia, Scaratti, Chiara, Briguglio, M, Crifaci, G, Vetrano, N, Giulintano, A, Bernava, G, Tartarisco, G, Pioggia, G, Raspelli, S, Cipresso, Pietro, Vigna, Cinzia, Grassi, Alessandra, Baruffi, M, Wiederhold, Brenda Kay, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Serino, Silvia (ORCID:0000-0002-8422-1358), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea, Morganti, Luca, Serino, Silvia, Scaratti, Chiara, Briguglio, M, Crifaci, G, Vetrano, N, Giulintano, A, Bernava, G, Tartarisco, G, Pioggia, G, Raspelli, S, Cipresso, Pietro, Vigna, Cinzia, Grassi, Alessandra, Baruffi, M, Wiederhold, Brenda Kay, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Serino, Silvia (ORCID:0000-0002-8422-1358), Cipresso, Pietro (ORCID:0000-0002-0662-7678), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

The recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. Objective: Our goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). Methods: The study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations-teachers and nurses-that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. Results: Although both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic t

Published
2014

24. A decision support system for real-time stress detection during virtual reality exposure

Author

Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Ferro, M, Carbonaro, N, Tognetti, A, De Rossi, D, Giakoumis, D, Tzovaras, D, Riera, A, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Ferro, M, Carbonaro, N, Tognetti, A, De Rossi, D, Giakoumis, D, Tzovaras, D, Riera, A, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

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.

Published
2014

27. Computerized experience-sampling approach for realtime assessment of stress

Author

Serino, Silvia, Cipresso, Pietro, Tartarisco, G., Baldus, G., Corda, D., Pioggia, G., Gaggioli, Andrea, Riva, Giuseppe, Serino, Silvia (ORCID:0000-0002-8422-1358), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Serino, Silvia, Cipresso, Pietro, Tartarisco, G., Baldus, G., Corda, D., Pioggia, G., Gaggioli, Andrea, Riva, Giuseppe, Serino, Silvia (ORCID:0000-0002-8422-1358), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Gaggioli, Andrea (ORCID:0000-0001-7818-7598), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health. Computerized experience-sampling method appears to be a promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://psychlog.com) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

Published
2013

28. An open source mobile platform for psychophysiological self tracking

Author

Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

Published
2012

29. An open source mobile platform for psychophysiological self tracking

Author

Westwood, J.D., Haluck, R.S., Robb, R.A., Vosburgh, K.G., Westwood, S.W., Senger, S., Felländer-Tsai, L., Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Westwood, J.D., Haluck, R.S., Robb, R.A., Vosburgh, K.G., Westwood, S.W., Senger, S., Felländer-Tsai, L., Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

Published
2012

30. A system for automatic detection of momentary stress in naturalistic settings

Author

Gaggioli, A, Pioggia, G, Tartarisco, G, Baldus, G, Ferro, M, Cipresso, P, Serino, S, Popleteev, A, Gabrielli, S, Maimone, R, Riva, G, Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, Riva, Giuseppe, Gaggioli, A, Pioggia, G, Tartarisco, G, Baldus, G, Ferro, M, Cipresso, P, Serino, S, Popleteev, A, Gabrielli, S, Maimone, R, Riva, G, Gaggioli, Andrea, Cipresso, Pietro, Serino, Silvia, and Riva, Giuseppe

Abstract

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system’s architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.

Published
2012

31. An event-driven psychophysiological assessment for health care

Author

Serino, S, Cipresso, P, Tartarisco, G, Baldus, G, Corda, D, Pioggia, G, Gaggioli, A, Riva, G, Serino, Silvia, Cipresso, Pietro, Tartarisco, Gennaro, Baldus, Giovanni, Corda, Daniele, Pioggia, Giovanni, Gaggioli, Andrea, Riva, Giuseppe, Serino, S, Cipresso, P, Tartarisco, G, Baldus, G, Corda, D, Pioggia, G, Gaggioli, A, Riva, G, Serino, Silvia, Cipresso, Pietro, Tartarisco, Gennaro, Baldus, Giovanni, Corda, Daniele, Pioggia, Giovanni, Gaggioli, Andrea, and Riva, Giuseppe

Abstract

Computerized experience-sampling method comprising a mobile-based system that collects psychophysiological data appears to be a very promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://sourceforge.net/projects/psychlog/) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Our innovative approach offers to researchers and clinicians new effective opportunities to assess and treat psychological stress in daily-life environments.

Published
2012

32. Personal Health System architecture for stress monitoring and support to clinical decisions

Author

Tartarisco, G, Baldus, G, Corda, D, Raso, R, Arnao, A, Ferro, M, Gaggioli, Andrea, Pioggia, G., Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Tartarisco, G, Baldus, G, Corda, D, Raso, R, Arnao, A, Ferro, M, Gaggioli, Andrea, Pioggia, G., and Gaggioli, Andrea (ORCID:0000-0001-7818-7598)

Abstract

Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.

Published
2012

33. A System for Automatic Detection of Momentary Stress in Naturalistic Settings

Author

Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Baldus, G, Ferro, M, Cipresso, Pietro, Serino, Silvia, Popleteev, A, Gabrielli, S, Maimone, R, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Baldus, G, Ferro, M, Cipresso, Pietro, Serino, Silvia, Popleteev, A, Gabrielli, S, Maimone, R, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Serino, Silvia (ORCID:0000-0002-8422-1358), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.

Published
2012

34. PsychLog: A Personal Data Collection Platform for Psychophysiological Research

Author

Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

In this paper we introduce PsychLog (http://www.psychlog.com/), a mobile experience sampling platform that allows real-time collection of psychological, behavioral and contextual data for research and clinical applications. The mobile platform allows administering self-report questionnaires to collect user's quality of experience in its various cognitive, affective and motivational dimensions. The researcher can schedule the administration of the questionnaires by setting a trigger, which can be launched at specific times or randomly during a day. A wireless electrocardiogram (ECG) equipped with an accelerometer allows monitoring levels of activity and heart rate information. PsychLog is freely available for Windows mobile and its open-source code can be configured to meet specific experimental or clinical requirements. Here, we provide an overview of the system and its future developments.

Published
2011

35. A mobile data collection platform for mental health research

Author

Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), Riva, Giuseppe (ORCID:0000-0003-3657-106X), Gaggioli, Andrea, Pioggia, G, Tartarisco, G, Baldus, G, Corda, D, Cipresso, Pietro, Riva, Giuseppe, Gaggioli, Andrea (ORCID:0000-0001-7818-7598), Cipresso, Pietro (ORCID:0000-0002-0662-7678), and Riva, Giuseppe (ORCID:0000-0003-3657-106X)

Abstract

Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user’s experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source

Published
2011

40. Poster Session 4: Friday 9 December 2011, 14:00-18:00 * Location: Poster Area

Author

Wang, M., primary, Yan, G., additional, Yue, W., additional, Siu, C., additional, Tse, H., additional, Perperidis, A., additional, Cusack, D., additional, White, A., additional, Macgillivray, T., additional, Mcdicken, W., additional, Anderson, T., additional, Ryabov, V., additional, Shurupov, V., additional, Suslova, T., additional, Markov, V., additional, Elmstedt, N., additional, Ferm Widlund, K., additional, Lind, B., additional, Brodin, L.-A., additional, Westgren, M., additional, Mantovani, F., additional, Barbieri, A., additional, Bursi, F., additional, Valenti, C., additional, Quaglia, M., additional, Modena, M., additional, Peluso, D., additional, Muraru, D., additional, Dal Bianco, L., additional, Beraldo, M., additional, Solda', E., additional, Tuveri, M., additional, Cucchini, U., additional, Al Mamary, A., additional, Badano, L., additional, Iliceto, S., additional, Goncalves, A., additional, Almeria, C., additional, Marcos-Alberca, P., additional, Feltes, G., additional, Hernandez-Antolin, R., additional, Rodriguez, H., additional, Maroto, L., additional, Silva Cardoso, J., additional, Macaya, C., additional, Zamorano, J., additional, Squarciotta, S., additional, Innocenti, F., additional, Guzzo, A., additional, Bianchi, S., additional, Lazzeretti, D., additional, De Villa, E., additional, Vicidomini, S., additional, Del Taglia, B., additional, Donnini, C., additional, Pini, R., additional, Mennie, C., additional, Salmasi, A. M., additional, Kutyifa, V., additional, Nagy, V., additional, Edes, E., additional, Apor, A., additional, Merkely, B., additional, Nyrnes, S., additional, Lovstakken, L., additional, Torp, H., additional, Haugen, B., additional, Said, K., additional, Shehata, A., additional, Ashour, Z., additional, El-Tobgy, S., additional, Cameli, M., additional, Bigio, E., additional, Lisi, M., additional, Righini, F., additional, Franchi, F., additional, Scolletta, S., additional, Mondillo, S., additional, Gayat, E., additional, Weinert, L., additional, Yodwut, C., additional, Mor-Avi, V., additional, Lang, R., additional, Hrynchyshyn, N., additional, Kachenoura, N., additional, Diebold, B., additional, Khedim, R., additional, Senesi, M., additional, Redheuil, A., additional, Mousseaux, E., additional, Perdrix, L., additional, Yurdakul, S., additional, Erdemir, V., additional, Tayyareci, Y., additional, Memic, K., additional, Yildirimturk, O., additional, Aytekin, V., additional, Gurel, M., additional, Aytekin, S., additional, Gargani, L., additional, Fernandez Cimadevilla, C., additional, La Falce, S., additional, Landi, P., additional, Picano, E., additional, Sicari, R., additional, Smedsrud, M. K., additional, Gravning, J., additional, Eek, C., additional, Morkrid, L., additional, Skulstad, H., additional, Aaberge, L., additional, Bendz, B., additional, Kjekshus, J., additional, Edvardsen, T., additional, Bajraktari, G., additional, Hyseni, V., additional, Morina, B., additional, Batalli, A., additional, Tafarshiku, R., additional, Olloni, R., additional, Henein, M., additional, Mjolstad, O., additional, Snare, S., additional, Folkvord, L., additional, Helland, F., additional, Haraldseth, O., additional, Grimsmo, A., additional, Berry, M., additional, Zaghden, O., additional, Nahum, J., additional, Macron, L., additional, Lairez, O., additional, Damy, T., additional, Bensaid, A., additional, Dubois Rande, J., additional, Gueret, P., additional, Lim, P., additional, Nciri, N., additional, Issaoui, Z., additional, Tlili, C., additional, Wanes, I., additional, Foudhil, H., additional, Dachraoui, F., additional, Grapsa, J., additional, Dawson, D., additional, Nihoyannopoulos, P., additional, Gianturco, L., additional, Turiel, M., additional, Atzeni, F., additional, Sarzi-Puttini, P., additional, Stella, D., additional, Donato, L., additional, Tomasoni, L., additional, Jung, P., additional, Mueller, M., additional, Huber, T., additional, Sevilmis, G., additional, Kroetz, F., additional, Sohn, H., additional, Panoulas, V., additional, Bratsas, A., additional, Raso, R., additional, Tartarisco, G., additional, Pioggia, G., additional, Gargiulo, P., additional, Petretta, M., additional, Cuocolo, A., additional, Prastaro, M., additional, D'amore, C., additional, Vassallo, E., additional, Savarese, G., additional, Marciano, C., additional, Paolillo, S., additional, Perrone Filardi, P., additional, Aggeli, C., additional, Felekos, I., additional, Roussakis, G., additional, Poulidakis, E., additional, Pietri, P., additional, Toutouzas, K., additional, Stefanadis, C., additional, Kaladaridis, A., additional, Skaltsiotis, I., additional, Kottis, G., additional, Bramos, D., additional, Takos, D., additional, Matthaios, I., additional, Agrios, I., additional, Papadopoulou, E., additional, Moulopoulos, S., additional, Toumanidis, S., additional, Carrilho-Ferreira, P., additional, Cortez-Dias, N., additional, Jorge, C., additional, Silva, D., additional, Silva Marques, J., additional, Placido, R., additional, Santos, L., additional, Ribeiro, S., additional, Fiuza, M., additional, Pinto, F., additional, Stoickov, V., additional, Ilic, S., additional, Deljanin Ilic, M., additional, Kim, W., additional, Woo, J., additional, Bae, J., additional, Kim, K., additional, Descalzo, M., additional, Rodriguez, J., additional, Moral, S., additional, Otaegui, I., additional, Mahia, P., additional, Garcia Del Blanco, L., additional, Gonzalez Alujas, T., additional, Figueras, J., additional, Evangelista, A., additional, Garcia-Dorado, D., additional, Takeuchi, M., additional, Kaku, K., additional, Otani, K., additional, Iwataki, M., additional, Kuwaki, H., additional, Haruki, N., additional, Yoshitani, H., additional, Otsuji, Y., additional, Kukucka, M., additional, Pasic, M., additional, Unbehaun, A., additional, Dreysse, S., additional, Mladenow, A., additional, Kuppe, H., additional, Hetzer, R., additional, Rajamannan, N., additional, Tanrikulu, A., additional, Kristiansson, L., additional, Gustafsson, S., additional, Lindmark, K., additional, Henein, M. Y., additional, Evdoridis, C., additional, Stougiannos, P., additional, Thomopoulos, M., additional, Fosteris, M., additional, Spanos, P., additional, Sionis, G., additional, Giatsios, D., additional, Paschalis, A., additional, Sakellaris, C., additional, Trikas, A., additional, Yong, Z. Y., additional, Boerlage-Van Dijk, K., additional, Koch, K., additional, Vis, M., additional, Bouma, B., additional, Piek, J., additional, Baan, J., additional, Abid, L., additional, Frikha, Z., additional, Makni, K., additional, Maazoun, N., additional, Abid, D., additional, Hentati, M., additional, Kammoun, S., additional, Barbier, P., additional, Staron, A., additional, Cefalu', C., additional, Berna, G., additional, Gripari, P., additional, Andreini, D., additional, Pontone, G., additional, Pepi, M., additional, Ring, L., additional, Rana, B., additional, Ho, S., additional, Wells, F., additional, Dogan, A., additional, Karaca, O., additional, Guler, G., additional, Guler, E., additional, Gunes, H., additional, Alizade, E., additional, Agus, H., additional, Gol, G., additional, Esen, O., additional, Esen, A., additional, Turkmen, M., additional, Agricola, E., additional, Ingallina, G., additional, Ancona, M., additional, Maggio, S., additional, Slavich, M., additional, Tufaro, V., additional, Oppizzi, M., additional, Margonato, A., additional, Orsborne, C., additional, Irwin, B., additional, Pearce, K., additional, Ray, S., additional, Garcia Alonso, C., additional, Vallejo, N., additional, Labata, C., additional, Lopez Ayerbe, J., additional, Teis, A., additional, Ferrer, E., additional, Nunez Aragon, R., additional, Gual, F., additional, Pedro Botet, M., additional, Bayes Genis, A., additional, Santos, C. M., additional, Carvalho, M., additional, Andrade, M., additional, Dores, H., additional, Madeira, S., additional, Cardoso, G., additional, Ventosa, A., additional, Aguiar, C., additional, Ribeiras, R., additional, Mendes, M., additional, Petrovic, M., additional, Milasinovic, G., additional, Vujisic-Tesic, B., additional, Nedeljkovic, I., additional, Zamaklar-Trifunovic, D., additional, Petrovic, I., additional, Draganic, G., additional, Banovic, M., additional, Boricic, M., additional, Villarraga, H., additional, Molini-Griggs Bs, C., additional, Silen-Rivera Bs, P., additional, Payne Mph Ms, B., additional, Koshino Md Phd, Y., additional, Hsiao Md, J., additional, Monivas Palomero, V., additional, Mingo Santos, S., additional, Mitroi, C., additional, Garcia Lunar, I., additional, Garcia Pavia, P., additional, Castro Urda, V., additional, Toquero, J., additional, Gonzalez Mirelis, J., additional, Cavero Gibanel, M., additional, Fernandez Lozano, I., additional, Oko-Sarnowska, Z., additional, Wachowiak-Baszynska, H., additional, Katarzynska-Szymanska, A., additional, Trojnarska, O., additional, Grajek, S., additional, Bellavia, D., additional, Pellikka, P., additional, Dispenzieri, A., additional, Oh, J. K., additional, Polizzi, V., additional, Pitrolo, F., additional, Musumeci, F., additional, Miller, F., additional, Ancona, R., additional, Comenale Pinto, S., additional, Caso, P., additional, Severino, S., additional, Cavallaro, C., additional, Vecchione, F., additional, D'onofrio, A., additional, Calabro', R., additional, Maceira Gonzalez, A. M., additional, Ripoll, C., additional, Cosin-Sales, J., additional, Igual, B., additional, Salazar, J., additional, Belloch, V., additional, Cosin-Aguilar, J., additional, Pinamonti, B., additional, Iorio, A., additional, Bobbo, M., additional, Merlo, M., additional, Barbati, G., additional, Massa, L., additional, Faganello, G., additional, Di Lenarda, A., additional, Sinagra, G. F., additional, Ishizu, T., additional, Seo, Y., additional, Enomoto, M., additional, Kameda, Y., additional, Ishibashi, N., additional, Inoue, M., additional, Aonuma, K., additional, Saleh, A., additional, Matsumori, A., additional, Negm, H., additional, Fouad, H., additional, Onsy, A., additional, Hamodraka, E., additional, Paraskevaidis, I., additional, Kallistratos, M., additional, Lezos, V., additional, Zamfir, T., additional, Manetos, C., additional, Mavropoulos, D., additional, Poulimenos, L., additional, Kremastinos, D., additional, Manolis, A., additional, Citro, R., additional, Rigo, F., additional, Ciampi, Q., additional, Patella, M., additional, Provenza, G., additional, Zito, C., additional, Tagliamonte, E., additional, Rotondi, F., additional, Silvestri, F., additional, Bossone, E., additional, Beltran Correas, P., additional, Gutierrez Landaluce, C., additional, Gomez Bueno, M., additional, Segovia Cubero, J., additional, Beladan, C., additional, Matei, F., additional, Popescu, B., additional, Calin, A., additional, Rosca, M., additional, Boanta, A., additional, Enache, R., additional, Savu, O., additional, Usurelu, C., additional, Ginghina, C., additional, Ciobanu, A. O., additional, Dulgheru, R., additional, Magda, S., additional, Dragoi, R., additional, Florescu, M., additional, Vinereanu, D., additional, Robalo Martins, S., additional, Calisto, C., additional, Goncalves, S., additional, Barrigoto, I., additional, Carvalho De Sousa, J., additional, Almeida, A., additional, Nunes Diogo, A., additional, Sargento, L., additional, Satendra, M., additional, Sousa, C., additional, Lousada, N., additional, Palma Reis, R., additional, Schiano Lomoriello, V., additional, Esposito, R., additional, Santoro, A., additional, Raia, R., additional, Schiattarella, P., additional, Dores, E., additional, Galderisi, M., additional, Mansencal, N., additional, Caille, V., additional, Dupland, A., additional, Perrot, S., additional, Bouferrache, K., additional, Vieillard-Baron, A., additional, Jouffroy, R., additional, Moceri, P., additional, Liodakis, E., additional, Gatzoulis, M., additional, Li, W., additional, Dimopoulos, K., additional, Sadron, M., additional, Seguela, P. E., additional, Arnaudis, B., additional, Dulac, Y., additional, Cognet, T., additional, Acar, P., additional, Shiina, Y., additional, Uemura, H., additional, Kupczynska, K., additional, Kasprzak, J., additional, Michalski, B., additional, Lipiec, P., additional, Carvalho, V., additional, Almeida, A. M. G., additional, David, C., additional, Marques, J., additional, Ferreira, P., additional, Amaro, M., additional, Costa, P., additional, Diogo, A., additional, Tritakis, V., additional, Ikonomidis, I., additional, Lekakis, J., additional, Tzortzis, S., additional, Kadoglou, N., additional, Papadakis, I., additional, Trivilou, P., additional, Koukoulis, C., additional, Anastasiou-Nana, M., additional, Bombardini, T., additional, Gherardi, S., additional, Arpesella, G., additional, Maccherini, M., additional, Serra, W., additional, Magnani, G., additional, Del Bene, R., additional, Pasanisi, E., additional, Startari, U., additional, Panchetti, L., additional, Rossi, A., additional, Piacenti, M., additional, Morales, M., additional, El Hajjaji, I., additional, El Mahmoud, R., additional, Digne, F., additional, Dubourg, O., additional, Agoston, G., additional, Moreo, A., additional, Pratali, L., additional, Moggi Pignone, A., additional, Pavellini, A., additional, Doveri, M., additional, Musca, F., additional, Varga, A., additional, Faita, F., additional, Rimoldi, S., additional, Sartori, C., additional, Alleman, Y., additional, Salinas Salmon, C., additional, Villena, M., additional, Scherrer, U., additional, Baptista, R., additional, Serra, S., additional, Castro, G., additional, Martins, R., additional, Salvador, M., additional, Monteiro, P., additional, Silva, J., additional, Szudi, L., additional, Temesvary, A., additional, Fekete, B., additional, Kassai, I., additional, Szekely, L., additional, Abdel Moneim, S. S., additional, Martinez, M., additional, Mankad, S., additional, Bernier, M., additional, Dhoble, A., additional, Chandrasekaran, K., additional, Oh, J., additional, Mulvagh, S., additional, Hong, G. R., additional, Kim, J. Y., additional, Lee, S. C., additional, Choi, S. H., additional, Sohn, I. S., additional, Seo, H. S., additional, Choi, J. H., additional, Cho, K. I., additional, Yoon, S. J., additional, Lim, S. J., additional, Wejner-Mik, P., additional, Kusmierek, J., additional, Plachcinska, A., additional, Szuminski, R., additional, Stoebe, S., additional, Tarr, A., additional, Trache, T., additional, Hagendorff, A., additional, Jenkins, C., additional, Kuhl, H., additional, Nesser, H., additional, Marwick, T., additional, Franke, A., additional, Niel, J., additional, Sugeng, L., additional, Soderberg, S., additional, Lindqvist, P., additional, Necas, J., additional, Kovalova, S., additional, Saha, S. K., additional, Kiotsekoglou, A., additional, Toole, R., additional, Govind, S., additional, Gopal, A., additional, Amzulescu, M.-S., additional, Florian, A., additional, Bogaert, J., additional, Janssens, S., additional, Voigt, J., additional, Parisi, V., additional, Losi, M., additional, Parrella, L., additional, Contaldi, C., additional, Chiacchio, E., additional, Caputi, A., additional, Scatteia, A., additional, Buonauro, A., additional, Betocchi, S., additional, Rimbas, R., additional, Mihaila, S., additional, Caputo, M., additional, Navarri, R., additional, Innelli, P., additional, Urselli, R., additional, Capati, E., additional, Ballo, P., additional, Furiozzi, F., additional, Favilli, R., additional, Lindquist, R., additional, Miller, A., additional, Reece, C., additional, O'leary, P., additional, Cetta, F., additional, Eidem, B. 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L., additional, Mandolesi, S., additional, Fedele, F., additional, Agati, L., additional, Marceca, A., additional, Rhee, S., additional, Shin, S., additional, Kim, S., additional, Yun, K., additional, Yoo, N., additional, Kim, N., additional, Oh, S., additional, Jeong, J., additional, and Alabdulkarim, N., additional

Published
2011
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47. A multi-step approach for non-invasive fetal ECG analysis.

Author

Varanini, M, Tartarisco, G, Billeci, L, Macerata, A, Pioggia, G, and Balocchi, R

Abstract

Non-invasive monitoring of fetal cardiac activity is of great clinical interest to assess fetal health. To date, however, difficulties in detecting fetal beats from abdominal mother recordings prevented the possibility of obtaining reliable results. In this study a multi-step approach for the analysis of non-invasive fetal ECG is proposed. The first steps concern the pre-processing stages of baseline removal and power line interference canceling. The successive operations are: Independent Component Analysis (ICA) for maternal ECG extraction; mother QRS detection; maternal ECG canceling using a PQRST approximation obtained by weighted Singular Value Decomposition (SVD); second ICA applied to enhance the fetal ECG signal; fetal QRS detection. The results obtained in Physionet Challenge 2013 on the test sets are expressed as two scores (HRmse and RRrmse) measuring respectively the matching between the reference annotations of fetal HR and RR time series and those estimated with the developed software. The results obtained on the learning set are: sensitivity=99.4%, positive predictive accuracy=99.2% and HRmse=1.52 bpm2, RRrmse=2.11 ms. The scores for the open test set are: HRmse=34.0 bpm2, RRrmse=5.10 ms. The scores for the hidden test (open source section) are: HRmse=187 bpm2, RRrmse=21.0 ms. [ABSTRACT FROM PUBLISHER]

Published
2013

49. Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

Author

Pietro Cipresso, Silvia Serino, Francesca Borghesi, Gennaro Tartarisco, Giuseppe Riva, Giovanni Pioggia, Andrea Gaggioli, Cipresso, P, Serino, S, Borghesi, F, Tartarisco, G, Riva, G, Pioggia, G, and Gaggioli, A

Subjects
Signal processing, Psychometrics, Mechanical Engineering, Assessment, Psychological stre, Experience sampling method, Experience sampling methods, Psychological stress, Electrical and Electronic Engineering, M-PSI/01 - PSICOLOGIA GENERALE, Instrumentation, Heart rate variability, Psychophysiology, Psychometric
Abstract

Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.

Published
2021
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50. Experiential virtual scenarios with real-time monitoring (interreality) for the management of psychological stress: A block randomized controlled trial

Author

Federica Pallavicini, Marilena Briguglio, Giuseppe Massimo Bernava, Margherita Baruffi, Silvia Serino, Alessandra Grassi, Gennaro Tartarisco, Giulia Crifaci, Simona Raspelli, Noemi Vetrano, Chiara Scaratti, Annunziata Giulintano, Luca Morganti, Giovanni Pioggia, Giuseppe Riva, Cinzia Vigna, Pietro Cipresso, Brenda K. Wiederhold, Andrea Gaggioli, Gaggioli, A, Pallavicini, F, Morganti, L, Serino, S, Scaratti, C, Briguglio, M, Crifaci, G, Vetrano, N, Giulintano, A, Bernava, G, Tartarisco, G, Pioggia, G, Raspelli, S, Cipresso, P, Vigna, C, Grassi, A, Baruffi, M, Wiederhold, B, and Riva, G

Subjects
Male, Stress management, Visual Analog Scale, medicine.medical_treatment, Nurses, Relaxation training, Health informatics, Virtual reality, law.invention, 0302 clinical medicine, Randomized controlled trial, law, Biofeedback training, Adaptation, Psychological, 030212 general & internal medicine, Health Informatic, lcsh:Public aspects of medicine, Medicine (all), Interreality, Physiological monitoring, Psychological stre, Faculty, 3. Good health, Cognitive behavioral therapy, Cognitive Therapy, lcsh:R858-859.7, Anxiety, Female, Smartphone, medicine.symptom, Psychology, Clinical psychology, Human, Adult, Heart rate, Health Informatics, lcsh:Computer applications to medicine. Medical informatics, Virtual Reality Exposure Therapy, 03 medical and health sciences, medicine, Settore M-PSI/01 - PSICOLOGIA GENERALE, Humans, Heart rate variability, Protocol (science), Original Paper, Analysis of Variance, Cognitive Behavioral Therapy, business.industry, Nurse, lcsh:RA1-1270, Biosensors, Psychological stress, Smartphones, Cognitive therapy, business, 030217 neurology & neurosurgery, Biosensor, Stress, Psychological
Abstract

BackgroundThe recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. ObjectiveOur goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). MethodsThe study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations—teachers and nurses—that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. ResultsAlthough both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic “trait” anxiety. A similar pattern was found for coping skills: both treatments were able to significantly increase most coping skills, but only EG participants reported a significant increase (EG=14% vs CG=0.3%) in the Emotional Support skill. ConclusionsOur findings provide initial evidence that the Interreality protocol yields better outcomes than the traditionally accepted gold standard for psychological stress treatment: CBT. Consequently, these findings constitute a sound foundation and rationale for the importance of continuing future research in technology-enhanced protocols for psychological stress management. Trial RegistrationClinicalTrials.gov: NCT01683617; http://clinicaltrials.gov/show/NCT01683617 (Archived by WebCite at http://www.webcitation.org/6QnziHv3h).

Published
2014
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