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Multi-Modal Sensors for Human Behavior Monitoring



Multi-Modal Sensors for Human Behavior Monitoring


Paolo Napoletano - Universita' degli Studi di Milano-Bicocca


In everyday life we are surrounded by various sensors, wearable and not, that explicitly or implicitly record information on our behavior either visible and hidden (e.g. physiological activity). Such sensors are of different nature: accelerometer, gyroscope, camera, electrodermal activity sensor, heart rate monitor, breath rate monitor and others. Most important, the multimodal nature of data is apt to sense and understand the many facets of human daily-life behavior from physical, voluntary activities to social signaling and lifestyle choices influenced by affect, personal traits, age and social context. The intelligent sensing community is able to exploit the data acquired with these sensors in order to develop machine-learning-based techniques, which can help in improving predictive models of human behavior. In this talk will be presented the latest research at Imaging and Vision Laboratory ( in the field of human behavior monitoring, both at the sensing and the understanding levels, by using multimodal data sources. Applications of interest can relate to domotics, healthcare, transport, education, safety aid, entertainment, sports and others.


Paolo Napoletano is assistant professor of Computer Science (tenure track - RTDB) at Department of Informatics, Systems and Communication of the University of Milano-Bicocca. In 2007, he received a Doctor of Philosophy degree (PhD) in Information Engineering from the University of Salerno (Italy) with a thesis focused on Computational Vision and Pattern Recognition. In 2003, he received a Master's degree in Telecommunications Engineering from the University of Naples Federico II, with a thesis focused on Transmission of Electromagnetic Fields. His current research interests focus on signal, image and video analysis and understanding, multimedia information processing and management and machine learning for multi-modal data classification and understanding. More information at


2020-01-28 at 3:00 pm (subject to variability)