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PEOPLE@HES-SO – Directory and Skills inventory

PEOPLE@HES-SO
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Albertetti Fabrizio

Albertetti Fabrizio

Professeur-e HES assistant

Main skills

Artificial Intelligence (AI)

Deep Learning

Natural Language Processing

Biomedical applications

  • Contact

  • Teaching

  • Conferences

Main contract

Professeur-e HES assistant

Haute Ecole Arc - Ingénierie
Espace de l'Europe 11, 2000 Neuchâtel, CH
DING
BSC HES-SO en Informatique et systèmes de communication - Haute Ecole Arc - Ingénierie
  • Modèles d'Intelligence Artificielle
  • Prétraitement des Données
  • Compilateurs
  • Gestion des Données et Cloud
  • Programmation Distribuée
MSc HES-SO en Engineering - HES-SO Master
  • Advanced Natural Language Processing

2021

Stress detection with deep learning approaches using physiological signals
Conference ArODES

Fabrizio Albertetti, Alena Simalastar, Aïcha Rizzotti

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; Proceedings of IoT Technologies for HealthCare, 7th EAI International Conference, HealthyIoT, 3 December 2020, Viana do Castelo, Portugal

Link to the conference

Summary:

The problem of stress detection and classification has attracted a lot of attention in the past decade. It has been tackled with mainly two different approaches, where signals were either collected in ambulatory settings, which can be limited to the period of presence in the hospital, or in continuous mode in the field. A sensor-based continuous measurement of stress in daily life has a potential to increase awareness of patterns of stress occurrence. In this work, we first present a data-flow infrastructure suitable for two types of studies that conforms with the data protection requirements of the ethics committee monitoring the research on humans. The detection and binary classification of stress events is compared with three different machine learning models based on the features (meta-data) extracted from physiological signals acquired in laboratory conditions and ground-truth stress level information provided by the subjects themselves via questionnaires associated with these features. The main signals considered in current classification are electro-dermal activity (EDA) and blood volume pulse (BVP) signals. Different models are compared and the best configuration yields an F1 score of 0.71 (random baseline: 0.48). The importance on prediction of phasic and tonic EDA components is also investigated. Our results also pave the way for further work on this topic with both machine learning approaches and signal processing directions.

2020

A deep learning approach for blood glucose prediction of type 1 diabetes
Conference ArODES

Jonas Freiburghaus, Aïcha Rizzotti, Fabrizio Albertetti

Proceedings of the Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), 29-30 August 2020, Santiago de Compostela, Spain

Link to the conference

Summary:

An essential part of this work is to provide a data-driven model for predicting blood glucose levels that will help to warn the person with type 1 diabetes about a potential hypo- or hyperglycemic event in an easy-to-manage and discreet way. In this work, we apply a convolutional recurrent neural network on a real dataset of 6 contributors, provided by the University of Ohio [5]. Our model is capable of predicting glucose levels with high precision with a 30- minute horizon (RMSE = 17.45 [mg/dL] and MAE = 11.22 [mg/dL]), and RMSE = 33.67 [mg/dL] and MAE = 23.25 [mg/dL] for the 60- minute horizon. We believe this precision can greatly impact the long-term health condition as well as the daily management of people with type 1 diabetes.

Achievements

Media and communication
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