@conference{A.2017c,
title = {Depression Severity Estimation from Multiple Modalities},
author = {Stepanov E. A., Lathuiliere S., Chowdhury S. A., Ghosh A., Vieriu R., Sebe N.and Riccardi G.},
editor = {AVEC Challenge},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/1711.060951.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Affective Computing, Health Analytics}
}

@article{A.2018b,
title = {Depression Severity Estimation from Multiple Modalities},
author = {Stepanov E. A., Lathuilierev S., Chowdhury S. A., Ghosh A., Vieriu R.D., Sebe N. and Riccardi G.},
editor = {IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/HealthCom18-Depression.pdf},
year = {2018},
date = {2018-01-01},
note = {EXCELLENT Paper AWARD},
keywords = {Health Analytics, Machine Learning}
}

@conference{A.2018b,
title = {HEAL: A Health Analytics Intelligent Agent Platform for the acquisition and analysis of physiological signals},
author = {Ghosh A., Stepanov E. A., Mayor Torres, J.M., Danieli M. and Riccardi G.},
editor = {IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/HealthCom18-healT.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Health Analytics}
}

@conference{R.2018,
title = {Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care},
author = {Dias R., Conboy M. H., Gabany M. J., Clarke A. L. , Osterweil J. L., Avrunin S. G., Arney D., Goldman M. J., Riccardi G., Yule J. S., Zenati A. M.},
editor = {IEEE Conf. on Cognitive and Computational Aspects of Situation Management, Boston},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/COGSIMA18ContextAwareDashboardSurgicalTeam.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Health Analytics, Interactive Systems, Machine Learning, Signal Annotation and Interpretation}
}

@proceedings{A.2017,
title = {Are You Stressed? Detecting High Stress from User Diaries},
author = {Ghosh A., Stepanov E. A., Danieli M., and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2017/09/55_PID4964285_55.pdf},
year = {2017},
date = {2017-09-11},
publisher = {8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2017) • September 11-14, 2017 • Debrecen, Hungary},
abstract = {Knowledge of the complete clinical history, lifestyle, behaviour, medication adherence data, and underlying symptoms, all affect the treatment outcomes. Collecting, analysing and using all these data, while treating a patient can often be very challenging. A doctor can spend only a limited time
with a patient. This time is often not enough to learn about all the lifestyle and underlying conditions of a patient’s life. Often patients are asked to maintain diaries of their daily activities. Diaries can help to improve adherence by increasing the consciousness of the patients, and can also serve as a way for the doctors to validate this adherence. However, diaries can be cumbersome to parse, and hence increase the task burden of the doctor. In this paper we demonstrate that automatic analysis of diaries can be used to predict the stress level of the diary writers with an F-measure of 0.70.},
keywords = {Health Analytics, Interactive Systems}
}

@proceedings{M.2016b,
title = {HEAL-T: An Efficient PPG-based Heart-Rate And IBI Estimation Method During Physical Exercise},
author = {Mayor J. M., Ghosh A., Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/EUSIPCO16-HearRateAlgorithm.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. EUSIPCO, Budapest, 2016},
abstract = {Photoplethysmography (PPG) is a simple, unobtrusive and low-cost technique for measuring blood volume pulse (BVP) used in heart-rate (HR) estimation. However, PPG based heart-rate monitoring devices are often affected by motion artifacts in on-the-go scenarios, and can yield a noisy BVP signal reporting erroneous HR values. Recent studies have proposed spectral decomposition techniques (e.g. M-FOCUSS, Joint-Sparse-Spectrum) to reduce motion artifacts and increase HR estimation accuracy, but at the cost of high computational load. The singular-value-decomposition and recursive calculations present in these approaches are not feasible for the implementation in real-time continuous-monitoring scenarios. In this paper, we propose an efficient HR estimation method based on a combination of fast-ICA, RLS and BHW filter stages that avoids sparse signal reconstruction, while maintaining a high HR estimation accuracy. The proposed method outperforms the state-of-the-art systems on the publicly available TROIKA data set both in terms of HR estimation accuracy (absolute error of 2.25 ± 1.93 bpm) and computational load.
},
keywords = {Health Analytics, Signal Annotation and Interpretation}
}

@conference{M.2016b,
title = {EEG Semantic Decoding Using Deep Neural Networks},
author = {Mayor J. M., Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/CAOS16-EEGDeepNN.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Workshop on Concepts, Actions and Objects, Rovereto, 2016.},
keywords = {Health Analytics, Signal Annotation and Interpretation}
}

@conference{M.2016b,
title = {Automatically classifying essential arterial hypertension from physiological and daily life stress responses.},
author = {Danieli M., Ghosh A., Berra E., Fulcheri C., Rabbia F., Testa E., Veglio F., Riccardi G.},
year = {2016},
date = {2016-06-10},
publisher = {ESH 2016 – The 26th European Meeting on Hypertension and Cardiovascular Protection, Paris, France, June 10 -13 2016.},
keywords = {Health Analytics}
}

@conference{M.2015,
title = {Comprendere l’Ipertensione Arteriosa Essenziale A Partire da Costrutti Psicologici e Segnali Fisiologici},
author = {Danieli M., Ghosh A, Berra E., Testa E., Rabbia F., Veglio F. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/SIIA-2015-ComprendereIpertensionePoster.pdf},
year = {2015},
date = {2015-09-24},
journal = {XXXI Congresso Societa’ Italiana Ipertensione Arteriosa, Bologna, 2015. (Poster)},
keywords = {Affective Computing, Health Analytics}
}

@conference{A2015b,
title = {Annotation and Prediction of Stress and Workload from Physiological and Inertial Signals},
author = {Ghosh A, Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/EMBC15-StressMonitoringPrediction.pdf},
year = {2015},
date = {2015-08-25},
journal = {Proc. EMBC, IEEE Conf. Engineering in Biology and Medicine Society, Milan, 2015.},
abstract = { Continuous daily stress and high workload can have negative effects on individuals’ physical and mental wellbeing. It has been shown that physiological signals may support the prediction of stress and workload. However, previous research is limited by the low diversity of signals concurring to such predictive tasks and controlled experimental design. In this paper we present 1) a pipeline for continuous and real-life acquisition of physiological and inertial signals 2) a mobile agent application for on-the-go event annotation and 3) an end-to-end signal processing and classification system for
stress and workload from diverse signal streams. We study physiological signals such as Galvanic Skin Response (GSR), Skin Temperature (ST), Inter Beat Interval (IBI) and Blood Volume Pulse (BVP) collected using a non-invasive wearable device; and inertial signals collected from accelerometer and
gyroscope sensors. We combine them with subjects’ inputs (e.g. event tagging) acquired using the agent application, and their emotion regulation scores. In our experiments we explore signal combination and selection techniques for stress and workload prediction from subjects whose signals have been recorded continuously during their daily life. The end-toend classification system is described for feature extraction, signal artifact removal, and classification. We show that a combination of physiological, inertial and user event signals provides accurate prediction of stress for real-life users and signals.},
keywords = {Health Analytics, Machine Learning, Signal Annotation and Interpretation}
}

@conference{A2015,
title = {Detection of Essential Hypertension with Physiological Signals from Wearable Devices},
author = {Ghosh A, Mayor Torres J.M., Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/EMBC15-HypertensionMonitoringPrediction.pdf},
year = {2015},
date = {2015-08-25},
journal = { Proc. EMBC, IEEE Conf. Engineering in Biology and Medicine Society, Milan, 2015.},
abstract = {Early detection of essential hypertension can support the prevention of cardiovascular disease, a leading cause of death. The traditional method of identification of hypertension involves periodic blood pressure measurement using brachial cuff-based measurement devices. While these devices are noninvasive, they require manual setup for each measurement and they are not suitable for continuous monitoring. Research has shown that physiological signals such as Heart Rate Variability,
which is a measure of the cardiac autonomic activity, is correlated with blood pressure. Wearable devices capable of measuring physiological signals such as Heart Rate, Galvanic Skin Response, Skin Temperature have recently become ubiquitous. However, these signals are not accurate and are prone to noise due to different artifacts. In this paper a) we present a data collection protocol for continuous non-invasive monitoring of physiological signals from wearable devices; b) we implement
signal processing techniques for signal estimation; c) we explore how the continuous monitoring of these physiological signals can be used to identify hypertensive patients; d) We conduct a pilot study with a group of normotensive and hypertensive patients to test our techniques. We show that physiological signals extracted from wearable devices can distinguish between these two groups with high accuracy.},
keywords = {Health Analytics, Machine Learning, Signal Annotation and Interpretation}
}