Special Issue “Multi-Modal Sensors for Human Behavior Monitoring” on MDPI Sensors Journal

CFPs. Special Issue “Multi-Modal Sensors for Human Behavior Monitoring”

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section “Physical Sensors”.

This special issue is now open for submission
Deadline for manuscript submissions: 15 July 2019


Dear Colleagues,
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.
The purpose of this special issue is to gather the latest research 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.
Given the need for data in this field of research, scientific works that present data collections are also welcome.
Therefore, contributions to this Special Issue may include, but are not limited to:

• Novel sensing techniques for the non-invasive measurement of physiological signals
• Internet-of-Things based architecture for multimodal monitoring of human behaviour
• Learning and inference from multimodal sensory data
• Real-time multimodal activity recognition
• Semantic interpretation of multimodal sensory data
• Multimodal sensors fusion techniques
• Multimodal databases and benchmarks for behavior monitoring and understanding.

Prof. Giuseppe Boccignone
Dr. Paolo Napoletano
Prof. Raimondo Schettini

Guest Editors